Please complete the implementation of logging and progress bars inside this code and give us the full new code at the end: from collections import deque
import random
import logging
from tqdm import tqdm

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')


### BASE UNIT ###
class BaseUnit:
    def __init__(self, num_units, power, frequency, latency):
        self.num_units = num_units       # Number of components (e.g., cores, TMUs)
        self.power = power               # Power consumption (watts)
        self.frequency = frequency       # Base clock rate (GHz)
        self.latency = latency           # Pipeline delay (cycles)


class Instruction:
    def __init__(self, instr_type, operands, dest=None, address=None, size_bytes=None):
        self.type = instr_type          # "ALU", "FPU", "Load/Store"
        self.operands = operands        # Registers or memory addresses
        self.dest = dest                # Destination register
        self.address = address          # For memory-related instructions
        self.size_bytes = size_bytes    # Size of memory data transfer


### SHADER CORE ###
class ShaderCore(BaseUnit):
    def __init__(self, simd_width, base_frequency):
        super().__init__(num_units=1, power=0.25, frequency=base_frequency, latency=5)
        self.logger = logging.getLogger(f"ShaderCore-{id(self)}")
        self.pipeline_stages = {
            "fetch": deque(),
            "decode": deque(),
            "execute": deque(),
            "memory_access": deque(),
            "write_back": deque(),
        }
        self.register_file = {"r0": 0, "r1": 0, "r2": 0, "r3": 0}
        self.execution_units = {
            "ALU": {"latency": 4, "throughput": simd_width},
            "FPU": {"latency": 8, "throughput": simd_width // 2},
            "Load/Store": {"latency": 10, "bandwidth": 128},
        }
        self.program_memory = []         # Shader program
        self.program_counter = 0

    def load_program(self, program):
        self.program_memory = program
        self.program_counter = 0

    def fetch_instruction(self):
        if self.program_counter < len(self.program_memory):
            instruction = self.program_memory[self.program_counter]
            self.program_counter += 1
            return instruction
        return None

    def execute_instruction(self, instruction):
        # Simulate different instruction types
        if instruction.type == "ALU":
            return sum(self.register_file[reg] for reg in instruction.operands)
        elif instruction.type == "FPU":
            return sum(self.register_file[reg] * 2 for reg in instruction.operands)  # Example for FP32 operation
        elif instruction.type == "Load/Store":
            return instruction.address  # Load/Store would interact with Cache/Memory
        return None

    def execute_cycle(self):
        # Completed instructions (from write-back)
        if self.pipeline_stages["write_back"]:
            completed_instruction = self.pipeline_stages["write_back"].popleft()
            # Handle completed instruction (e.g., update register)

        # Progress instructions through pipeline stages
        if self.pipeline_stages["memory_access"]:
            self.pipeline_stages["write_back"].append(
                self.pipeline_stages["memory_access"].popleft()
            )

        if self.pipeline_stages["execute"]:
            # Process instructions in "execute" stage based on throughput
            for _ in range(self.execution_units["FPU"]["throughput"]):
                if self.pipeline_stages["execute"]:
                    instruction = self.pipeline_stages["execute"].popleft()
                    result = self.execute_instruction(instruction)
                    if instruction.dest:
                        self.register_file[instruction.dest] = result

            self.pipeline_stages["memory_access"].append(None)

        if self.pipeline_stages["decode"]:
            self.pipeline_stages["execute"].append(self.pipeline_stages["decode"].popleft())

        if self.pipeline_stages["fetch"]:
            self.pipeline_stages["decode"].append(self.pipeline_stages["fetch"].popleft())

        # Fetch new instruction
        if len(self.pipeline_stages["fetch"]) < 2:  # Example fetch limit
            instruction = self.fetch_instruction()
            if instruction:
                self.pipeline_stages["fetch"].append(instruction)
        # Log pipeline status
        self.logger.debug(f"Pipeline Status: Fetch={len(self.pipeline_stages['fetch'])}, "
                          f"Decode={len(self.pipeline_stages['decode'])}, Execute={len(self.pipeline_stages['execute'])}, "
                           f"Memory Access={len(self.pipeline_stages['memory_access'])}, "
                          f"Write Back = {len(self.pipeline_stages['write_back'])}")


### CACHE UNIT ###
class CacheLine:
    def __init__(self, tag, data=None):
        self.tag = tag
        self.data = data                 # Placeholder for actual data


class CacheUnit(BaseUnit):
    def __init__(self, size_kb, line_size=64, latency=4, bandwidth=256):
        super().__init__(num_units=1, power=0.2, frequency=1.0, latency=latency)
        self.logger = logging.getLogger(f"CacheUnit-{id(self)}") # Logger for Cache unit
        self.size_bytes = size_kb * 1024
        self.line_size = line_size
        self.cache_lines = {}            # Line address -> CacheLine object
        self.access_history = deque()    # For LRU eviction
        self.cache_hits = 0
        self.cache_misses = 0

    def access_cache(self, address, size_bytes):
        line_address = address // self.line_size
        end_line = (address + size_bytes - 1) // self.line_size
        latency = 0

        for line in range(line_address, end_line + 1):
            if line in self.cache_lines:
                self.cache_hits += 1
                self.access_history.remove(line)
                self.access_history.append(line)
            else:
                self.cache_misses += 1
                self.evict_if_needed()
                self.cache_lines[line] = CacheLine(tag=line, data=f"Data_{line}")
                self.access_history.append(line)
                latency += self.latency

        return latency

    def evict_if_needed(self):
        while len(self.access_history) > self.size_bytes // self.line_size:
            evicted_line = self.access_history.popleft()
            del self.cache_lines[evicted_line]


