Add files using upload-large-folder tool

.venv311/Lib/site-packages/openai-2.36.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

.venv311/Lib/site-packages/openai-2.36.0.dist-info/METADATA

@@ -0,0 +1,1012 @@
+Metadata-Version: 2.3
+Name: openai
+Version: 2.36.0
+Summary: The official Python library for the openai API
+Project-URL: Homepage, https://github.com/openai/openai-python
+Project-URL: Repository, https://github.com/openai/openai-python
+Author-email: OpenAI <support@openai.com>
+License: Apache-2.0
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: MacOS
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: OS Independent
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Typing :: Typed
+Requires-Python: >=3.9
+Requires-Dist: anyio<5,>=3.5.0
+Requires-Dist: distro<2,>=1.7.0
+Requires-Dist: httpx<1,>=0.23.0
+Requires-Dist: jiter<1,>=0.10.0
+Requires-Dist: pydantic<3,>=1.9.0
+Requires-Dist: sniffio
+Requires-Dist: tqdm>4
+Requires-Dist: typing-extensions<5,>=4.11
+Requires-Dist: typing-extensions<5,>=4.14
+Provides-Extra: aiohttp
+Requires-Dist: aiohttp; extra == 'aiohttp'
+Requires-Dist: httpx-aiohttp>=0.1.9; extra == 'aiohttp'
+Provides-Extra: datalib
+Requires-Dist: numpy>=1; extra == 'datalib'
+Requires-Dist: pandas-stubs>=1.1.0.11; extra == 'datalib'
+Requires-Dist: pandas>=1.2.3; extra == 'datalib'
+Provides-Extra: realtime
+Requires-Dist: websockets<16,>=13; extra == 'realtime'
+Provides-Extra: voice-helpers
+Requires-Dist: numpy>=2.0.2; extra == 'voice-helpers'
+Requires-Dist: sounddevice>=0.5.1; extra == 'voice-helpers'
+Description-Content-Type: text/markdown
+
+# OpenAI Python API library
+
+<!-- prettier-ignore -->
+[![PyPI version](https://img.shields.io/pypi/v/openai.svg?label=pypi%20(stable))](https://pypi.org/project/openai/)
+
+The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.9+
+application. The library includes type definitions for all request params and response fields,
+and offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).
+
+It is generated from our [OpenAPI specification](https://github.com/openai/openai-openapi) with [Stainless](https://stainlessapi.com/).
+
+## Documentation
+
+The REST API documentation can be found on [platform.openai.com](https://platform.openai.com/docs/api-reference). The full API of this library can be found in [api.md](https://github.com/openai/openai-python/tree/main/api.md).
+
+## Installation
+
+```sh
+# install from PyPI
+pip install openai
+```
+
+## Usage
+
+The full API of this library can be found in [api.md](https://github.com/openai/openai-python/tree/main/api.md).
+
+The primary API for interacting with OpenAI models is the [Responses API](https://platform.openai.com/docs/api-reference/responses). You can generate text from the model with the code below.
+
+```python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+    # This is the default and can be omitted
+    api_key=os.environ.get("OPENAI_API_KEY"),
+)
+
+response = client.responses.create(
+    model="gpt-5.2",
+    instructions="You are a coding assistant that talks like a pirate.",
+    input="How do I check if a Python object is an instance of a class?",
+)
+
+print(response.output_text)
+```
+
+The previous standard (supported indefinitely) for generating text is the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). You can use that API to generate text from the model with the code below.
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+completion = client.chat.completions.create(
+    model="gpt-5.2",
+    messages=[
+        {"role": "developer", "content": "Talk like a pirate."},
+        {
+            "role": "user",
+            "content": "How do I check if a Python object is an instance of a class?",
+        },
+    ],
+)
+
+print(completion.choices[0].message.content)
+```
+
+While you can provide an `api_key` keyword argument,
+we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
+to add `OPENAI_API_KEY="My API Key"` to your `.env` file
+so that your API key is not stored in source control.
+[Get an API key here](https://platform.openai.com/settings/organization/api-keys).
+
+### Workload Identity Authentication
+
+For secure, automated environments like cloud-managed Kubernetes, Azure, and Google Cloud Platform, you can use workload identity authentication with short-lived tokens from cloud identity providers instead of long-lived API keys.
+
+#### Kubernetes (service account tokens)
+
+```python
+from openai import OpenAI
+from openai.auth import k8s_service_account_token_provider
+
+client = OpenAI(
+    workload_identity={
+        "client_id": "your-client-id",
+        "identity_provider_id": "idp-123",
+        "service_account_id": "sa-456",
+        "provider": k8s_service_account_token_provider(
+            "/var/run/secrets/kubernetes.io/serviceaccount/token"
+        ),
+    },
+    organization="org-xyz",
+    project="proj-abc",
+)
+
+response = client.chat.completions.create(
+    model="gpt-4",
+    messages=[{"role": "user", "content": "Hello!"}],
+)
+```
+
+#### Azure (managed identity)
+
+```python
+from openai import OpenAI
+from openai.auth import azure_managed_identity_token_provider
+
+client = OpenAI(
+    workload_identity={
+        "client_id": "your-client-id",
+        "identity_provider_id": "idp-123",
+        "service_account_id": "sa-456",
+        "provider": azure_managed_identity_token_provider(
+            resource="https://management.azure.com/",
+        ),
+    },
+)
+```
+
+#### Google Cloud Platform (compute engine metadata)
+
+```python
+from openai import OpenAI
+from openai.auth import gcp_id_token_provider
+
+client = OpenAI(
+    workload_identity={
+        "client_id": "your-client-id",
+        "identity_provider_id": "idp-123",
+        "service_account_id": "sa-456",
+        "provider": gcp_id_token_provider(audience="https://api.openai.com/v1"),
+    },
+)
+```
+
+#### Custom subject token provider
+
+```python
+from openai import OpenAI
+
+
+def get_custom_token() -> str:
+    return "your-jwt-token"
+
+
+client = OpenAI(
+    workload_identity={
+        "client_id": "your-client-id",
+        "identity_provider_id": "idp-123",
+        "service_account_id": "sa-456",
+        "provider": {
+            "token_type": "jwt",
+            "get_token": get_custom_token,
+        },
+    }
+)
+```
+
+You can also customize the token refresh buffer (default is 1200 seconds (20 minutes) before expiration):
+
+```python
+from openai import OpenAI
+from openai.auth import k8s_service_account_token_provider
+
+client = OpenAI(
+    workload_identity={
+        "client_id": "your-client-id",
+        "identity_provider_id": "idp-123",
+        "service_account_id": "sa-456",
+        "provider": k8s_service_account_token_provider("/var/token"),
+        "refresh_buffer_seconds": 120.0,
+    }
+)
+```
+
+### Vision
+
+With an image URL:
+
+```python
+prompt = "What is in this image?"
+img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/2023_06_08_Raccoon1.jpg/1599px-2023_06_08_Raccoon1.jpg"
+
+response = client.responses.create(
+    model="gpt-5.2",
+    input=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "input_text", "text": prompt},
+                {"type": "input_image", "image_url": f"{img_url}"},
+            ],
+        }
+    ],
+)
+```
+
+With the image as a base64 encoded string:
+
+```python
+import base64
+from openai import OpenAI
+
+client = OpenAI()
+
+prompt = "What is in this image?"
+with open("path/to/image.png", "rb") as image_file:
+    b64_image = base64.b64encode(image_file.read()).decode("utf-8")
+
+response = client.responses.create(
+    model="gpt-5.2",
+    input=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "input_text", "text": prompt},
+                {"type": "input_image", "image_url": f"data:image/png;base64,{b64_image}"},
+            ],
+        }
+    ],
+)
+```
+
+## Async usage
+
+Simply import `AsyncOpenAI` instead of `OpenAI` and use `await` with each API call:
+
+```python
+import os
+import asyncio
+from openai import AsyncOpenAI
+
+client = AsyncOpenAI(
+    # This is the default and can be omitted
+    api_key=os.environ.get("OPENAI_API_KEY"),
+)
+
+
+async def main() -> None:
+    response = await client.responses.create(
+        model="gpt-5.2", input="Explain disestablishmentarianism to a smart five year old."
+    )
+    print(response.output_text)
+
+
+asyncio.run(main())
+```
+
+Functionality between the synchronous and asynchronous clients is otherwise identical.
+
+### With aiohttp
+
+By default, the async client uses `httpx` for HTTP requests. However, for improved concurrency performance you may also use `aiohttp` as the HTTP backend.
+
+You can enable this by installing `aiohttp`:
+
+```sh
+# install from PyPI
+pip install openai[aiohttp]
+```
+
+Then you can enable it by instantiating the client with `http_client=DefaultAioHttpClient()`:
+
+```python
+import os
+import asyncio
+from openai import DefaultAioHttpClient
+from openai import AsyncOpenAI
+
+
+async def main() -> None:
+    async with AsyncOpenAI(
+        api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
+        http_client=DefaultAioHttpClient(),
+    ) as client:
+        chat_completion = await client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Say this is a test",
+                }
+            ],
+            model="gpt-5.2",
+        )
+
+
+asyncio.run(main())
+```
+
+## Streaming responses
+
+We provide support for streaming responses using Server Side Events (SSE).
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+stream = client.responses.create(
+    model="gpt-5.2",
+    input="Write a one-sentence bedtime story about a unicorn.",
+    stream=True,
+)
+
+for event in stream:
+    print(event)
+```
+
+The async client uses the exact same interface.
+
+```python
+import asyncio
+from openai import AsyncOpenAI
+
+client = AsyncOpenAI()
+
+
+async def main():
+    stream = await client.responses.create(
+        model="gpt-5.2",
+        input="Write a one-sentence bedtime story about a unicorn.",
+        stream=True,
+    )
+
+    async for event in stream:
+        print(event)
+
+
+asyncio.run(main())
+```
+
+## Realtime API
+
+The Realtime API enables you to build low-latency, multi-modal conversational experiences. It currently supports text and audio as both input and output, as well as [function calling](https://platform.openai.com/docs/guides/function-calling) through a WebSocket connection.
+
+Under the hood the SDK uses the [`websockets`](https://websockets.readthedocs.io/en/stable/) library to manage connections.
+
+The Realtime API works through a combination of client-sent events and server-sent events. Clients can send events to do things like update session configuration or send text and audio inputs. Server events confirm when audio responses have completed, or when a text response from the model has been received. A full event reference can be found [here](https://platform.openai.com/docs/api-reference/realtime-client-events) and a guide can be found [here](https://platform.openai.com/docs/guides/realtime).
+
+Basic text based example:
+
+```py
+import asyncio
+from openai import AsyncOpenAI
+
+async def main():
+    client = AsyncOpenAI()
+
+    async with client.realtime.connect(model="gpt-realtime") as connection:
+        await connection.session.update(
+            session={"type": "realtime", "output_modalities": ["text"]}
+        )
+
+        await connection.conversation.item.create(
+            item={
+                "type": "message",
+                "role": "user",
+                "content": [{"type": "input_text", "text": "Say hello!"}],
+            }
+        )
+        await connection.response.create()
+
+        async for event in connection:
+            if event.type == "response.output_text.delta":
+                print(event.delta, flush=True, end="")
+
+            elif event.type == "response.output_text.done":
+                print()
+
+            elif event.type == "response.done":
+                break
+
+asyncio.run(main())
+```
+
+However the real magic of the Realtime API is handling audio inputs / outputs, see this example [TUI script](https://github.com/openai/openai-python/blob/main/examples/realtime/push_to_talk_app.py) for a fully fledged example.
+
+### Realtime error handling
+
+Whenever an error occurs, the Realtime API will send an [`error` event](https://platform.openai.com/docs/guides/realtime-model-capabilities#error-handling) and the connection will stay open and remain usable. This means you need to handle it yourself, as _no errors are raised directly_ by the SDK when an `error` event comes in.
+
+```py
+client = AsyncOpenAI()
+
+async with client.realtime.connect(model="gpt-realtime") as connection:
+    ...
+    async for event in connection:
+        if event.type == 'error':
+            print(event.error.type)
+            print(event.error.code)
+            print(event.error.event_id)
+            print(event.error.message)
+```
+
+## Using types
+
+Nested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:
+
+- Serializing back into JSON, `model.to_json()`
+- Converting to a dictionary, `model.to_dict()`
+
+Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.
+
+## Pagination
+
+List methods in the OpenAI API are paginated.
+
+This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+all_jobs = []
+# Automatically fetches more pages as needed.
+for job in client.fine_tuning.jobs.list(
+    limit=20,
+):
+    # Do something with job here
+    all_jobs.append(job)
+print(all_jobs)
+```
+
+Or, asynchronously:
+
+```python
+import asyncio
+from openai import AsyncOpenAI
+
+client = AsyncOpenAI()
+
+
+async def main() -> None:
+    all_jobs = []
+    # Iterate through items across all pages, issuing requests as needed.
+    async for job in client.fine_tuning.jobs.list(
+        limit=20,
+    ):
+        all_jobs.append(job)
+    print(all_jobs)
+
+
+asyncio.run(main())
+```
+
+Alternatively, you can use the `.has_next_page()`, `.next_page_info()`, or `.get_next_page()` methods for more granular control working with pages:
+
+```python
+first_page = await client.fine_tuning.jobs.list(
+    limit=20,
+)
+if first_page.has_next_page():
+    print(f"will fetch next page using these details: {first_page.next_page_info()}")
+    next_page = await first_page.get_next_page()
+    print(f"number of items we just fetched: {len(next_page.data)}")
+
+# Remove `await` for non-async usage.
+```
+
+Or just work directly with the returned data:
+
+```python
+first_page = await client.fine_tuning.jobs.list(
+    limit=20,
+)
+
+print(f"next page cursor: {first_page.after}")  # => "next page cursor: ..."
+for job in first_page.data:
+    print(job.id)
+
+# Remove `await` for non-async usage.
+```
+
+## Nested params
+
+Nested parameters are dictionaries, typed using `TypedDict`, for example:
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+response = client.chat.responses.create(
+    input=[
+        {
+            "role": "user",
+            "content": "How much ?",
+        }
+    ],
+    model="gpt-5.2",
+    response_format={"type": "json_object"},
+)
+```
+
+## File uploads
+
+Request parameters that correspond to file uploads can be passed as `bytes`, or a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance or a tuple of `(filename, contents, media type)`.
+
+```python
+from pathlib import Path
+from openai import OpenAI
+
+client = OpenAI()
+
+client.files.create(
+    file=Path("input.jsonl"),
+    purpose="fine-tune",
+)
+```
+
+The async client uses the exact same interface. If you pass a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance, the file contents will be read asynchronously automatically.
+
+## Webhook Verification
+
+Verifying webhook signatures is _optional but encouraged_.
+
+For more information about webhooks, see [the API docs](https://platform.openai.com/docs/guides/webhooks).
+
+### Parsing webhook payloads
+
+For most use cases, you will likely want to verify the webhook and parse the payload at the same time. To achieve this, we provide the method `client.webhooks.unwrap()`, which parses a webhook request and verifies that it was sent by OpenAI. This method will raise an error if the signature is invalid.
+
+Note that the `body` parameter must be the raw JSON string sent from the server (do not parse it first). The `.unwrap()` method will parse this JSON for you into an event object after verifying the webhook was sent from OpenAI.
+
+```python
+from openai import OpenAI
+from flask import Flask, request
+
+app = Flask(__name__)
+client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default
+
+
+@app.route("/webhook", methods=["POST"])
+def webhook():
+    request_body = request.get_data(as_text=True)
+
+    try:
+        event = client.webhooks.unwrap(request_body, request.headers)
+
+        if event.type == "response.completed":
+            print("Response completed:", event.data)
+        elif event.type == "response.failed":
+            print("Response failed:", event.data)
+        else:
+            print("Unhandled event type:", event.type)
+
+        return "ok"
+    except Exception as e:
+        print("Invalid signature:", e)
+        return "Invalid signature", 400
+
+
+if __name__ == "__main__":
+    app.run(port=8000)
+```
+
+### Verifying webhook payloads directly
+
+In some cases, you may want to verify the webhook separately from parsing the payload. If you prefer to handle these steps separately, we provide the method `client.webhooks.verify_signature()` to _only verify_ the signature of a webhook request. Like `.unwrap()`, this method will raise an error if the signature is invalid.
+
+Note that the `body` parameter must be the raw JSON string sent from the server (do not parse it first). You will then need to parse the body after verifying the signature.
+
+```python
+import json
+from openai import OpenAI
+from flask import Flask, request
+
+app = Flask(__name__)
+client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default
+
+
+@app.route("/webhook", methods=["POST"])
+def webhook():
+    request_body = request.get_data(as_text=True)
+
+    try:
+        client.webhooks.verify_signature(request_body, request.headers)
+
+        # Parse the body after verification
+        event = json.loads(request_body)
+        print("Verified event:", event)
+
+        return "ok"
+    except Exception as e:
+        print("Invalid signature:", e)
+        return "Invalid signature", 400
+
+
+if __name__ == "__main__":
+    app.run(port=8000)
+```
+
+## Handling errors
+
+When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `openai.APIConnectionError` is raised.
+
+When the API returns a non-success status code (that is, 4xx or 5xx
+response), a subclass of `openai.APIStatusError` is raised, containing `status_code` and `response` properties.
+
+All errors inherit from `openai.APIError`.
+
+```python
+import openai
+from openai import OpenAI
+
+client = OpenAI()
+
+try:
+    client.fine_tuning.jobs.create(
+        model="gpt-4o",
+        training_file="file-abc123",
+    )
+except openai.APIConnectionError as e:
+    print("The server could not be reached")
+    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
+except openai.RateLimitError as e:
+    print("A 429 status code was received; we should back off a bit.")
+except openai.APIStatusError as e:
+    print("Another non-200-range status code was received")
+    print(e.status_code)
+    print(e.response)
+```
+
+Error codes are as follows:
+
+| Status Code | Error Type                 |
+| ----------- | -------------------------- |
+| 400         | `BadRequestError`          |
+| 401         | `AuthenticationError`      |
+| 403         | `PermissionDeniedError`    |
+| 404         | `NotFoundError`            |
+| 422         | `UnprocessableEntityError` |
+| 429         | `RateLimitError`           |
+| >=500       | `InternalServerError`      |
+| N/A         | `APIConnectionError`       |
+
+## Request IDs
+
+> For more information on debugging requests, see [these docs](https://platform.openai.com/docs/api-reference/debugging-requests)
+
+All object responses in the SDK provide a `_request_id` property which is added from the `x-request-id` response header so that you can quickly log failing requests and report them back to OpenAI.
+
+```python
+response = await client.responses.create(
+    model="gpt-5.2",
+    input="Say 'this is a test'.",
+)
+print(response._request_id)  # req_123
+```
+
+Note that unlike other properties that use an `_` prefix, the `_request_id` property
+_is_ public. Unless documented otherwise, _all_ other `_` prefix properties,
+methods and modules are _private_.
+
+> [!IMPORTANT]  
+> If you need to access request IDs for failed requests you must catch the `APIStatusError` exception
+
+```python
+import openai
+
+try:
+    completion = await client.chat.completions.create(
+        messages=[{"role": "user", "content": "Say this is a test"}], model="gpt-5.2"
+    )
+except openai.APIStatusError as exc:
+    print(exc.request_id)  # req_123
+    raise exc
+```
+
+## Retries
+
+Certain errors are automatically retried 2 times by default, with a short exponential backoff.
+Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,
+429 Rate Limit, and >=500 Internal errors are all retried by default.
+
+You can use the `max_retries` option to configure or disable retry settings:
+
+```python
+from openai import OpenAI
+
+# Configure the default for all requests:
+client = OpenAI(
+    # default is 2
+    max_retries=0,
+)
+
+# Or, configure per-request:
+client.with_options(max_retries=5).chat.completions.create(
+    messages=[
+        {
+            "role": "user",
+            "content": "How can I get the name of the current day in JavaScript?",
+        }
+    ],
+    model="gpt-5.2",
+)
+```
+
+## Timeouts
+
+By default requests time out after 10 minutes. You can configure this with a `timeout` option,
+which accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/timeouts/#fine-tuning-the-configuration) object:
+
+```python
+from openai import OpenAI
+
+# Configure the default for all requests:
+client = OpenAI(
+    # 20 seconds (default is 10 minutes)
+    timeout=20.0,
+)
+
+# More granular control:
+client = OpenAI(
+    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
+)
+
+# Override per-request:
+client.with_options(timeout=5.0).chat.completions.create(
+    messages=[
+        {
+            "role": "user",
+            "content": "How can I list all files in a directory using Python?",
+        }
+    ],
+    model="gpt-5.2",
+)
+```
+
+On timeout, an `APITimeoutError` is thrown.
+
+Note that requests that time out are [retried twice by default](https://github.com/openai/openai-python/tree/main/#retries).
+
+## Advanced
+
+### Logging
+
+We use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.
+
+You can enable logging by setting the environment variable `OPENAI_LOG` to `info`.
+
+```shell
+$ export OPENAI_LOG=info
+```
+
+Or to `debug` for more verbose logging.
+
+### How to tell whether `None` means `null` or missing
+
+In an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:
+
+```py
+if response.my_field is None:
+  if 'my_field' not in response.model_fields_set:
+    print('Got json like {}, without a "my_field" key present at all.')
+  else:
+    print('Got json like {"my_field": null}.')
+```
+
+### Accessing raw response data (e.g. headers)
+
+The "raw" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,
+
+```py
+from openai import OpenAI
+
+client = OpenAI()
+response = client.chat.completions.with_raw_response.create(
+    messages=[{
+        "role": "user",
+        "content": "Say this is a test",
+    }],
+    model="gpt-5.2",
+)
+print(response.headers.get('X-My-Header'))
+
+completion = response.parse()  # get the object that `chat.completions.create()` would have returned
+print(completion)
+```
+
+These methods return a [`LegacyAPIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_legacy_response.py) object. This is a legacy class as we're changing it slightly in the next major version.
+
+For the sync client this will mostly be the same with the exception
+of `content` & `text` will be methods instead of properties. In the
+async client, all methods will be async.
+
+A migration script will be provided & the migration in general should
+be smooth.
+
+#### `.with_streaming_response`
+
+The above interface eagerly reads the full response body when you make the request, which may not always be what you want.
+
+To stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.
+
+As such, `.with_streaming_response` methods return a different [`APIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_response.py) object, and the async client returns an [`AsyncAPIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_response.py) object.
+
+```python
+with client.chat.completions.with_streaming_response.create(
+    messages=[
+        {
+            "role": "user",
+            "content": "Say this is a test",
+        }
+    ],
+    model="gpt-5.2",
+) as response:
+    print(response.headers.get("X-My-Header"))
+
+    for line in response.iter_lines():
+        print(line)
+```
+
+The context manager is required so that the response will reliably be closed.
+
+### Making custom/undocumented requests
+
+This library is typed for convenient access to the documented API.
+
+If you need to access undocumented endpoints, params, or response properties, the library can still be used.
+
+#### Undocumented endpoints
+
+To make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other
+http verbs. Options on the client will be respected (such as retries) when making this request.
+
+```py
+import httpx
+
+response = client.post(
+    "/foo",
+    cast_to=httpx.Response,
+    body={"my_param": True},
+)
+
+print(response.headers.get("x-foo"))
+```
+
+#### Undocumented request params
+
+If you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request
+options.
+
+#### Undocumented response properties
+
+To access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You
+can also get all the extra fields on the Pydantic model as a dict with
+[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).
+
+### Configuring the HTTP client
+
+You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:
+
+- Support for [proxies](https://www.python-httpx.org/advanced/proxies/)
+- Custom [transports](https://www.python-httpx.org/advanced/transports/)
+- Additional [advanced](https://www.python-httpx.org/advanced/clients/) functionality
+
+```python
+import httpx
+from openai import OpenAI, DefaultHttpxClient
+
+client = OpenAI(
+    # Or use the `OPENAI_BASE_URL` env var
+    base_url="http://my.test.server.example.com:8083/v1",
+    http_client=DefaultHttpxClient(
+        proxy="http://my.test.proxy.example.com",
+        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
+    ),
+)
+```
+
+You can also customize the client on a per-request basis by using `with_options()`:
+
+```python
+client.with_options(http_client=DefaultHttpxClient(...))
+```
+
+### Managing HTTP resources
+
+By default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.
+
+```py
+from openai import OpenAI
+
+with OpenAI() as client:
+  # make requests here
+  ...
+
+# HTTP client is now closed
+```
+
+## Microsoft Azure OpenAI
+
+To use this library with [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/overview), use the `AzureOpenAI`
+class instead of the `OpenAI` class.
+
+> [!IMPORTANT]
+> The Azure API shape differs from the core API shape which means that the static types for responses / params
+> won't always be correct.
+
+```py
+from openai import AzureOpenAI
+
+# gets the API Key from environment variable AZURE_OPENAI_API_KEY
+client = AzureOpenAI(
+    # https://learn.microsoft.com/azure/ai-services/openai/reference#rest-api-versioning
+    api_version="2023-07-01-preview",
+    # https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
+    azure_endpoint="https://example-endpoint.openai.azure.com",
+)
+
+completion = client.chat.completions.create(
+    model="deployment-name",  # e.g. gpt-35-instant
+    messages=[
+        {
+            "role": "user",
+            "content": "How do I output all files in a directory using Python?",
+        },
+    ],
+)
+print(completion.to_json())
+```
+
+In addition to the options provided in the base `OpenAI` client, the following options are provided:
+
+- `azure_endpoint` (or the `AZURE_OPENAI_ENDPOINT` environment variable)
+- `azure_deployment`
+- `api_version` (or the `OPENAI_API_VERSION` environment variable)
+- `azure_ad_token` (or the `AZURE_OPENAI_AD_TOKEN` environment variable)
+- `azure_ad_token_provider`
+
+An example of using the client with Microsoft Entra ID (formerly known as Azure Active Directory) can be found [here](https://github.com/openai/openai-python/blob/main/examples/azure_ad.py).
+
+## Versioning
+
+This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:
+
+1. Changes that only affect static types, without breaking runtime behavior.
+2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals.)_
+3. Changes that we do not expect to impact the vast majority of users in practice.
+
+We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
+
+We are keen for your feedback; please open an [issue](https://www.github.com/openai/openai-python/issues) with questions, bugs, or suggestions.
+
+### Determining the installed version
+
+If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.
+
+You can determine the version that is being used at runtime with:
+
+```py
+import openai
+print(openai.__version__)
+```
+
+## Requirements
+
+Python 3.9 or higher.
+
+## Contributing
+
+See [the contributing documentation](https://github.com/openai/openai-python/tree/main/./CONTRIBUTING.md).

