"""
Model Pipeline Architecture Diagram
Shows complete flow from model loading through execution
"""
import json
from typing import Dict, Any
from .pipeline_components import RemoteStoragePipeline, WeightLoadingPipeline, ArchitectureLoadingPipeline
from ..virtual_gpu_device import VGPUMemoryManager, ComputePipeline, MemoryPipeline, StreamPipeline, ExecutionEngine

MODEL_PIPELINE_DIAGRAM = """
+-------------------------------------------------------------------------------------------+
|                                     Model Loading Pipeline                                  |
+-------------------------------------------------------------------------------------------+

[User Input] -> [Tokenization/Preprocessing]
                         |
                         v
+------------------+  +------------------------+  +-------------------------+
|   Local Storage  |  |     Model Weights     |  |    Model Architecture  |
|   Module         |  |     Loading Module    |  |    Definition          |
| +--------------+ |  | +------------------+  |  | +-------------------+  |
| |  HTTP Store  | |  | | Weight Matrices  |  |  | | Layer Configs    |  |
| |  Cache Layer | |  | | Bias Vectors     |  |  | | Activation Maps  |  |
| |  Persistence | |  | | Layer Parameters |  |  | | Network Graph    |  |
| +--------------+ |  | +------------------+  |  | +-------------------+  |
+--------+--------+  +-----------+----------+  +-----------+-----------+
         |                       |                         |
         +-------------------+   |   +-------------------+
                             |   |   |
                             v   v   v
+-------------------------------------------------------------------------+
|                        VGPU Memory Manager                               |
| +---------------------+  +-------------------+  +--------------------+    |
| | Tensor Allocation   |  | Memory Pools     |  | Stream Management  |    |
| | Shape Management    |  | Block Allocation  |  | Sync Primitives   |    |
| +---------------------+  +-------------------+  +--------------------+    |
+-------------------------^--------+------------------------^--------------+
                         |        |                        |
                         v        v                        |
+----------------------+ +-------------------------+ +------------------+
|   Compute Pipeline   | |    Memory Pipeline     | |  Stream Pipeline |
| +-----------------+ | | +-------------------+   | | +--------------+ |
| | Matrix Multiply  | | | | Data Movement    |   | | | Scheduling   | |
| | Tensor Ops      | | | | Caching          |   | | | Queuing      | |
| | Activations     | | | | Prefetching      |   | | | Syncing      | |
| +-----------------+ | | +-------------------+   | | +--------------+ |
+--------+----------+ +------------+-------------+ +--------+---------+
         |                         |                        |
         +-------------------------+------------------------+
                                  |
                                  v
+-------------------------------------------------------------------------------------------+
|                              Execution Engine                                               |
| +------------------+  +---------------------+  +------------------+  +------------------+   |
| | Tensor Core      |  | Shader Processors   |  | Memory Controls  |  | Output Buffers   |   |
| | Matrix Engine    |  | SIMD Units          |  | Cache Controls   |  | Display Pipeline |   |
| +------------------+  +---------------------+  +------------------+  +------------------+   |
+-------------------------------------------------------------------------------------------+
                                  |
                                  v
                        [Output Visualization]

Flow Description:
1. Model Loading:
   - Architecture definition loaded from config
   - Weights loaded from storage
   - Memory pre-allocated in VGPU

2. Input Processing:
   - User input tokenized/preprocessed
   - Converted to tensor format
   - Loaded into VGPU memory

3. Memory Management:
   - Tensor blocks allocated
   - Memory pools organized
   - Stream synchronization setup

4. Pipeline Coordination:
   - Compute pipeline handles math ops
   - Memory pipeline manages data flow
   - Stream pipeline handles scheduling

5. Execution:
   - Tensor core processes matrix ops
   - Shader units handle activations
   - Memory controller manages data movement

6. Output:
   - Results collected in output buffers
   - Processed for visualization
   - Displayed through graphics pipeline
"""

def print_pipeline_diagram():
    """Print the complete pipeline architecture diagram"""
    print(MODEL_PIPELINE_DIAGRAM)

# Implementation of actual pipeline components
class ModelPipeline:
    """
    Coordinates all pipeline components for model loading and execution
    """
    def __init__(self):
        self.remote_storage = None
        self.weight_loader = None
        self.arch_loader = None
        self.memory_manager = None
        self.compute_pipeline = None
        self.memory_pipeline = None
        self.stream_pipeline = None
        self.execution_engine = None
        
    def initialize_pipeline(self):
        """Set up all pipeline components"""
        # Initialize storage systems
        self.remote_storage = RemoteStoragePipeline()  # Using remote storage instead of local
        self.weight_loader = WeightLoadingPipeline()
        self.arch_loader = ArchitectureLoadingPipeline()
        
