"""
SHA-256 Matrix Operations for GPU Mining
Optimizes SHA-256 algorithm using matrix operations for tensor cores
"""

import numpy as np
from typing import Tuple

class SHA256MatrixOps:
    def __init__(self):
        # SHA-256 initial hash values as matrix
        self.H0 = np.array([
            0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a,
            0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19
        ], dtype=np.uint32)

        # SHA-256 round constants as matrix
        self.K = np.array([
            0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5,
            0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5,
            # ... (more constants)
        ], dtype=np.uint32)

    def message_schedule_matrix(self, block_matrix: np.ndarray) -> np.ndarray:
        """
        Convert message block into message schedule matrix W
        Input shape: (batch_size, 16) - 16 words per block
        Output shape: (batch_size, 64) - 64 words in schedule
        """
        batch_size = block_matrix.shape[0]
        W = np.zeros((batch_size, 64), dtype=np.uint32)
        
        # First 16 words are from the input block
        W[:, :16] = block_matrix

        # Generate remaining 48 words using matrix operations
        for i in range(16, 64):
            s0 = (np.right_shift(W[:, i-15], 7) ^ 
                  np.right_shift(W[:, i-15], 18) ^ 
                  np.right_shift(W[:, i-15], 3))
            
            s1 = (np.right_shift(W[:, i-2], 17) ^ 
                  np.right_shift(W[:, i-2], 19) ^ 
                  np.right_shift(W[:, i-2], 10))
            
            W[:, i] = W[:, i-16] + s0 + W[:, i-7] + s1

        return W

    def compression_matrix(self, schedule: np.ndarray, state: np.ndarray) -> np.ndarray:
        """
        Perform compression function using matrix operations
        schedule shape: (batch_size, 64)
        state shape: (batch_size, 8)
        """
        batch_size = schedule.shape[0]
        
        # Initialize working variables matrix
        # Each row contains [a,b,c,d,e,f,g,h]
        work = np.zeros((batch_size, 8), dtype=np.uint32)
        work[:] = state  # Broadcast state to all batches

        # Matrix operations for each round
        for i in range(64):
            # Matrix operations for Ch(e,f,g)
            Ch = (work[:, 4] & work[:, 5]) ^ (~work[:, 4] & work[:, 6])
            
            # Matrix operations for Maj(a,b,c)
            Maj = (work[:, 0] & work[:, 1]) ^ (work[:, 0] & work[:, 2]) ^ (work[:, 1] & work[:, 2])
            
            # Matrix operations for Σ0(a) and Σ1(e)
            S0 = (np.right_shift(work[:, 0], 2) ^ 
                  np.right_shift(work[:, 0], 13) ^ 
                  np.right_shift(work[:, 0], 22))
            
            S1 = (np.right_shift(work[:, 4], 6) ^ 
                  np.right_shift(work[:, 4], 11) ^ 
                  np.right_shift(work[:, 4], 25))

            # Temporary matrix operations
            temp1 = work[:, 7] + S1 + Ch + self.K[i] + schedule[:, i]
            temp2 = S0 + Maj

            # Update working variables matrix
            work[:, 7] = work[:, 6]
            work[:, 6] = work[:, 5]
            work[:, 5] = work[:, 4]
            work[:, 4] = work[:, 3] + temp1
            work[:, 3] = work[:, 2]
            work[:, 2] = work[:, 1]
            work[:, 1] = work[:, 0]
            work[:, 0] = temp1 + temp2

        return work

    def process_block_matrix(self, block_matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        """
        Process multiple blocks in parallel using matrix operations
        Returns: (final_state, intermediate_states)
        """
        batch_size = block_matrix.shape[0]
        
        # Initialize state matrix with H0 for each batch
        state = np.tile(self.H0, (batch_size, 1))
        
        # Generate message schedule matrix
        schedule = self.message_schedule_matrix(block_matrix)
        
        # Perform compression rounds
        final_state = self.compression_matrix(schedule, state)
        
        # Add original state (matrix addition)
        final_state += state
        
        return final_state, schedule

    def parallel_nonce_search(self, 
                            header_matrix: np.ndarray,
                            target: np.ndarray,
                            nonce_range: Tuple[int, int]) -> Tuple[int, np.ndarray]:
        """
        Search for valid nonces in parallel using matrix operations
        Returns: (valid_nonce, hash_matrix)
        """
        batch_size = header_matrix.shape[0]
        
        # Create nonce matrix
        nonce_start, nonce_end = nonce_range
        nonce_matrix = np.arange(nonce_start, 
                               min(nonce_start + batch_size, nonce_end), 
                               dtype=np.uint32)
        
        # Add nonces to header matrix
        header_with_nonces = header_matrix.copy()
        header_with_nonces[:, -1] = nonce_matrix  # Nonce is last word
        
        # First SHA-256
        first_hash, _ = self.process_block_matrix(header_with_nonces)
        
        # Second SHA-256 (double hashing)
        final_hash, _ = self.process_block_matrix(first_hash)
        
        # Find valid solutions (parallel comparison)
        valid_mask = np.all(final_hash <= target, axis=1)
        valid_indices = np.where(valid_mask)[0]
        
        if len(valid_indices) > 0:
            # Found valid nonce(s)
            valid_nonce = nonce_matrix[valid_indices[0]]
            return valid_nonce, final_hash
        
        return None, final_hash