.venv311/Lib/site-packages/openai-2.36.0.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: hatchling 1.26.3
+Root-Is-Purelib: true
+Tag: py3-none-any

.venv311/Lib/site-packages/openai-2.36.0.dist-info/licenses/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2026 OpenAI
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

.venv311/Lib/site-packages/openai/_extras/__init__.py

@@ -0,0 +1,3 @@
+from .numpy_proxy import numpy as numpy, has_numpy as has_numpy
+from .pandas_proxy import pandas as pandas
+from .sounddevice_proxy import sounddevice as sounddevice

.venv311/Lib/site-packages/openai/_extras/_common.py

@@ -0,0 +1,21 @@
+from .._exceptions import OpenAIError
+
+INSTRUCTIONS = """
+
+OpenAI error:
+
+    missing `{library}`
+
+This feature requires additional dependencies:
+
+    $ pip install openai[{extra}]
+
+"""
+
+
+def format_instructions(*, library: str, extra: str) -> str:
+    return INSTRUCTIONS.format(library=library, extra=extra)
+
+
+class MissingDependencyError(OpenAIError):
+    pass

.venv311/Lib/site-packages/openai/_extras/numpy_proxy.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import numpy as numpy
+
+
+NUMPY_INSTRUCTIONS = format_instructions(library="numpy", extra="voice_helpers")
+
+
+class NumpyProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import numpy
+        except ImportError as err:
+            raise MissingDependencyError(NUMPY_INSTRUCTIONS) from err
+
+        return numpy
+
+
+if not TYPE_CHECKING:
+    numpy = NumpyProxy()
+
+
+def has_numpy() -> bool:
+    try:
+        import numpy  # noqa: F401  # pyright: ignore[reportUnusedImport]
+    except ImportError:
+        return False
+
+    return True

.venv311/Lib/site-packages/openai/_extras/pandas_proxy.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import pandas as pandas
+
+
+PANDAS_INSTRUCTIONS = format_instructions(library="pandas", extra="datalib")
+
+
+class PandasProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import pandas
+        except ImportError as err:
+            raise MissingDependencyError(PANDAS_INSTRUCTIONS) from err
+
+        return pandas
+
+
+if not TYPE_CHECKING:
+    pandas = PandasProxy()

.venv311/Lib/site-packages/openai/_extras/sounddevice_proxy.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import sounddevice as sounddevice  # type: ignore
+
+
+SOUNDDEVICE_INSTRUCTIONS = format_instructions(library="sounddevice", extra="voice_helpers")
+
+
+class SounddeviceProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import sounddevice  # type: ignore
+        except ImportError as err:
+            raise MissingDependencyError(SOUNDDEVICE_INSTRUCTIONS) from err
+
+        return sounddevice
+
+
+if not TYPE_CHECKING:
+    sounddevice = SounddeviceProxy()

.venv311/Lib/site-packages/openai/_utils/__init__.py

@@ -0,0 +1,67 @@
+from ._logs import SensitiveHeadersFilter as SensitiveHeadersFilter
+from ._path import path_template as path_template
+from ._sync import asyncify as asyncify
+from ._proxy import LazyProxy as LazyProxy
+from ._utils import (
+    flatten as flatten,
+    is_dict as is_dict,
+    is_list as is_list,
+    is_given as is_given,
+    is_tuple as is_tuple,
+    json_safe as json_safe,
+    lru_cache as lru_cache,
+    is_mapping as is_mapping,
+    is_tuple_t as is_tuple_t,
+    is_iterable as is_iterable,
+    is_sequence as is_sequence,
+    coerce_float as coerce_float,
+    is_mapping_t as is_mapping_t,
+    removeprefix as removeprefix,
+    removesuffix as removesuffix,
+    extract_files as extract_files,
+    is_sequence_t as is_sequence_t,
+    required_args as required_args,
+    coerce_boolean as coerce_boolean,
+    coerce_integer as coerce_integer,
+    file_from_path as file_from_path,
+    is_azure_client as is_azure_client,
+    strip_not_given as strip_not_given,
+    get_async_library as get_async_library,
+    maybe_coerce_float as maybe_coerce_float,
+    get_required_header as get_required_header,
+    maybe_coerce_boolean as maybe_coerce_boolean,
+    maybe_coerce_integer as maybe_coerce_integer,
+    is_async_azure_client as is_async_azure_client,
+)
+from ._compat import (
+    get_args as get_args,
+    is_union as is_union,
+    get_origin as get_origin,
+    is_typeddict as is_typeddict,
+    is_literal_type as is_literal_type,
+)
+from ._typing import (
+    is_list_type as is_list_type,
+    is_union_type as is_union_type,
+    extract_type_arg as extract_type_arg,
+    is_iterable_type as is_iterable_type,
+    is_required_type as is_required_type,
+    is_sequence_type as is_sequence_type,
+    is_annotated_type as is_annotated_type,
+    is_type_alias_type as is_type_alias_type,
+    strip_annotated_type as strip_annotated_type,
+    extract_type_var_from_base as extract_type_var_from_base,
+)
+from ._streams import consume_sync_iterator as consume_sync_iterator, consume_async_iterator as consume_async_iterator
+from ._transform import (
+    PropertyInfo as PropertyInfo,
+    transform as transform,
+    async_transform as async_transform,
+    maybe_transform as maybe_transform,
+    async_maybe_transform as async_maybe_transform,
+)
+from ._reflection import (
+    function_has_argument as function_has_argument,
+    assert_signatures_in_sync as assert_signatures_in_sync,
+)
+from ._datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime

.venv311/Lib/site-packages/openai/_utils/_compat.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import sys
+import typing_extensions
+from typing import Any, Type, Union, Literal, Optional
+from datetime import date, datetime
+from typing_extensions import get_args as _get_args, get_origin as _get_origin
+
+from .._types import StrBytesIntFloat
+from ._datetime_parse import parse_date as _parse_date, parse_datetime as _parse_datetime
+
+_LITERAL_TYPES = {Literal, typing_extensions.Literal}
+
+
+def get_args(tp: type[Any]) -> tuple[Any, ...]:
+    return _get_args(tp)
+
+
+def get_origin(tp: type[Any]) -> type[Any] | None:
+    return _get_origin(tp)
+
+
+def is_union(tp: Optional[Type[Any]]) -> bool:
+    if sys.version_info < (3, 10):
+        return tp is Union  # type: ignore[comparison-overlap]
+    else:
+        import types
+
+        return tp is Union or tp is types.UnionType  # type: ignore[comparison-overlap]
+
+
+def is_typeddict(tp: Type[Any]) -> bool:
+    return typing_extensions.is_typeddict(tp)
+
+
+def is_literal_type(tp: Type[Any]) -> bool:
+    return get_origin(tp) in _LITERAL_TYPES
+
+
+def parse_date(value: Union[date, StrBytesIntFloat]) -> date:
+    return _parse_date(value)
+
+
+def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:
+    return _parse_datetime(value)

.venv311/Lib/site-packages/openai/_utils/_datetime_parse.py

@@ -0,0 +1,136 @@
+"""
+This file contains code from https://github.com/pydantic/pydantic/blob/main/pydantic/v1/datetime_parse.py
+without the Pydantic v1 specific errors.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Dict, Union, Optional
+from datetime import date, datetime, timezone, timedelta
+
+from .._types import StrBytesIntFloat
+
+date_expr = r"(?P<year>\d{4})-(?P<month>\d{1,2})-(?P<day>\d{1,2})"
+time_expr = (
+    r"(?P<hour>\d{1,2}):(?P<minute>\d{1,2})"
+    r"(?::(?P<second>\d{1,2})(?:\.(?P<microsecond>\d{1,6})\d{0,6})?)?"
+    r"(?P<tzinfo>Z|[+-]\d{2}(?::?\d{2})?)?$"
+)
+
+date_re = re.compile(f"{date_expr}$")
+datetime_re = re.compile(f"{date_expr}[T ]{time_expr}")
+
+
+EPOCH = datetime(1970, 1, 1)
+# if greater than this, the number is in ms, if less than or equal it's in seconds
+# (in seconds this is 11th October 2603, in ms it's 20th August 1970)
+MS_WATERSHED = int(2e10)
+# slightly more than datetime.max in ns - (datetime.max - EPOCH).total_seconds() * 1e9
+MAX_NUMBER = int(3e20)
+
+
+def _get_numeric(value: StrBytesIntFloat, native_expected_type: str) -> Union[None, int, float]:
+    if isinstance(value, (int, float)):
+        return value
+    try:
+        return float(value)
+    except ValueError:
+        return None
+    except TypeError:
+        raise TypeError(f"invalid type; expected {native_expected_type}, string, bytes, int or float") from None
+
+
+def _from_unix_seconds(seconds: Union[int, float]) -> datetime:
+    if seconds > MAX_NUMBER:
+        return datetime.max
+    elif seconds < -MAX_NUMBER:
+        return datetime.min
+
+    while abs(seconds) > MS_WATERSHED:
+        seconds /= 1000
+    dt = EPOCH + timedelta(seconds=seconds)
+    return dt.replace(tzinfo=timezone.utc)
+
+
+def _parse_timezone(value: Optional[str]) -> Union[None, int, timezone]:
+    if value == "Z":
+        return timezone.utc
+    elif value is not None:
+        offset_mins = int(value[-2:]) if len(value) > 3 else 0
+        offset = 60 * int(value[1:3]) + offset_mins
+        if value[0] == "-":
+            offset = -offset
+        return timezone(timedelta(minutes=offset))
+    else:
+        return None
+
+
+def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:
+    """
+    Parse a datetime/int/float/string and return a datetime.datetime.
+
+    This function supports time zone offsets. When the input contains one,
+    the output uses a timezone with a fixed offset from UTC.
+
+    Raise ValueError if the input is well formatted but not a valid datetime.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, datetime):
+        return value
+
+    number = _get_numeric(value, "datetime")
+    if number is not None:
+        return _from_unix_seconds(number)
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    assert not isinstance(value, (float, int))
+
+    match = datetime_re.match(value)
+    if match is None:
+        raise ValueError("invalid datetime format")
+
+    kw = match.groupdict()
+    if kw["microsecond"]:
+        kw["microsecond"] = kw["microsecond"].ljust(6, "0")
+
+    tzinfo = _parse_timezone(kw.pop("tzinfo"))
+    kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None}
+    kw_["tzinfo"] = tzinfo
+
+    return datetime(**kw_)  # type: ignore
+
+
+def parse_date(value: Union[date, StrBytesIntFloat]) -> date:
+    """
+    Parse a date/int/float/string and return a datetime.date.
+
+    Raise ValueError if the input is well formatted but not a valid date.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, date):
+        if isinstance(value, datetime):
+            return value.date()
+        else:
+            return value
+
+    number = _get_numeric(value, "date")
+    if number is not None:
+        return _from_unix_seconds(number).date()
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    assert not isinstance(value, (float, int))
+    match = date_re.match(value)
+    if match is None:
+        raise ValueError("invalid date format")
+
+    kw = {k: int(v) for k, v in match.groupdict().items()}
+
+    try:
+        return date(**kw)
+    except ValueError:
+        raise ValueError("invalid date format") from None