        # Initialize VGPU components
        self.memory_manager = VGPUMemoryManager()
        self.compute_pipeline = ComputePipeline()
        self.memory_pipeline = MemoryPipeline()
        self.stream_pipeline = StreamPipeline()
        
        # Initialize execution engine
        self.execution_engine = ExecutionEngine()
        
    def load_model(self, model_path: str):
        """
        Load model through the pipeline
        
        Flow:
        1. Load architecture definition
        2. Initialize memory pools
        3. Load weights
        4. Set up compute streams
        5. Initialize execution engine
        """
        # Load model architecture
        arch_config = self.arch_loader.load_architecture(model_path)
        
        # Pre-allocate memory pools
        self.memory_manager.initialize_pools(arch_config)
        
        # Load weights into VGPU memory
        weight_addresses = self.weight_loader.load_weights(
            model_path,
            self.memory_manager
        )
        
        # Set up compute streams for model layers
        stream_config = self.stream_pipeline.configure_streams(arch_config)
        
        # Initialize execution engine with model configuration
        self.execution_engine.initialize(
            arch_config,
            weight_addresses,
            stream_config
        )
        
    def run_inference(self, input_data):
        """
        Run model inference through the pipeline
        
        Flow:
        1. Preprocess input
        2. Allocate input tensors
        3. Execute through pipeline
        4. Collect and format output
        """
        # Preprocess input data
        processed_input = self.preprocess_input(input_data)
        
        # Allocate input tensors
        input_address = self.memory_manager.allocate_input_tensor(processed_input)
        
        # Execute through pipeline
        output_address = self.execution_engine.run_inference(
            input_address,
            self.stream_pipeline
        )
        
        # Post-process and return output
        return self.postprocess_output(output_address)
        
    def preprocess_input(self, input_data):
        """
        Convert various input types to VGPU-compatible tensor format
        Supports: text, images, audio, video, and numerical data
        """
        from typing import Union, Dict, Any
        import array
        import json
        from pathlib import Path
        
        # Determine input type and format
        input_format = self._detect_input_format(input_data)
        
        if input_format == 'text':
            # Text processing
            if isinstance(input_data, str):
                # Convert to token IDs using tokenizer
                tokens = self.tokenizer.encode(input_data)
                # Create tensor array
                tensor_data = array.array('l', tokens)
                shape = (1, len(tokens))  # Batch size 1
                dtype = 'int32'
                
        elif input_format == 'image':
            # Image processing
            # Support both file paths and raw image data
            if isinstance(input_data, (str, Path)):
                from PIL import Image
                img = Image.open(input_data)
            else:
                img = input_data
                
            # Convert to RGB if needed
            if img.mode != 'RGB':
                img = img.convert('RGB')
                
            # Normalize and convert to tensor format
            width, height = self.arch_config['input_shape'][1:3]
            img = img.resize((width, height))
            tensor_data = array.array('f')
            
            # Normalize pixel values to [-1, 1]
            for pixel in img.getdata():
                tensor_data.extend([p/127.5 - 1 for p in pixel])
            shape = (1, height, width, 3)  # NHWC format
            dtype = 'float32'
            
        elif input_format == 'audio':
            # Audio processing
            import wave
            import audioop
            
            if isinstance(input_data, (str, Path)):
                with wave.open(str(input_data), 'rb') as wav:
                    # Get audio properties
                    n_channels = wav.getnchannels()
                    sampwidth = wav.getsampwidth()
                    framerate = wav.getframerate()
                    n_frames = wav.getnframes()
                    
                    # Read and normalize audio data
                    audio_data = wav.readframes(n_frames)
                    
                    # Convert to mono if stereo
                    if n_channels == 2:
                        audio_data = audioop.tomono(audio_data, sampwidth, 0.5, 0.5)
                    
                    # Convert to float32 and normalize
                    tensor_data = array.array('f')
                    for i in range(0, len(audio_data), sampwidth):
                        sample = int.from_bytes(audio_data[i:i+sampwidth], 'little', signed=True)
                        tensor_data.append(sample / (2**(8*sampwidth-1)))
                        
            shape = (1, len(tensor_data))  # Batch size 1, time series
            dtype = 'float32'
            
        elif input_format == 'video':
            # Video processing
            import cv2
            
            if isinstance(input_data, (str, Path)):
                cap = cv2.VideoCapture(str(input_data))
                
            frames = []
            target_frames = self.arch_config['input_shape'][1]  # Time dimension
            
            while len(frames) < target_frames:
                ret, frame = cap.read()
                if not ret:
                    break
                    
