





import gc
import threading
import time

import psutil
import torch

class      lowerCAmelCase							:
 def __init__(   self						:   Tuple			)    ->			Tuple:
     lowerCamelCase__					:   str      				=	psutil.Process()
     lowerCamelCase__					:   int      				=	False
 def   A_			(   self						:   Optional[Any]			)    ->			Any:
     lowerCamelCase__					:   int      				=	-1

     while True:
         lowerCamelCase__					:   Tuple      				=	max(self.process.memory_info().rss    ,  self.cpu_memory_peak			)

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break
 def   A_			(   self						:   str			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	True
     lowerCamelCase__					:   List[Any]      				=	threading.Thread(target=self.peak_monitor			)
     lowerCamelCase__					:   List[Any]      				=	True
     self.thread.start()







 def   A_			(   self						:   Dict			)    ->			List[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase       : Any			     =       PeakCPUMemory()


def  SCREAMING_SNAKE_CASE   ( )    ->  Union[str, Any]:
    # Time
    lowerCamelCase__					:   Optional[int]      				=	{'time': time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__					:   Optional[int]      				=	psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()				):
        lowerCamelCase__					:   List[str]      				=	torch.cuda.memory_allocated(_UpperCAmelCase				)
    torch.cuda.reset_peak_memory_stats()

    return measures


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[Any]:
    # Time
    lowerCamelCase__					:   Dict      				=	{'time': time.time() - start_measures['time']}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__					:   Any      				=	(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__					:   Any      				=	(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()				):
        lowerCamelCase__					:   str      				=	(torch.cuda.memory_allocated(_UpperCAmelCase				) - start_measures[str(_UpperCAmelCase				)]) / 2**20
        lowerCamelCase__					:   Tuple      				=	(torch.cuda.max_memory_allocated(_UpperCAmelCase				) - start_measures[str(_UpperCAmelCase				)]) / 2**20

    return measures


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    print(F"""{description}:"""				)
    print(F"""- Time: {measures["time"]:.2f}s"""				)
    for i in range(torch.cuda.device_count()				):
        print(F"""- GPU {i} allocated: {measures[str(_UpperCAmelCase				)]:.2f}MiB"""				)
        lowerCamelCase__					:   Dict      				=	measures[F"""{i}-peak"""]
        print(F"""- GPU {i} peak: {peak:.2f}MiB"""				)
    print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""				)
    print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""				)












import socket


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   int      				=	socket.socket(socket.AF_INET		,			socket.SOCK_STREAM				)
    lowerCamelCase__					:   Optional[int]      				=	socket.gethostname()
    lowerCamelCase__					:   Optional[int]      				=	1_2312

    sock.connect((host, port)				)
    sock.send(b'Hello server!'				)

    with open('Received_file'		,			'wb'				) as out_file:
        print('File opened'				)
        print('Receiving data...'				)
        while True:
            lowerCamelCase__					:   str      				=	sock.recv(1024				)
            if not data:
                break
            out_file.write(_UpperCAmelCase				)

    print('Successfully received the file'				)
    sock.close()
    print('Connection closed'				)


if __name__ == "__main__":
    main()