.venv311/Lib/site-packages/openai/_utils/_json.py

@@ -0,0 +1,35 @@
+import json
+from typing import Any
+from datetime import datetime
+from typing_extensions import override
+
+import pydantic
+
+from .._compat import model_dump
+
+
+def openapi_dumps(obj: Any) -> bytes:
+    """
+    Serialize an object to UTF-8 encoded JSON bytes.
+
+    Extends the standard json.dumps with support for additional types
+    commonly used in the SDK, such as `datetime`, `pydantic.BaseModel`, etc.
+    """
+    return json.dumps(
+        obj,
+        cls=_CustomEncoder,
+        # Uses the same defaults as httpx's JSON serialization
+        ensure_ascii=False,
+        separators=(",", ":"),
+        allow_nan=False,
+    ).encode()
+
+
+class _CustomEncoder(json.JSONEncoder):
+    @override
+    def default(self, o: Any) -> Any:
+        if isinstance(o, datetime):
+            return o.isoformat()
+        if isinstance(o, pydantic.BaseModel):
+            return model_dump(o, exclude_unset=True, mode="json", by_alias=True)
+        return super().default(o)

.venv311/Lib/site-packages/openai/_utils/_logs.py

@@ -0,0 +1,42 @@
+import os
+import logging
+from typing_extensions import override
+
+from ._utils import is_dict
+
+logger: logging.Logger = logging.getLogger("openai")
+httpx_logger: logging.Logger = logging.getLogger("httpx")
+
+
+SENSITIVE_HEADERS = {"api-key", "authorization"}
+
+
+def _basic_config() -> None:
+    # e.g. [2023-10-05 14:12:26 - openai._base_client:818 - DEBUG] HTTP Request: POST http://127.0.0.1:4010/foo/bar "200 OK"
+    logging.basicConfig(
+        format="[%(asctime)s - %(name)s:%(lineno)d - %(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+
+def setup_logging() -> None:
+    env = os.environ.get("OPENAI_LOG")
+    if env == "debug":
+        _basic_config()
+        logger.setLevel(logging.DEBUG)
+        httpx_logger.setLevel(logging.DEBUG)
+    elif env == "info":
+        _basic_config()
+        logger.setLevel(logging.INFO)
+        httpx_logger.setLevel(logging.INFO)
+
+
+class SensitiveHeadersFilter(logging.Filter):
+    @override
+    def filter(self, record: logging.LogRecord) -> bool:
+        if is_dict(record.args) and "headers" in record.args and is_dict(record.args["headers"]):
+            headers = record.args["headers"] = {**record.args["headers"]}
+            for header in headers:
+                if str(header).lower() in SENSITIVE_HEADERS:
+                    headers[header] = "<redacted>"
+        return True

.venv311/Lib/site-packages/openai/_utils/_path.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import re
+from typing import (
+    Any,
+    Mapping,
+    Callable,
+)
+from urllib.parse import quote
+
+# Matches '.' or '..' where each dot is either literal or percent-encoded (%2e / %2E).
+_DOT_SEGMENT_RE = re.compile(r"^(?:\.|%2[eE]){1,2}$")
+
+_PLACEHOLDER_RE = re.compile(r"\{(\w+)\}")
+
+
+def _quote_path_segment_part(value: str) -> str:
+    """Percent-encode `value` for use in a URI path segment.
+
+    Considers characters not in `pchar` set from RFC 3986 §3.3 to be unsafe.
+    https://datatracker.ietf.org/doc/html/rfc3986#section-3.3
+    """
+    # quote() already treats unreserved characters (letters, digits, and -._~)
+    # as safe, so we only need to add sub-delims, ':', and '@'.
+    # Notably, unlike the default `safe` for quote(), / is unsafe and must be quoted.
+    return quote(value, safe="!$&'()*+,;=:@")
+
+
+def _quote_query_part(value: str) -> str:
+    """Percent-encode `value` for use in a URI query string.
+
+    Considers &, = and characters not in `query` set from RFC 3986 §3.4 to be unsafe.
+    https://datatracker.ietf.org/doc/html/rfc3986#section-3.4
+    """
+    return quote(value, safe="!$'()*+,;:@/?")
+
+
+def _quote_fragment_part(value: str) -> str:
+    """Percent-encode `value` for use in a URI fragment.
+
+    Considers characters not in `fragment` set from RFC 3986 §3.5 to be unsafe.
+    https://datatracker.ietf.org/doc/html/rfc3986#section-3.5
+    """
+    return quote(value, safe="!$&'()*+,;=:@/?")
+
+
+def _interpolate(
+    template: str,
+    values: Mapping[str, Any],
+    quoter: Callable[[str], str],
+) -> str:
+    """Replace {name} placeholders in `template`, quoting each value with `quoter`.
+
+    Placeholder names are looked up in `values`.
+
+    Raises:
+        KeyError: If a placeholder is not found in `values`.
+    """
+    # re.split with a capturing group returns alternating
+    # [text, name, text, name, ..., text] elements.
+    parts = _PLACEHOLDER_RE.split(template)
+
+    for i in range(1, len(parts), 2):
+        name = parts[i]
+        if name not in values:
+            raise KeyError(f"a value for placeholder {{{name}}} was not provided")
+        val = values[name]
+        if val is None:
+            parts[i] = "null"
+        elif isinstance(val, bool):
+            parts[i] = "true" if val else "false"
+        else:
+            parts[i] = quoter(str(values[name]))
+
+    return "".join(parts)
+
+
+def path_template(template: str, /, **kwargs: Any) -> str:
+    """Interpolate {name} placeholders in `template` from keyword arguments.
+
+    Args:
+        template: The template string containing {name} placeholders.
+        **kwargs: Keyword arguments to interpolate into the template.
+
+    Returns:
+        The template with placeholders interpolated and percent-encoded.
+
+        Safe characters for percent-encoding are dependent on the URI component.
+        Placeholders in path and fragment portions are percent-encoded where the `segment`
+        and `fragment` sets from RFC 3986 respectively are considered safe.
+        Placeholders in the query portion are percent-encoded where the `query` set from
+        RFC 3986 §3.3 is considered safe except for = and & characters.
+
+    Raises:
+        KeyError: If a placeholder is not found in `kwargs`.
+        ValueError: If resulting path contains /./ or /../ segments (including percent-encoded dot-segments).
+    """
+    # Split the template into path, query, and fragment portions.
+    fragment_template: str | None = None
+    query_template: str | None = None
+
+    rest = template
+    if "#" in rest:
+        rest, fragment_template = rest.split("#", 1)
+    if "?" in rest:
+        rest, query_template = rest.split("?", 1)
+    path_template = rest
+
+    # Interpolate each portion with the appropriate quoting rules.
+    path_result = _interpolate(path_template, kwargs, _quote_path_segment_part)
+
+    # Reject dot-segments (. and ..) in the final assembled path.  The check
+    # runs after interpolation so that adjacent placeholders or a mix of static
+    # text and placeholders that together form a dot-segment are caught.
+    # Also reject percent-encoded dot-segments to protect against incorrectly
+    # implemented normalization in servers/proxies.
+    for segment in path_result.split("/"):
+        if _DOT_SEGMENT_RE.match(segment):
+            raise ValueError(f"Constructed path {path_result!r} contains dot-segment {segment!r} which is not allowed")
+
+    result = path_result
+    if query_template is not None:
+        result += "?" + _interpolate(query_template, kwargs, _quote_query_part)
+    if fragment_template is not None:
+        result += "#" + _interpolate(fragment_template, kwargs, _quote_fragment_part)
+
+    return result

.venv311/Lib/site-packages/openai/_utils/_proxy.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Generic, TypeVar, Iterable, cast
+from typing_extensions import override
+
+T = TypeVar("T")
+
+
+class LazyProxy(Generic[T], ABC):
+    """Implements data methods to pretend that an instance is another instance.
+
+    This includes forwarding attribute access and other methods.
+    """
+
+    # Note: we have to special case proxies that themselves return proxies
+    # to support using a proxy as a catch-all for any random access, e.g. `proxy.foo.bar.baz`
+
+    def __getattr__(self, attr: str) -> object:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied  # pyright: ignore
+        return getattr(proxied, attr)
+
+    @override
+    def __repr__(self) -> str:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied.__class__.__name__
+        return repr(self.__get_proxied__())
+
+    @override
+    def __str__(self) -> str:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied.__class__.__name__
+        return str(proxied)
+
+    @override
+    def __dir__(self) -> Iterable[str]:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return []
+        return proxied.__dir__()
+
+    @property  # type: ignore
+    @override
+    def __class__(self) -> type:  # pyright: ignore
+        try:
+            proxied = self.__get_proxied__()
+        except Exception:
+            return type(self)
+        if issubclass(type(proxied), LazyProxy):
+            return type(proxied)
+        return proxied.__class__
+
+    def __get_proxied__(self) -> T:
+        return self.__load__()
+
+    def __as_proxied__(self) -> T:
+        """Helper method that returns the current proxy, typed as the loaded object"""
+        return cast(T, self)
+
+    @abstractmethod
+    def __load__(self) -> T: ...

.venv311/Lib/site-packages/openai/_utils/_reflection.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import inspect
+from typing import Any, Callable
+
+
+def function_has_argument(func: Callable[..., Any], arg_name: str) -> bool:
+    """Returns whether or not the given function has a specific parameter"""
+    sig = inspect.signature(func)
+    return arg_name in sig.parameters
+
+
+def assert_signatures_in_sync(
+    source_func: Callable[..., Any],
+    check_func: Callable[..., Any],
+    *,
+    exclude_params: set[str] = set(),
+    description: str = "",
+) -> None:
+    """Ensure that the signature of the second function matches the first."""
+
+    check_sig = inspect.signature(check_func)
+    source_sig = inspect.signature(source_func)
+
+    errors: list[str] = []
+
+    for name, source_param in source_sig.parameters.items():
+        if name in exclude_params:
+            continue
+
+        custom_param = check_sig.parameters.get(name)
+        if not custom_param:
+            errors.append(f"the `{name}` param is missing")
+            continue
+
+        if custom_param.annotation != source_param.annotation:
+            errors.append(
+                f"types for the `{name}` param are do not match; source={repr(source_param.annotation)} checking={repr(custom_param.annotation)}"
+            )
+            continue
+
+    if errors:
+        raise AssertionError(
+            f"{len(errors)} errors encountered when comparing signatures{description}:\n\n" + "\n\n".join(errors)
+        )

.venv311/Lib/site-packages/openai/_utils/_resources_proxy.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from typing import Any
+from typing_extensions import override
+
+from ._proxy import LazyProxy
+
+
+class ResourcesProxy(LazyProxy[Any]):
+    """A proxy for the `openai.resources` module.
+
+    This is used so that we can lazily import `openai.resources` only when
+    needed *and* so that users can just import `openai` and reference `openai.resources`
+    """
+
+    @override
+    def __load__(self) -> Any:
+        import importlib
+
+        mod = importlib.import_module("openai.resources")
+        return mod
+
+
+resources = ResourcesProxy().__as_proxied__()

.venv311/Lib/site-packages/openai/_utils/_streams.py

@@ -0,0 +1,12 @@
+from typing import Any
+from typing_extensions import Iterator, AsyncIterator
+
+
+def consume_sync_iterator(iterator: Iterator[Any]) -> None:
+    for _ in iterator:
+        ...
+
+
+async def consume_async_iterator(iterator: AsyncIterator[Any]) -> None:
+    async for _ in iterator:
+        ...

