"""
Tensor Core subsystem for hyperrealistic GPU simulation.
Models hardware-level matrix multiply-accumulate, scheduling, and memory integration.
"""

import time
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
try:
    from electron_speed import TARGET_SWITCHES_PER_SEC, TRANSISTORS_ON_CHIP
except ImportError:
    TARGET_SWITCHES_PER_SEC = 9e20
    TRANSISTORS_ON_CHIP = 6e11

class TensorCore:
    """
    Simulates a hardware tensor core for matrix operations (multiply-accumulate),
    with realistic operand fetch from registers, shared memory, and VRAM/global memory.
    """
    def __init__(self, bits=2, memory_size=800*1024*1024*1024, bandwidth_tbps=10000, sm=None):
        self.bits = bits
        # Use a sparse dict for local memory: keys are (row, col), values are floats
        self.memory = {}
        self.bandwidth_tbps = bandwidth_tbps  # Simulated bandwidth for operand fetch (TB/s)
        self.sm = sm  # Reference to parent SM for memory access

    def fetch_operand(self, source, addr, shape):
        """
        Fetches a matrix operand from a given source (registers, shared, global).
        Simulates bandwidth and latency.
        """
        n, m = shape
        if source == 'register':
            # Simulate register fetch (fast, minimal latency)
            matrix = self.sm.read_register_matrix(addr, n, m)
            latency = 1e-9  # 1ns
        elif source == 'shared':
            # Simulate shared memory fetch
            matrix = self.sm.shared_mem.read_matrix(addr, n, m)
            latency = 10e-9  # 10ns
        elif source == 'global':
            # Simulate VRAM/global memory fetch
            matrix = self.sm.global_mem.read_matrix(addr, n, m)
            latency = 200e-9  # 200ns
        else:
            raise ValueError(f"Unknown source: {source}")
        # Simulate bandwidth (TB/s)
        data_size_bytes = n * m * (self.bits // 8)
        transfer_time = data_size_bytes / (self.bandwidth_tbps * 1e12)
        time.sleep(latency + transfer_time)  # Simulate delay
        return matrix

    def matmul(self, A, B):
        # A, B: 2D lists (matrices) of voltages
        n = len(A)
        m = len(B[0])
        p = len(B)
        C = [[0.0 for _ in range(m)] for _ in range(n)]
        for i in range(n):
            for j in range(m):
                acc = 0.0
                for k in range(p):
                    acc += A[i][k] * B[k][j]
                C[i][j] = acc
        return C

    def matmul_from_memory(self, srcA, addrA, srcB, addrB, shapeA, shapeB):
        """
        Fetches operands from memory hierarchy and performs matmul.
        srcA/srcB: 'register', 'shared', or 'global'
        addrA/addrB: address or index
        shapeA/shapeB: (n, p), (p, m)
        """
        A = self.fetch_operand(srcA, addrA, shapeA)
        B = self.fetch_operand(srcB, addrB, shapeB)
        return self.matmul(A, B)

    def load_matrix(self, matrix, row_offset=0, col_offset=0):
        # Loads a matrix into local memory (sparse)
        for i, row in enumerate(matrix):
            for j, val in enumerate(row):
                self.memory[(row_offset+i, col_offset+j)] = val

    def read_matrix(self, n, m, row_offset=0, col_offset=0):
        # Reads an n x m matrix from local memory (sparse)
        return [
            [self.memory.get((row_offset+i, col_offset+j), 0.0) for j in range(m)]
            for i in range(n)
        ]

class TensorCoreArray:
    """
    Array of tensor cores per SM, with scheduling and memory integration.
    """
    def __init__(self, num_tensor_cores=8000, bits=2, memory_size=800*1024*1024*1024, bandwidth_tbps=10000, sm=None):
        self.tensor_cores = [TensorCore(bits=bits, memory_size=memory_size, bandwidth_tbps=bandwidth_tbps, sm=sm) for _ in range(num_tensor_cores)]
        self.schedule_ptr = 0
        self.sm = sm
        # Deep realism: calculate theoretical PFLOPS
        # Use foundational switching rate from electron_speed.py
        # PFLOPS = (num_tensor_cores * ops_per_cycle * clock_GHz) / 1e6
        # clock_GHz = (TARGET_SWITCHES_PER_SEC / TRANSISTORS_ON_CHIP) / 1e9
        self.ops_per_cycle = 1024  # Example: 1024 fused-multiply-adds per cycle per core
        self.clock_ghz = (TARGET_SWITCHES_PER_SEC / TRANSISTORS_ON_CHIP) / 1e9
        self.pflops = (num_tensor_cores * self.ops_per_cycle * self.clock_ghz) / 1e6

    def schedule(self):
        # Simple round-robin scheduling
        tc = self.tensor_cores[self.schedule_ptr]
        self.schedule_ptr = (self.schedule_ptr + 1) % len(self.tensor_cores)
        return tc

    def matmul(self, A, B):
        tc = self.schedule()
        # Deep realism: calculate actual compute time
        n = len(A)
        m = len(B[0])
        p = len(B)
        total_ops = n * m * p * 2  # 2 ops per FMA (multiply and add)
        seconds = total_ops / (self.pflops * 1e15)
        print(f"[TensorCoreArray] Matmul on {len(self.tensor_cores)} tensor cores @ {self.pflops:.1f} PFLOPS, ops={total_ops}, time={seconds:.9f}s")
        time.sleep(seconds)  # Simulate actual compute time
        return tc.matmul(A, B)

    def matmul_from_memory(self, srcA, addrA, srcB, addrB, shapeA, shapeB):
        tc = self.schedule()
        n, p = shapeA
        p2, m = shapeB
        total_ops = n * m * p * 2
        seconds = total_ops / (self.pflops * 1e15)
        print(f"[TensorCoreArray] Matmul from memory on {len(self.tensor_cores)} tensor cores @ {self.pflops:.1f} PFLOPS, ops={total_ops}, time={seconds:.9f}s")
        time.sleep(seconds)
        return tc.matmul_from_memory(srcA, addrA, srcB, addrB, shapeA, shapeB)

    def load_matrix(self, matrix, core_idx=0, row_offset=0, col_offset=0):
        self.tensor_cores[core_idx].load_matrix(matrix, row_offset, col_offset)

    def read_matrix(self, n, m, core_idx=0, row_offset=0, col_offset=0):
        return self.tensor_cores[core_idx].read_matrix(n, m, row_offset, col_offset)