                # Resize and normalize frame
                width, height = self.arch_config['input_shape'][2:4]
                frame = cv2.resize(frame, (width, height))
                frame = frame.astype('float32') / 127.5 - 1
                frames.append(frame)
                
            tensor_data = array.array('f')
            for frame in frames:
                tensor_data.extend(frame.flatten())
                
            shape = (1, len(frames), height, width, 3)  # NTHWC format
            dtype = 'float32'
            
        else:  # Numerical data
            # Handle numerical arrays
            tensor_data = array.array('f', input_data)
            shape = (1, len(input_data))
            dtype = 'float32'
            
        # Allocate and transfer to VGPU memory
        tensor_addr = self.memory_manager.allocate_tensor(shape, dtype)
        self.memory_manager.write_tensor(tensor_addr, tensor_data)
        
        return tensor_addr
        
    def postprocess_output(self, output_address):
        """
        Convert VGPU tensor output to user-friendly format
        Supports: text generation, image generation, audio synthesis, 
        video frames, and numerical outputs
        """
        # Get tensor metadata
        tensor_meta = self.memory_manager.get_tensor_metadata(output_address)
        output_format = self.arch_config['output_format']
        
        # Read raw tensor data
        tensor_data = self.memory_manager.read_tensor(output_address)
        
        if output_format == 'text':
            # Convert logits to token IDs
            if tensor_meta['dtype'] == 'float32':
                # Apply softmax and get token IDs
                logits = array.array('f', tensor_data)
                token_ids = self._softmax_to_tokens(logits)
                # Decode tokens to text
                return self.tokenizer.decode(token_ids)
                
        elif output_format == 'image':
            # Convert tensor to image
            import numpy as np
            from PIL import Image
            
            # Reshape and denormalize
            height, width = tensor_meta['shape'][1:3]
            pixels = array.array('B')  # uint8 array
            
            # Convert normalized values back to [0, 255]
            for value in tensor_data:
                pixels.append(int((value + 1) * 127.5))
                
            # Create image from array
            img_array = np.array(pixels, dtype=np.uint8)
            img_array = img_array.reshape(height, width, 3)
            return Image.fromarray(img_array)
            
        elif output_format == 'audio':
            # Convert tensor to audio waveform
            import wave
            import struct
            
            # Denormalize and convert to int16
            audio_data = bytes()
            for value in tensor_data:
                # Scale to int16 range
                sample = int(value * 32767)
                audio_data += struct.pack('h', sample)
                
            # Create WAV file
            with wave.open('output.wav', 'wb') as wav:
                wav.setnchannels(1)  # Mono
                wav.setsampwidth(2)  # 16-bit
                wav.setframerate(44100)  # Standard sample rate
                wav.writeframes(audio_data)
                
            return 'output.wav'
            
        elif output_format == 'video':
            # Convert tensor to video frames
            import cv2
            import numpy as np
            
            n_frames, height, width = tensor_meta['shape'][1:4]
            
            # Create video writer
            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
            out = cv2.VideoWriter('output.mp4', fourcc, 30.0, (width, height))
            
            # Process each frame
            frame_size = height * width * 3
            for i in range(n_frames):
                frame_data = tensor_data[i*frame_size:(i+1)*frame_size]
                
                # Denormalize and reshape
                frame = np.array(frame_data, dtype=np.float32)
                frame = (frame + 1) * 127.5
                frame = frame.reshape(height, width, 3).astype(np.uint8)
                
                out.write(frame)
                
            out.release()
            return 'output.mp4'
            
        else:  # Numerical output
            # Return raw numerical data
            return array.array('f', tensor_data)
            
    def _detect_input_format(self, input_data):
        """Detect input data format"""
        if isinstance(input_data, str):
            if any(input_data.lower().endswith(ext) for ext in ['.jpg', '.png', '.bmp']):
                return 'image'
            elif any(input_data.lower().endswith(ext) for ext in ['.wav', '.mp3']):
                return 'audio'
            elif any(input_data.lower().endswith(ext) for ext in ['.mp4', '.avi']):
                return 'video'
            return 'text'
        elif hasattr(input_data, 'mode'):  # PIL Image
            return 'image'
        return 'numerical'
        
    def _softmax_to_tokens(self, logits):
        """Convert softmax logits to token IDs"""
        # Basic argmax implementation
        chunk_size = self.arch_config['vocab_size']
        token_ids = []
        
        for i in range(0, len(logits), chunk_size):
            chunk = logits[i:i+chunk_size]
            token_ids.append(chunk.index(max(chunk)))
            
        return token_ids