.venv311/Lib/site-packages/openai/_utils/_sync.py

@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+import asyncio
+import functools
+from typing import TypeVar, Callable, Awaitable
+from typing_extensions import ParamSpec
+
+import anyio
+import sniffio
+import anyio.to_thread
+
+T_Retval = TypeVar("T_Retval")
+T_ParamSpec = ParamSpec("T_ParamSpec")
+
+
+async def to_thread(
+    func: Callable[T_ParamSpec, T_Retval], /, *args: T_ParamSpec.args, **kwargs: T_ParamSpec.kwargs
+) -> T_Retval:
+    if sniffio.current_async_library() == "asyncio":
+        return await asyncio.to_thread(func, *args, **kwargs)
+
+    return await anyio.to_thread.run_sync(
+        functools.partial(func, *args, **kwargs),
+    )
+
+
+# inspired by `asyncer`, https://github.com/tiangolo/asyncer
+def asyncify(function: Callable[T_ParamSpec, T_Retval]) -> Callable[T_ParamSpec, Awaitable[T_Retval]]:
+    """
+    Take a blocking function and create an async one that receives the same
+    positional and keyword arguments.
+
+    Usage:
+
+    ```python
+    def blocking_func(arg1, arg2, kwarg1=None):
+        # blocking code
+        return result
+
+
+    result = asyncify(blocking_function)(arg1, arg2, kwarg1=value1)
+    ```
+
+    ## Arguments
+
+    `function`: a blocking regular callable (e.g. a function)
+
+    ## Return
+
+    An async function that takes the same positional and keyword arguments as the
+    original one, that when called runs the same original function in a thread worker
+    and returns the result.
+    """
+
+    async def wrapper(*args: T_ParamSpec.args, **kwargs: T_ParamSpec.kwargs) -> T_Retval:
+        return await to_thread(function, *args, **kwargs)
+
+    return wrapper

.venv311/Lib/site-packages/openai/_utils/_transform.py

@@ -0,0 +1,457 @@
+from __future__ import annotations
+
+import io
+import base64
+import pathlib
+from typing import Any, Mapping, TypeVar, cast
+from datetime import date, datetime
+from typing_extensions import Literal, get_args, override, get_type_hints as _get_type_hints
+
+import anyio
+import pydantic
+
+from ._utils import (
+    is_list,
+    is_given,
+    lru_cache,
+    is_mapping,
+    is_iterable,
+    is_sequence,
+)
+from .._files import is_base64_file_input
+from ._compat import get_origin, is_typeddict
+from ._typing import (
+    is_list_type,
+    is_union_type,
+    extract_type_arg,
+    is_iterable_type,
+    is_required_type,
+    is_sequence_type,
+    is_annotated_type,
+    strip_annotated_type,
+)
+
+_T = TypeVar("_T")
+
+
+# TODO: support for drilling globals() and locals()
+# TODO: ensure works correctly with forward references in all cases
+
+
+PropertyFormat = Literal["iso8601", "base64", "custom"]
+
+
+class PropertyInfo:
+    """Metadata class to be used in Annotated types to provide information about a given type.
+
+    For example:
+
+    class MyParams(TypedDict):
+        account_holder_name: Annotated[str, PropertyInfo(alias='accountHolderName')]
+
+    This means that {'account_holder_name': 'Robert'} will be transformed to {'accountHolderName': 'Robert'} before being sent to the API.
+    """
+
+    alias: str | None
+    format: PropertyFormat | None
+    format_template: str | None
+    discriminator: str | None
+
+    def __init__(
+        self,
+        *,
+        alias: str | None = None,
+        format: PropertyFormat | None = None,
+        format_template: str | None = None,
+        discriminator: str | None = None,
+    ) -> None:
+        self.alias = alias
+        self.format = format
+        self.format_template = format_template
+        self.discriminator = discriminator
+
+    @override
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(alias='{self.alias}', format={self.format}, format_template='{self.format_template}', discriminator='{self.discriminator}')"
+
+
+def maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `transform()` that allows `None` to be passed.
+
+    See `transform()` for more details.
+    """
+    if data is None:
+        return None
+    return transform(data, expected_type)
+
+
+# Wrapper over _transform_recursive providing fake types
+def transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = _transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+@lru_cache(maxsize=8096)
+def _get_annotated_type(type_: type) -> type | None:
+    """If the given type is an `Annotated` type then it is returned, if not `None` is returned.
+
+    This also unwraps the type when applicable, e.g. `Required[Annotated[T, ...]]`
+    """
+    if is_required_type(type_):
+        # Unwrap `Required[Annotated[T, ...]]` to `Annotated[T, ...]`
+        type_ = get_args(type_)[0]
+
+    if is_annotated_type(type_):
+        return type_
+
+    return None
+
+
+def _maybe_transform_key(key: str, type_: type) -> str:
+    """Transform the given `data` based on the annotations provided in `type_`.
+
+    Note: this function only looks at `Annotated` types that contain `PropertyInfo` metadata.
+    """
+    annotated_type = _get_annotated_type(type_)
+    if annotated_type is None:
+        # no `Annotated` definition for this type, no transformation needed
+        return key
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.alias is not None:
+            return annotation.alias
+
+    return key
+
+
+def _no_transform_needed(annotation: type) -> bool:
+    return annotation == float or annotation == int
+
+
+def _transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    from .._compat import model_dump
+
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    origin = get_origin(stripped_type) or stripped_type
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return _transform_typeddict(data, stripped_type)
+
+    if origin == dict and is_mapping(data):
+        items_type = get_args(stripped_type)[1]
+        return {key: _transform_recursive(value, annotation=items_type) for key, value in data.items()}
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+        # Sequence[T]
+        or (is_sequence_type(stripped_type) and is_sequence(data) and not isinstance(data, str))
+    ):
+        # dicts are technically iterable, but it is an iterable on the keys of the dict and is not usually
+        # intended as an iterable, so we don't transform it.
+        if isinstance(data, dict):
+            return cast(object, data)
+
+        inner_type = extract_type_arg(stripped_type, 0)
+        if _no_transform_needed(inner_type):
+            # for some types there is no need to transform anything, so we can get a small
+            # perf boost from skipping that work.
+            #
+            # but we still need to convert to a list to ensure the data is json-serializable
+            if is_list(data):
+                return data
+            return list(data)
+
+        return [_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = _transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True, mode="json", exclude=getattr(data, "__api_exclude__", None))
+
+    annotated_type = _get_annotated_type(annotation)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return _format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+def _format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    if format_ == "base64" and is_base64_file_input(data):
+        binary: str | bytes | None = None
+
+        if isinstance(data, pathlib.Path):
+            binary = data.read_bytes()
+        elif isinstance(data, io.IOBase):
+            binary = data.read()
+
+            if isinstance(binary, str):  # type: ignore[unreachable]
+                binary = binary.encode()
+
+        if not isinstance(binary, bytes):
+            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+
+        return base64.b64encode(binary).decode("ascii")
+
+    return data
+
+
+def _transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        if not is_given(value):
+            # we don't need to include omitted values here as they'll
+            # be stripped out before the request is sent anyway
+            continue
+
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = _transform_recursive(value, annotation=type_)
+    return result
+
+
+async def async_maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `async_transform()` that allows `None` to be passed.
+
+    See `async_transform()` for more details.
+    """
+    if data is None:
+        return None
+    return await async_transform(data, expected_type)
+
+
+async def async_transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = await _async_transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+async def _async_transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    from .._compat import model_dump
+
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    origin = get_origin(stripped_type) or stripped_type
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return await _async_transform_typeddict(data, stripped_type)
+
+    if origin == dict and is_mapping(data):
+        items_type = get_args(stripped_type)[1]
+        return {key: _transform_recursive(value, annotation=items_type) for key, value in data.items()}
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+        # Sequence[T]
+        or (is_sequence_type(stripped_type) and is_sequence(data) and not isinstance(data, str))
+    ):
+        # dicts are technically iterable, but it is an iterable on the keys of the dict and is not usually
+        # intended as an iterable, so we don't transform it.
+        if isinstance(data, dict):
+            return cast(object, data)
+
+        inner_type = extract_type_arg(stripped_type, 0)
+        if _no_transform_needed(inner_type):
+            # for some types there is no need to transform anything, so we can get a small
+            # perf boost from skipping that work.
+            #
+            # but we still need to convert to a list to ensure the data is json-serializable
+            if is_list(data):
+                return data
+            return list(data)
+
+        return [await _async_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = await _async_transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True, mode="json")
+
+    annotated_type = _get_annotated_type(annotation)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return await _async_format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+async def _async_format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    if format_ == "base64" and is_base64_file_input(data):
+        binary: str | bytes | None = None
+
+        if isinstance(data, pathlib.Path):
+            binary = await anyio.Path(data).read_bytes()
+        elif isinstance(data, io.IOBase):
+            binary = data.read()
+
+            if isinstance(binary, str):  # type: ignore[unreachable]
+                binary = binary.encode()
+
+        if not isinstance(binary, bytes):
+            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+
+        return base64.b64encode(binary).decode("ascii")
+
+    return data
+
+
+async def _async_transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        if not is_given(value):
+            # we don't need to include omitted values here as they'll
+            # be stripped out before the request is sent anyway
+            continue
+
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = await _async_transform_recursive(value, annotation=type_)
+    return result
+
+
+@lru_cache(maxsize=8096)
+def get_type_hints(
+    obj: Any,
+    globalns: dict[str, Any] | None = None,
+    localns: Mapping[str, Any] | None = None,
+    include_extras: bool = False,
+) -> dict[str, Any]:
+    return _get_type_hints(obj, globalns=globalns, localns=localns, include_extras=include_extras)

.venv311/Lib/site-packages/openai/_utils/_typing.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import sys
+import typing
+import typing_extensions
+from typing import Any, TypeVar, Iterable, cast
+from collections import abc as _c_abc
+from typing_extensions import (
+    TypeIs,
+    Required,
+    Annotated,
+    get_args,
+    get_origin,
+)
+
+from ._utils import lru_cache
+from .._types import InheritsGeneric
+from ._compat import is_union as _is_union
+
+
+def is_annotated_type(typ: type) -> bool:
+    return get_origin(typ) == Annotated
+
+
+def is_list_type(typ: type) -> bool:
+    return (get_origin(typ) or typ) == list
+
+
+def is_sequence_type(typ: type) -> bool:
+    origin = get_origin(typ) or typ
+    return origin == typing_extensions.Sequence or origin == typing.Sequence or origin == _c_abc.Sequence
+
+
+def is_iterable_type(typ: type) -> bool:
+    """If the given type is `typing.Iterable[T]`"""
+    origin = get_origin(typ) or typ
+    return origin == Iterable or origin == _c_abc.Iterable
+
+
+def is_union_type(typ: type) -> bool:
+    return _is_union(get_origin(typ))
+
+
+def is_required_type(typ: type) -> bool:
+    return get_origin(typ) == Required
+
+
+def is_typevar(typ: type) -> bool:
+    # type ignore is required because type checkers
+    # think this expression will always return False
+    return type(typ) == TypeVar  # type: ignore
+
+
+_TYPE_ALIAS_TYPES: tuple[type[typing_extensions.TypeAliasType], ...] = (typing_extensions.TypeAliasType,)
+if sys.version_info >= (3, 12):
+    _TYPE_ALIAS_TYPES = (*_TYPE_ALIAS_TYPES, typing.TypeAliasType)
+
+
+def is_type_alias_type(tp: Any, /) -> TypeIs[typing_extensions.TypeAliasType]:
+    """Return whether the provided argument is an instance of `TypeAliasType`.
+
+    ```python
+    type Int = int
+    is_type_alias_type(Int)
+    # > True
+    Str = TypeAliasType("Str", str)
+    is_type_alias_type(Str)
+    # > True
+    ```
+    """
+    return isinstance(tp, _TYPE_ALIAS_TYPES)
+
+
+# Extracts T from Annotated[T, ...] or from Required[Annotated[T, ...]]
+@lru_cache(maxsize=8096)
+def strip_annotated_type(typ: type) -> type:
+    if is_required_type(typ) or is_annotated_type(typ):
+        return strip_annotated_type(cast(type, get_args(typ)[0]))
+
+    return typ
+
+
+def extract_type_arg(typ: type, index: int) -> type:
+    args = get_args(typ)
+    try:
+        return cast(type, args[index])
+    except IndexError as err:
+        raise RuntimeError(f"Expected type {typ} to have a type argument at index {index} but it did not") from err
+
+
+def extract_type_var_from_base(
+    typ: type,
+    *,
+    generic_bases: tuple[type, ...],
+    index: int,
+    failure_message: str | None = None,
+) -> type:
+    """Given a type like `Foo[T]`, returns the generic type variable `T`.
+
+    This also handles the case where a concrete subclass is given, e.g.
+    ```py
+    class MyResponse(Foo[bytes]):
+        ...
+
+    extract_type_var(MyResponse, bases=(Foo,), index=0) -> bytes
+    ```
+
+    And where a generic subclass is given:
+    ```py
+    _T = TypeVar('_T')
+    class MyResponse(Foo[_T]):
+        ...
+
+    extract_type_var(MyResponse[bytes], bases=(Foo,), index=0) -> bytes
+    ```
+    """
+    cls = cast(object, get_origin(typ) or typ)
+    if cls in generic_bases:  # pyright: ignore[reportUnnecessaryContains]
+        # we're given the class directly
+        return extract_type_arg(typ, index)
+
+    # if a subclass is given
+    # ---
+    # this is needed as __orig_bases__ is not present in the typeshed stubs
+    # because it is intended to be for internal use only, however there does
+    # not seem to be a way to resolve generic TypeVars for inherited subclasses
+    # without using it.
+    if isinstance(cls, InheritsGeneric):
+        target_base_class: Any | None = None
+        for base in cls.__orig_bases__:
+            if base.__origin__ in generic_bases:
+                target_base_class = base
+                break
+
+        if target_base_class is None:
+            raise RuntimeError(
+                "Could not find the generic base class;\n"
+                "This should never happen;\n"
+                f"Does {cls} inherit from one of {generic_bases} ?"
+            )
+
+        extracted = extract_type_arg(target_base_class, index)
+        if is_typevar(extracted):
+            # If the extracted type argument is itself a type variable
+            # then that means the subclass itself is generic, so we have
+            # to resolve the type argument from the class itself, not
+            # the base class.
+            #
+            # Note: if there is more than 1 type argument, the subclass could
+            # change the ordering of the type arguments, this is not currently
+            # supported.
+            return extract_type_arg(typ, index)
+
+        return extracted
+
+    raise RuntimeError(failure_message or f"Could not resolve inner type variable at index {index} for {typ}")