### MEMORY CONTROLLER ###
class MemoryController:
    def __init__(self, bandwidth_gbps):
        self.bandwidth_bytes_per_cycle = bandwidth_gbps * 1e9 / 1e9  # Bytes per cycle
        self.active_transfers = deque()
        self.logger = logging.getLogger("MemoryController")

    def access_memory(self, address, size_bytes):
        transfer_time = size_bytes / self.bandwidth_bytes_per_cycle
        contention_delay = len(self.active_transfers) * 0.1  # Simple contention simulation
        latency = transfer_time + contention_delay
        self.active_transfers.append({"address": address, "latency": latency})
        self.logger.debug(f"Accessing memory address {address}, size {size_bytes} bytes") # Log memory accesses
        return {"latency": latency, "data": f"Data_from_{address}"}

    def advance_cycle(self):
        for transfer in list(self.active_transfers):
            transfer["latency"] -= 1
            if transfer["latency"] <= 0:
                self.active_transfers.popleft()


### INTERCONNECT ###
class Interconnect:
    def __init__(self, topology="mesh", bandwidth_gbps=256):
        self.bandwidth_bytes_per_cycle = bandwidth_gbps * 1e9 / 1e9
        self.active_transfers = deque()
        self.logger = logging.getLogger("Interconnect")

    def route_data(self, source, destination, size_bytes):
        latency = size_bytes / self.bandwidth_bytes_per_cycle
        contention_delay = len(self.active_transfers) * 0.1  # Simplified contention
        total_latency = latency + contention_delay
        self.active_transfers.append((source, destination, total_latency))
        self.logger.debug(f"Routing data from {source} to {destination} ({size_bytes} bytes, {total_latency} latency)") # Log data routing
        return total_latency


### GPU CLUSTER ###
class GPUCluster:
    def __init__(self, num_cores, simd_width, l2_cache_size_kb, interconnect_bandwidth):
        self.shader_cores = [
            ShaderCore(simd_width, base_frequency=1.5) for _ in range(num_cores)
        ]
        self.l2_cache = CacheUnit(size_kb=l2_cache_size_kb, latency=10, bandwidth=interconnect_bandwidth)
        self.interconnect = Interconnect(topology="ring", bandwidth_gbps=interconnect_bandwidth)
        self.logger = logging.getLogger(f"GPUCluster-{id(self)}")

    def execute_workload(self, workload, memory_controller):
        cycles = 0

        self.logger.info(f"Executing workload on cluster (size: {len(workload)})") # Log workload start
        while workload or any(core.pipeline_stages["write_back"] for core in self.shader_cores):
            # Execute shader cores
            for core in self.shader_cores:
                core.execute_cycle()

                # Handle memory instructions
                if core.pipeline_stages["memory_access"]:
                    instruction = core.pipeline_stages["memory_access"][0]
                    if instruction and instruction.type == "Load/Store":
                        cache_latency = self.l2_cache.access_cache(instruction.address, instruction.size_bytes)
                        if instruction.address not in self.l2_cache.cache_lines:
                            interconnect_latency = self.interconnect.route_data("L2", "MemoryController", instruction.size_bytes)
                            memory_latency = memory_controller.access_memory(instruction.address, instruction.size_bytes)["latency"]
                            cycles += cache_latency + interconnect_latency + memory_latency
            cycles += 1
        return cycles


### GPU SOC ###
class GPUSoC:
    def __init__(self, num_clusters, cores_per_cluster, simd_width, l2_cache_size_kb, memory_bandwidth, process_node):
        self.clusters = [
            GPUCluster(cores_per_cluster, simd_width, l2_cache_size_kb, interconnect_bandwidth=memory_bandwidth // num_clusters)
            for _ in range(num_clusters)
        ]
        self.memory_controller = MemoryController(memory_bandwidth)
        self.logger = logging.getLogger("GPUSoC")

    def execute_workload(self, workload):
        workload_per_cluster = len(workload) // len(self.clusters)
        cycles = 0

        with tqdm(total=len(self.clusters), desc="Simulating Clusters") as pbar:  # Progress bar for clusters
            for i, cluster in enumerate(self.clusters):
                self.logger.info(f"Starting workload execution on cluster {i + 1}") # Log per cluster execution
                cluster_workload = workload[i * workload_per_cluster:(i + 1) * workload_per_cluster]
                cycles += cluster.execute_workload(cluster_workload, self.memory_controller)
                pbar.update(1)  # Update progress bar after each cluster
        self.logger.info("Workload execution on all clusters complete.") # Log finishing the workload execution
        return cycles


### TEST ###
if __name__ == "__main__":
    workload = [
        Instruction("FPU", ["r0", "r1"], "r2", size_bytes=random.randint(4, 64)) for _ in range(100)
    ] + [
        Instruction("Load/Store", ["r0"], address=random.randint(0, 1000), size_bytes=random.randint(4, 64)) for _ in range(100)
    ]

    gpu = GPUSoC(num_clusters=4, cores_per_cluster=32, simd_width=16, l2_cache_size_kb=512, memory_bandwidth=1024, process_node=5)
    total_cycles = gpu.execute_workload(workload)
    print(f"Total Cycles to Complete Workload: {total_cycles}")

    # Example of accessing cache stats (you can add this after the workload execution)
    for cluster in gpu.clusters:
        print(f"Cluster {cluster} L2 Cache Stats: {cluster.l2_cache.cache_stats()}")