.venv311/Lib/site-packages/openai/_utils/_utils.py

@@ -0,0 +1,449 @@
+from __future__ import annotations
+
+import os
+import re
+import inspect
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Tuple,
+    Mapping,
+    TypeVar,
+    Callable,
+    Iterable,
+    Sequence,
+    cast,
+    overload,
+)
+from pathlib import Path
+from datetime import date, datetime
+from typing_extensions import TypeGuard, get_args
+
+import sniffio
+
+from .._types import Omit, NotGiven, FileTypes, ArrayFormat, HeadersLike
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=Tuple[object, ...])
+_MappingT = TypeVar("_MappingT", bound=Mapping[str, object])
+_SequenceT = TypeVar("_SequenceT", bound=Sequence[object])
+CallableT = TypeVar("CallableT", bound=Callable[..., Any])
+
+if TYPE_CHECKING:
+    from ..lib.azure import AzureOpenAI, AsyncAzureOpenAI
+
+
+def flatten(t: Iterable[Iterable[_T]]) -> list[_T]:
+    return [item for sublist in t for item in sublist]
+
+
+def extract_files(
+    # TODO: this needs to take Dict but variance issues.....
+    # create protocol type ?
+    query: Mapping[str, object],
+    *,
+    paths: Sequence[Sequence[str]],
+    array_format: ArrayFormat = "brackets",
+) -> list[tuple[str, FileTypes]]:
+    """Recursively extract files from the given dictionary based on specified paths.
+
+    A path may look like this ['foo', 'files', '<array>', 'data'].
+
+    ``array_format`` controls how ``<array>`` segments contribute to the emitted
+    field name. Supported values: ``"brackets"`` (``foo[]``), ``"repeat"`` and
+    ``"comma"`` (``foo``), ``"indices"`` (``foo[0]``, ``foo[1]``).
+
+    Note: this mutates the given dictionary.
+    """
+    files: list[tuple[str, FileTypes]] = []
+    for path in paths:
+        files.extend(_extract_items(query, path, index=0, flattened_key=None, array_format=array_format))
+    return files
+
+
+def _array_suffix(array_format: ArrayFormat, array_index: int) -> str:
+    if array_format == "brackets":
+        return "[]"
+    if array_format == "indices":
+        return f"[{array_index}]"
+    if array_format == "repeat" or array_format == "comma":
+        # Both repeat the bare field name for each file part; there is no
+        # meaningful way to comma-join binary parts.
+        return ""
+    raise NotImplementedError(
+        f"Unknown array_format value: {array_format}, choose from {', '.join(get_args(ArrayFormat))}"
+    )
+
+
+def _extract_items(
+    obj: object,
+    path: Sequence[str],
+    *,
+    index: int,
+    flattened_key: str | None,
+    array_format: ArrayFormat,
+) -> list[tuple[str, FileTypes]]:
+    try:
+        key = path[index]
+    except IndexError:
+        if not is_given(obj):
+            # no value was provided - we can safely ignore
+            return []
+
+        # cyclical import
+        from .._files import assert_is_file_content
+
+        # We have exhausted the path, return the entry we found.
+        assert flattened_key is not None
+
+        if is_list(obj):
+            files: list[tuple[str, FileTypes]] = []
+            for array_index, entry in enumerate(obj):
+                suffix = _array_suffix(array_format, array_index)
+                emitted_key = (flattened_key + suffix) if flattened_key else suffix
+                assert_is_file_content(entry, key=emitted_key)
+                files.append((emitted_key, cast(FileTypes, entry)))
+            return files
+
+        assert_is_file_content(obj, key=flattened_key)
+        return [(flattened_key, cast(FileTypes, obj))]
+
+    index += 1
+    if is_dict(obj):
+        try:
+            # Remove the field if there are no more dict keys in the path,
+            # only "<array>" traversal markers or end.
+            if all(p == "<array>" for p in path[index:]):
+                item = obj.pop(key)
+            else:
+                item = obj[key]
+        except KeyError:
+            # Key was not present in the dictionary, this is not indicative of an error
+            # as the given path may not point to a required field. We also do not want
+            # to enforce required fields as the API may differ from the spec in some cases.
+            return []
+        if flattened_key is None:
+            flattened_key = key
+        else:
+            flattened_key += f"[{key}]"
+        return _extract_items(
+            item,
+            path,
+            index=index,
+            flattened_key=flattened_key,
+            array_format=array_format,
+        )
+    elif is_list(obj):
+        if key != "<array>":
+            return []
+
+        return flatten(
+            [
+                _extract_items(
+                    item,
+                    path,
+                    index=index,
+                    flattened_key=(
+                        (flattened_key if flattened_key is not None else "") + _array_suffix(array_format, array_index)
+                    ),
+                    array_format=array_format,
+                )
+                for array_index, item in enumerate(obj)
+            ]
+        )
+
+    # Something unexpected was passed, just ignore it.
+    return []
+
+
+def is_given(obj: _T | NotGiven | Omit) -> TypeGuard[_T]:
+    return not isinstance(obj, NotGiven) and not isinstance(obj, Omit)
+
+
+# Type safe methods for narrowing types with TypeVars.
+# The default narrowing for isinstance(obj, dict) is dict[unknown, unknown],
+# however this cause Pyright to rightfully report errors. As we know we don't
+# care about the contained types we can safely use `object` in its place.
+#
+# There are two separate functions defined, `is_*` and `is_*_t` for different use cases.
+# `is_*` is for when you're dealing with an unknown input
+# `is_*_t` is for when you're narrowing a known union type to a specific subset
+
+
+def is_tuple(obj: object) -> TypeGuard[tuple[object, ...]]:
+    return isinstance(obj, tuple)
+
+
+def is_tuple_t(obj: _TupleT | object) -> TypeGuard[_TupleT]:
+    return isinstance(obj, tuple)
+
+
+def is_sequence(obj: object) -> TypeGuard[Sequence[object]]:
+    return isinstance(obj, Sequence)
+
+
+def is_sequence_t(obj: _SequenceT | object) -> TypeGuard[_SequenceT]:
+    return isinstance(obj, Sequence)
+
+
+def is_mapping(obj: object) -> TypeGuard[Mapping[str, object]]:
+    return isinstance(obj, Mapping)
+
+
+def is_mapping_t(obj: _MappingT | object) -> TypeGuard[_MappingT]:
+    return isinstance(obj, Mapping)
+
+
+def is_dict(obj: object) -> TypeGuard[dict[object, object]]:
+    return isinstance(obj, dict)
+
+
+def is_list(obj: object) -> TypeGuard[list[object]]:
+    return isinstance(obj, list)
+
+
+def is_iterable(obj: object) -> TypeGuard[Iterable[object]]:
+    return isinstance(obj, Iterable)
+
+
+# copied from https://github.com/Rapptz/RoboDanny
+def human_join(seq: Sequence[str], *, delim: str = ", ", final: str = "or") -> str:
+    size = len(seq)
+    if size == 0:
+        return ""
+
+    if size == 1:
+        return seq[0]
+
+    if size == 2:
+        return f"{seq[0]} {final} {seq[1]}"
+
+    return delim.join(seq[:-1]) + f" {final} {seq[-1]}"
+
+
+def quote(string: str) -> str:
+    """Add single quotation marks around the given string. Does *not* do any escaping."""
+    return f"'{string}'"
+
+
+def required_args(*variants: Sequence[str]) -> Callable[[CallableT], CallableT]:
+    """Decorator to enforce a given set of arguments or variants of arguments are passed to the decorated function.
+
+    Useful for enforcing runtime validation of overloaded functions.
+
+    Example usage:
+    ```py
+    @overload
+    def foo(*, a: str) -> str: ...
+
+
+    @overload
+    def foo(*, b: bool) -> str: ...
+
+
+    # This enforces the same constraints that a static type checker would
+    # i.e. that either a or b must be passed to the function
+    @required_args(["a"], ["b"])
+    def foo(*, a: str | None = None, b: bool | None = None) -> str: ...
+    ```
+    """
+
+    def inner(func: CallableT) -> CallableT:
+        params = inspect.signature(func).parameters
+        positional = [
+            name
+            for name, param in params.items()
+            if param.kind
+            in {
+                param.POSITIONAL_ONLY,
+                param.POSITIONAL_OR_KEYWORD,
+            }
+        ]
+
+        @functools.wraps(func)
+        def wrapper(*args: object, **kwargs: object) -> object:
+            given_params: set[str] = set()
+            for i, _ in enumerate(args):
+                try:
+                    given_params.add(positional[i])
+                except IndexError:
+                    raise TypeError(
+                        f"{func.__name__}() takes {len(positional)} argument(s) but {len(args)} were given"
+                    ) from None
+
+            for key in kwargs.keys():
+                given_params.add(key)
+
+            for variant in variants:
+                matches = all((param in given_params for param in variant))
+                if matches:
+                    break
+            else:  # no break
+                if len(variants) > 1:
+                    variations = human_join(
+                        ["(" + human_join([quote(arg) for arg in variant], final="and") + ")" for variant in variants]
+                    )
+                    msg = f"Missing required arguments; Expected either {variations} arguments to be given"
+                else:
+                    assert len(variants) > 0
+
+                    # TODO: this error message is not deterministic
+                    missing = list(set(variants[0]) - given_params)
+                    if len(missing) > 1:
+                        msg = f"Missing required arguments: {human_join([quote(arg) for arg in missing])}"
+                    else:
+                        msg = f"Missing required argument: {quote(missing[0])}"
+                raise TypeError(msg)
+            return func(*args, **kwargs)
+
+        return wrapper  # type: ignore
+
+    return inner
+
+
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+
+
+@overload
+def strip_not_given(obj: None) -> None: ...
+
+
+@overload
+def strip_not_given(obj: Mapping[_K, _V | NotGiven]) -> dict[_K, _V]: ...
+
+
+@overload
+def strip_not_given(obj: object) -> object: ...
+
+
+def strip_not_given(obj: object | None) -> object:
+    """Remove all top-level keys where their values are instances of `NotGiven`"""
+    if obj is None:
+        return None
+
+    if not is_mapping(obj):
+        return obj
+
+    return {key: value for key, value in obj.items() if not isinstance(value, NotGiven)}
+
+
+def coerce_integer(val: str) -> int:
+    return int(val, base=10)
+
+
+def coerce_float(val: str) -> float:
+    return float(val)
+
+
+def coerce_boolean(val: str) -> bool:
+    return val == "true" or val == "1" or val == "on"
+
+
+def maybe_coerce_integer(val: str | None) -> int | None:
+    if val is None:
+        return None
+    return coerce_integer(val)
+
+
+def maybe_coerce_float(val: str | None) -> float | None:
+    if val is None:
+        return None
+    return coerce_float(val)
+
+
+def maybe_coerce_boolean(val: str | None) -> bool | None:
+    if val is None:
+        return None
+    return coerce_boolean(val)
+
+
+def removeprefix(string: str, prefix: str) -> str:
+    """Remove a prefix from a string.
+
+    Backport of `str.removeprefix` for Python < 3.9
+    """
+    if string.startswith(prefix):
+        return string[len(prefix) :]
+    return string
+
+
+def removesuffix(string: str, suffix: str) -> str:
+    """Remove a suffix from a string.
+
+    Backport of `str.removesuffix` for Python < 3.9
+    """
+    if string.endswith(suffix):
+        return string[: -len(suffix)]
+    return string
+
+
+def file_from_path(path: str) -> FileTypes:
+    contents = Path(path).read_bytes()
+    file_name = os.path.basename(path)
+    return (file_name, contents)
+
+
+def get_required_header(headers: HeadersLike, header: str) -> str:
+    lower_header = header.lower()
+    if is_mapping_t(headers):
+        # mypy doesn't understand the type narrowing here
+        for k, v in headers.items():  # type: ignore
+            if k.lower() == lower_header and isinstance(v, str):
+                return v
+
+    # to deal with the case where the header looks like Stainless-Event-Id
+    intercaps_header = re.sub(r"([^\w])(\w)", lambda pat: pat.group(1) + pat.group(2).upper(), header.capitalize())
+
+    for normalized_header in [header, lower_header, header.upper(), intercaps_header]:
+        value = headers.get(normalized_header)
+        if value:
+            return value
+
+    raise ValueError(f"Could not find {header} header")
+
+
+def get_async_library() -> str:
+    try:
+        return sniffio.current_async_library()
+    except Exception:
+        return "false"
+
+
+def lru_cache(*, maxsize: int | None = 128) -> Callable[[CallableT], CallableT]:
+    """A version of functools.lru_cache that retains the type signature
+    for the wrapped function arguments.
+    """
+    wrapper = functools.lru_cache(  # noqa: TID251
+        maxsize=maxsize,
+    )
+    return cast(Any, wrapper)  # type: ignore[no-any-return]
+
+
+def json_safe(data: object) -> object:
+    """Translates a mapping / sequence recursively in the same fashion
+    as `pydantic` v2's `model_dump(mode="json")`.
+    """
+    if is_mapping(data):
+        return {json_safe(key): json_safe(value) for key, value in data.items()}
+
+    if is_iterable(data) and not isinstance(data, (str, bytes, bytearray)):
+        return [json_safe(item) for item in data]
+
+    if isinstance(data, (datetime, date)):
+        return data.isoformat()
+
+    return data
+
+
+def is_azure_client(client: object) -> TypeGuard[AzureOpenAI]:
+    from ..lib.azure import AzureOpenAI
+
+    return isinstance(client, AzureOpenAI)
+
+
+def is_async_azure_client(client: object) -> TypeGuard[AsyncAzureOpenAI]:
+    from ..lib.azure import AsyncAzureOpenAI
+
+    return isinstance(client, AsyncAzureOpenAI)

.venv311/Lib/site-packages/openai/types/admin/organization/projects/__init__.py

@@ -0,0 +1,34 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .project_user import ProjectUser as ProjectUser
+from .project_group import ProjectGroup as ProjectGroup
+from .project_api_key import ProjectAPIKey as ProjectAPIKey
+from .role_list_params import RoleListParams as RoleListParams
+from .user_list_params import UserListParams as UserListParams
+from .group_list_params import GroupListParams as GroupListParams
+from .project_rate_limit import ProjectRateLimit as ProjectRateLimit
+from .role_create_params import RoleCreateParams as RoleCreateParams
+from .role_update_params import RoleUpdateParams as RoleUpdateParams
+from .user_create_params import UserCreateParams as UserCreateParams
+from .user_update_params import UserUpdateParams as UserUpdateParams
+from .api_key_list_params import APIKeyListParams as APIKeyListParams
+from .group_create_params import GroupCreateParams as GroupCreateParams
+from .role_delete_response import RoleDeleteResponse as RoleDeleteResponse
+from .user_delete_response import UserDeleteResponse as UserDeleteResponse
+from .group_delete_response import GroupDeleteResponse as GroupDeleteResponse
+from .api_key_delete_response import APIKeyDeleteResponse as APIKeyDeleteResponse
+from .certificate_list_params import CertificateListParams as CertificateListParams
+from .project_service_account import ProjectServiceAccount as ProjectServiceAccount
+from .certificate_list_response import CertificateListResponse as CertificateListResponse
+from .certificate_activate_params import CertificateActivateParams as CertificateActivateParams
+from .service_account_list_params import ServiceAccountListParams as ServiceAccountListParams
+from .certificate_activate_response import CertificateActivateResponse as CertificateActivateResponse
+from .certificate_deactivate_params import CertificateDeactivateParams as CertificateDeactivateParams
+from .service_account_create_params import ServiceAccountCreateParams as ServiceAccountCreateParams
+from .certificate_deactivate_response import CertificateDeactivateResponse as CertificateDeactivateResponse
+from .service_account_create_response import ServiceAccountCreateResponse as ServiceAccountCreateResponse
+from .service_account_delete_response import ServiceAccountDeleteResponse as ServiceAccountDeleteResponse
+from .rate_limit_list_rate_limits_params import RateLimitListRateLimitsParams as RateLimitListRateLimitsParams
+from .rate_limit_update_rate_limit_params import RateLimitUpdateRateLimitParams as RateLimitUpdateRateLimitParams

.venv311/Lib/site-packages/openai/types/admin/organization/projects/groups/__init__.py

@@ -0,0 +1,9 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .role_list_params import RoleListParams as RoleListParams
+from .role_create_params import RoleCreateParams as RoleCreateParams
+from .role_list_response import RoleListResponse as RoleListResponse
+from .role_create_response import RoleCreateResponse as RoleCreateResponse
+from .role_delete_response import RoleDeleteResponse as RoleDeleteResponse

.venv311/Lib/site-packages/openai/types/admin/organization/projects/groups/role_create_params.py

@@ -0,0 +1,14 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import Required, TypedDict
+
+__all__ = ["RoleCreateParams"]
+
+
+class RoleCreateParams(TypedDict, total=False):
+    project_id: Required[str]
+
+    role_id: Required[str]
+    """Identifier of the role to assign."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/groups/role_create_response.py

@@ -0,0 +1,40 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal
+
+from ...role import Role
+from ......_models import BaseModel
+
+__all__ = ["RoleCreateResponse", "Group"]
+
+
+class Group(BaseModel):
+    """Summary information about a group returned in role assignment responses."""
+
+    id: str
+    """Identifier for the group."""
+
+    created_at: int
+    """Unix timestamp (in seconds) when the group was created."""
+
+    name: str
+    """Display name of the group."""
+
+    object: Literal["group"]
+    """Always `group`."""
+
+    scim_managed: bool
+    """Whether the group is managed through SCIM."""
+
+
+class RoleCreateResponse(BaseModel):
+    """Role assignment linking a group to a role."""
+
+    group: Group
+    """Summary information about a group returned in role assignment responses."""
+
+    object: Literal["group.role"]
+    """Always `group.role`."""
+
+    role: Role
+    """Details about a role that can be assigned through the public Roles API."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/groups/role_delete_response.py

@@ -0,0 +1,18 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from ......_models import BaseModel
+
+__all__ = ["RoleDeleteResponse"]
+
+
+class RoleDeleteResponse(BaseModel):
+    """Confirmation payload returned after unassigning a role."""
+
+    deleted: bool
+    """Whether the assignment was removed."""
+
+    object: str
+    """
+    Identifier for the deleted assignment, such as `group.role.deleted` or
+    `user.role.deleted`.
+    """

.venv311/Lib/site-packages/openai/types/admin/organization/projects/groups/role_list_params.py

@@ -0,0 +1,24 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import Literal, Required, TypedDict
+
+__all__ = ["RoleListParams"]
+
+
+class RoleListParams(TypedDict, total=False):
+    project_id: Required[str]
+
+    after: str
+    """Cursor for pagination.
+
+    Provide the value from the previous response's `next` field to continue listing
+    project roles.
+    """
+
+    limit: int
+    """A limit on the number of project role assignments to return."""
+
+    order: Literal["asc", "desc"]
+    """Sort order for the returned project roles."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/groups/role_list_response.py

@@ -0,0 +1,46 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Dict, List, Optional
+
+from ......_models import BaseModel
+
+__all__ = ["RoleListResponse"]
+
+
+class RoleListResponse(BaseModel):
+    """
+    Detailed information about a role assignment entry returned when listing assignments.
+    """
+
+    id: str
+    """Identifier for the role."""
+
+    created_at: Optional[int] = None
+    """When the role was created."""
+
+    created_by: Optional[str] = None
+    """Identifier of the actor who created the role."""
+
+    created_by_user_obj: Optional[Dict[str, object]] = None
+    """User details for the actor that created the role, when available."""
+
+    description: Optional[str] = None
+    """Description of the role."""
+
+    metadata: Optional[Dict[str, object]] = None
+    """Arbitrary metadata stored on the role."""
+
+    name: str
+    """Name of the role."""
+
+    permissions: List[str]
+    """Permissions associated with the role."""
+
+    predefined_role: bool
+    """Whether the role is predefined by OpenAI."""
+
+    resource_type: str
+    """Resource type the role applies to."""
+
+    updated_at: Optional[int] = None
+    """When the role was last updated."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/user_delete_response.py

@@ -0,0 +1,15 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal
+
+from ....._models import BaseModel
+
+__all__ = ["UserDeleteResponse"]
+
+
+class UserDeleteResponse(BaseModel):
+    id: str
+
+    deleted: bool
+
+    object: Literal["organization.project.user.deleted"]

.venv311/Lib/site-packages/openai/types/admin/organization/projects/user_update_params.py

@@ -0,0 +1,15 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import Optional
+from typing_extensions import Required, TypedDict
+
+__all__ = ["UserUpdateParams"]
+
+
+class UserUpdateParams(TypedDict, total=False):
+    project_id: Required[str]
+
+    role: Optional[str]
+    """`owner` or `member`"""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/users/__init__.py

@@ -0,0 +1,9 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .role_list_params import RoleListParams as RoleListParams
+from .role_create_params import RoleCreateParams as RoleCreateParams
+from .role_list_response import RoleListResponse as RoleListResponse
+from .role_create_response import RoleCreateResponse as RoleCreateResponse
+from .role_delete_response import RoleDeleteResponse as RoleDeleteResponse

.venv311/Lib/site-packages/openai/types/admin/organization/projects/users/role_create_params.py

@@ -0,0 +1,14 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import Required, TypedDict
+
+__all__ = ["RoleCreateParams"]
+
+
+class RoleCreateParams(TypedDict, total=False):
+    project_id: Required[str]
+
+    role_id: Required[str]
+    """Identifier of the role to assign."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/users/role_create_response.py

@@ -0,0 +1,22 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal
+
+from ...role import Role
+from ......_models import BaseModel
+from ...organization_user import OrganizationUser
+
+__all__ = ["RoleCreateResponse"]
+
+
+class RoleCreateResponse(BaseModel):
+    """Role assignment linking a user to a role."""
+
+    object: Literal["user.role"]
+    """Always `user.role`."""
+
+    role: Role
+    """Details about a role that can be assigned through the public Roles API."""
+
+    user: OrganizationUser
+    """Represents an individual `user` within an organization."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/users/role_delete_response.py

@@ -0,0 +1,18 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from ......_models import BaseModel
+
+__all__ = ["RoleDeleteResponse"]
+
+
+class RoleDeleteResponse(BaseModel):
+    """Confirmation payload returned after unassigning a role."""
+
+    deleted: bool
+    """Whether the assignment was removed."""
+
+    object: str
+    """
+    Identifier for the deleted assignment, such as `group.role.deleted` or
+    `user.role.deleted`.
+    """

.venv311/Lib/site-packages/openai/types/admin/organization/projects/users/role_list_params.py

@@ -0,0 +1,24 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import Literal, Required, TypedDict
+
+__all__ = ["RoleListParams"]
+
+
+class RoleListParams(TypedDict, total=False):
+    project_id: Required[str]
+
+    after: str
+    """Cursor for pagination.
+
+    Provide the value from the previous response's `next` field to continue listing
+    project roles.
+    """
+
+    limit: int
+    """A limit on the number of project role assignments to return."""
+
+    order: Literal["asc", "desc"]
+    """Sort order for the returned project roles."""

.venv311/Lib/site-packages/openai/types/admin/organization/projects/users/role_list_response.py

@@ -0,0 +1,46 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Dict, List, Optional
+
+from ......_models import BaseModel
+
+__all__ = ["RoleListResponse"]
+
+
+class RoleListResponse(BaseModel):
+    """
+    Detailed information about a role assignment entry returned when listing assignments.
+    """
+
+    id: str
+    """Identifier for the role."""
+
+    created_at: Optional[int] = None
+    """When the role was created."""
+
+    created_by: Optional[str] = None
+    """Identifier of the actor who created the role."""
+
+    created_by_user_obj: Optional[Dict[str, object]] = None
+    """User details for the actor that created the role, when available."""
+
+    description: Optional[str] = None
+    """Description of the role."""
+
+    metadata: Optional[Dict[str, object]] = None
+    """Arbitrary metadata stored on the role."""
+
+    name: str
+    """Name of the role."""
+
+    permissions: List[str]
+    """Permissions associated with the role."""
+
+    predefined_role: bool
+    """Whether the role is predefined by OpenAI."""
+
+    resource_type: str
+    """Resource type the role applies to."""
+
+    updated_at: Optional[int] = None
+    """When the role was last updated."""

.venv311/Lib/site-packages/openai/types/admin/organization/users/__init__.py

@@ -0,0 +1,9 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .role_list_params import RoleListParams as RoleListParams
+from .role_create_params import RoleCreateParams as RoleCreateParams
+from .role_list_response import RoleListResponse as RoleListResponse
+from .role_create_response import RoleCreateResponse as RoleCreateResponse
+from .role_delete_response import RoleDeleteResponse as RoleDeleteResponse

.venv311/Lib/site-packages/openai/types/admin/organization/users/role_create_params.py

@@ -0,0 +1,12 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import Required, TypedDict
+
+__all__ = ["RoleCreateParams"]
+
+
+class RoleCreateParams(TypedDict, total=False):
+    role_id: Required[str]
+    """Identifier of the role to assign."""

.venv311/Lib/site-packages/openai/types/admin/organization/users/role_create_response.py

@@ -0,0 +1,22 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal
+
+from ..role import Role
+from ....._models import BaseModel
+from ..organization_user import OrganizationUser
+
+__all__ = ["RoleCreateResponse"]
+
+
+class RoleCreateResponse(BaseModel):
+    """Role assignment linking a user to a role."""
+
+    object: Literal["user.role"]
+    """Always `user.role`."""
+
+    role: Role
+    """Details about a role that can be assigned through the public Roles API."""
+
+    user: OrganizationUser
+    """Represents an individual `user` within an organization."""

.venv311/Lib/site-packages/openai/types/admin/organization/users/role_delete_response.py

@@ -0,0 +1,18 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from ....._models import BaseModel
+
+__all__ = ["RoleDeleteResponse"]
+
+
+class RoleDeleteResponse(BaseModel):
+    """Confirmation payload returned after unassigning a role."""
+
+    deleted: bool
+    """Whether the assignment was removed."""
+
+    object: str
+    """
+    Identifier for the deleted assignment, such as `group.role.deleted` or
+    `user.role.deleted`.
+    """

.venv311/Lib/site-packages/openai/types/admin/organization/users/role_list_params.py

@@ -0,0 +1,22 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import Literal, TypedDict
+
+__all__ = ["RoleListParams"]
+
+
+class RoleListParams(TypedDict, total=False):
+    after: str
+    """Cursor for pagination.
+
+    Provide the value from the previous response's `next` field to continue listing
+    organization roles.
+    """
+
+    limit: int
+    """A limit on the number of organization role assignments to return."""
+
+    order: Literal["asc", "desc"]
+    """Sort order for the returned organization roles."""

.venv311/Lib/site-packages/openai/types/admin/organization/users/role_list_response.py

@@ -0,0 +1,46 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Dict, List, Optional
+
+from ....._models import BaseModel
+
+__all__ = ["RoleListResponse"]
+
+
+class RoleListResponse(BaseModel):
+    """
+    Detailed information about a role assignment entry returned when listing assignments.
+    """
+
+    id: str
+    """Identifier for the role."""
+
+    created_at: Optional[int] = None
+    """When the role was created."""
+
+    created_by: Optional[str] = None
+    """Identifier of the actor who created the role."""
+
+    created_by_user_obj: Optional[Dict[str, object]] = None
+    """User details for the actor that created the role, when available."""
+
+    description: Optional[str] = None
+    """Description of the role."""
+
+    metadata: Optional[Dict[str, object]] = None
+    """Arbitrary metadata stored on the role."""
+
+    name: str
+    """Name of the role."""
+
+    permissions: List[str]
+    """Permissions associated with the role."""
+
+    predefined_role: bool
+    """Whether the role is predefined by OpenAI."""
+
+    resource_type: str
+    """Resource type the role applies to."""
+
+    updated_at: Optional[int] = None
+    """When the role was last updated."""

.venv311/Lib/site-packages/openai/types/audio/__init__.py

@@ -0,0 +1,23 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .translation import Translation as Translation
+from .speech_model import SpeechModel as SpeechModel
+from .transcription import Transcription as Transcription
+from .transcription_word import TranscriptionWord as TranscriptionWord
+from .translation_verbose import TranslationVerbose as TranslationVerbose
+from .speech_create_params import SpeechCreateParams as SpeechCreateParams
+from .transcription_include import TranscriptionInclude as TranscriptionInclude
+from .transcription_segment import TranscriptionSegment as TranscriptionSegment
+from .transcription_verbose import TranscriptionVerbose as TranscriptionVerbose
+from .transcription_diarized import TranscriptionDiarized as TranscriptionDiarized
+from .translation_create_params import TranslationCreateParams as TranslationCreateParams
+from .transcription_stream_event import TranscriptionStreamEvent as TranscriptionStreamEvent
+from .transcription_create_params import TranscriptionCreateParams as TranscriptionCreateParams
+from .translation_create_response import TranslationCreateResponse as TranslationCreateResponse
+from .transcription_create_response import TranscriptionCreateResponse as TranscriptionCreateResponse
+from .transcription_text_done_event import TranscriptionTextDoneEvent as TranscriptionTextDoneEvent
+from .transcription_diarized_segment import TranscriptionDiarizedSegment as TranscriptionDiarizedSegment
+from .transcription_text_delta_event import TranscriptionTextDeltaEvent as TranscriptionTextDeltaEvent
+from .transcription_text_segment_event import TranscriptionTextSegmentEvent as TranscriptionTextSegmentEvent

.venv311/Lib/site-packages/openai/types/audio/speech_create_params.py

@@ -0,0 +1,68 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import Union
+from typing_extensions import Literal, Required, TypeAlias, TypedDict
+
+from .speech_model import SpeechModel
+
+__all__ = ["SpeechCreateParams", "Voice", "VoiceID"]
+
+
+class SpeechCreateParams(TypedDict, total=False):
+    input: Required[str]
+    """The text to generate audio for. The maximum length is 4096 characters."""
+
+    model: Required[Union[str, SpeechModel]]
+    """
+    One of the available [TTS models](https://platform.openai.com/docs/models#tts):
+    `tts-1`, `tts-1-hd`, `gpt-4o-mini-tts`, or `gpt-4o-mini-tts-2025-12-15`.
+    """
+
+    voice: Required[Voice]
+    """The voice to use when generating the audio.
+
+    Supported built-in voices are `alloy`, `ash`, `ballad`, `coral`, `echo`,
+    `fable`, `onyx`, `nova`, `sage`, `shimmer`, `verse`, `marin`, and `cedar`. You
+    may also provide a custom voice object with an `id`, for example
+    `{ "id": "voice_1234" }`. Previews of the voices are available in the
+    [Text to speech guide](https://platform.openai.com/docs/guides/text-to-speech#voice-options).
+    """
+
+    instructions: str
+    """Control the voice of your generated audio with additional instructions.
+
+    Does not work with `tts-1` or `tts-1-hd`.
+    """
+
+    response_format: Literal["mp3", "opus", "aac", "flac", "wav", "pcm"]
+    """The format to audio in.
+
+    Supported formats are `mp3`, `opus`, `aac`, `flac`, `wav`, and `pcm`.
+    """
+
+    speed: float
+    """The speed of the generated audio.
+
+    Select a value from `0.25` to `4.0`. `1.0` is the default.
+    """
+
+    stream_format: Literal["sse", "audio"]
+    """The format to stream the audio in.
+
+    Supported formats are `sse` and `audio`. `sse` is not supported for `tts-1` or
+    `tts-1-hd`.
+    """
+
+
+class VoiceID(TypedDict, total=False):
+    """Custom voice reference."""
+
+    id: Required[str]
+    """The custom voice ID, e.g. `voice_1234`."""
+
+
+Voice: TypeAlias = Union[
+    str, Literal["alloy", "ash", "ballad", "coral", "echo", "sage", "shimmer", "verse", "marin", "cedar"], VoiceID
+]

.venv311/Lib/site-packages/openai/types/audio/speech_model.py

@@ -0,0 +1,7 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal, TypeAlias
+
+__all__ = ["SpeechModel"]
+
+SpeechModel: TypeAlias = Literal["tts-1", "tts-1-hd", "gpt-4o-mini-tts", "gpt-4o-mini-tts-2025-12-15"]

.venv311/Lib/site-packages/openai/types/audio/transcription.py

@@ -0,0 +1,81 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import List, Union, Optional
+from typing_extensions import Literal, Annotated, TypeAlias
+
+from ..._utils import PropertyInfo
+from ..._models import BaseModel
+
+__all__ = ["Transcription", "Logprob", "Usage", "UsageTokens", "UsageTokensInputTokenDetails", "UsageDuration"]
+
+
+class Logprob(BaseModel):
+    token: Optional[str] = None
+    """The token in the transcription."""
+
+    bytes: Optional[List[float]] = None
+    """The bytes of the token."""
+
+    logprob: Optional[float] = None
+    """The log probability of the token."""
+
+
+class UsageTokensInputTokenDetails(BaseModel):
+    """Details about the input tokens billed for this request."""
+
+    audio_tokens: Optional[int] = None
+    """Number of audio tokens billed for this request."""
+
+    text_tokens: Optional[int] = None
+    """Number of text tokens billed for this request."""
+
+
+class UsageTokens(BaseModel):
+    """Usage statistics for models billed by token usage."""
+
+    input_tokens: int
+    """Number of input tokens billed for this request."""
+
+    output_tokens: int
+    """Number of output tokens generated."""
+
+    total_tokens: int
+    """Total number of tokens used (input + output)."""
+
+    type: Literal["tokens"]
+    """The type of the usage object. Always `tokens` for this variant."""
+
+    input_token_details: Optional[UsageTokensInputTokenDetails] = None
+    """Details about the input tokens billed for this request."""
+
+
+class UsageDuration(BaseModel):
+    """Usage statistics for models billed by audio input duration."""
+
+    seconds: float
+    """Duration of the input audio in seconds."""
+
+    type: Literal["duration"]
+    """The type of the usage object. Always `duration` for this variant."""
+
+
+Usage: TypeAlias = Annotated[Union[UsageTokens, UsageDuration], PropertyInfo(discriminator="type")]
+
+
+class Transcription(BaseModel):
+    """
+    Represents a transcription response returned by model, based on the provided input.
+    """
+
+    text: str
+    """The transcribed text."""
+
+    logprobs: Optional[List[Logprob]] = None
+    """The log probabilities of the tokens in the transcription.
+
+    Only returned with the models `gpt-4o-transcribe` and `gpt-4o-mini-transcribe`
+    if `logprobs` is added to the `include` array.
+    """
+
+    usage: Optional[Usage] = None
+    """Token usage statistics for the request."""