






import torch
from torch import autograd





class  _UpperCAmelCase	(			autograd.Function							):




					@staticmethod
					def   A  (    A					:						int   ,						A					:						torch.tensor   ,						A					:						float   ,						A					:						bool       )  ->  int:
							lowercase_			:			Optional[int]								=       inputs.numel()
							lowercase_			:			List[str]								=       int(0.005 * nb_elems       ) + 1
							if sigmoid:
									lowercase_			:			Optional[int]								=       (torch.sigmoid(A       ) > threshold).type(inputs.type()       )
							else:
									lowercase_			:			Dict								=       (inputs > threshold).type(inputs.type()       )
							if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									lowercase_			:			Tuple								=       inputs.flatten().kthvalue(max(nb_elems - nb_min   ,						1       )       ).values
									lowercase_			:			List[str]								=       (inputs > k_threshold).type(inputs.type()       )
							return mask






					@staticmethod
					def   A  (    A					:						int   ,						A					:						Optional[Any]       )  ->  Optional[int]:
							return gradOutput, None, None





class  _UpperCAmelCase	(			autograd.Function							):




					@staticmethod
					def   A  (    A					:						Tuple   ,						A					:						torch.tensor   ,						A					:						float       )  ->  Optional[int]:
							lowercase_			:			Union[str, Any]								=       inputs.clone()
							lowercase_			:			Optional[int]								=       inputs.flatten().sort(descending=A       )
							lowercase_			:			Union[str, Any]								=       int(threshold * inputs.numel()       )

							# flat_out and mask access the same memory.
							lowercase_			:			List[str]								=       mask.flatten()
							lowercase_			:			Any								=       0
							lowercase_			:			Tuple								=       1
							return mask






					@staticmethod
					def   A  (    A					:						str   ,						A					:						Optional[int]       )  ->  Optional[Any]:
							return gradOutput, None





class  _UpperCAmelCase	(			_A							):




					@staticmethod
					def   A  (    A					:						torch.tensor   ,						A					:						float       )  ->  Dict:
							lowercase_			:			List[Any]								=       inputs.clone()
							lowercase_			:			int								=       inputs.abs().flatten().sort(descending=A       )
							lowercase_			:			Any								=       int(threshold * inputs.numel()       )

							# flat_out and mask access the same memory.
							lowercase_			:			Optional[Any]								=       mask.flatten()
							lowercase_			:			Dict								=       0
							lowercase_			:			List[Any]								=       1
							return mask











"""simple docstring"""



def 						lowercase   (    __snake_case    :			str = "mytext.txt"		, __snake_case    :			bool = False     ):
		import socket

		lowercase_			:			List[str]								=       1_2_3_1_2  # Reserve a port for your service.
		lowercase_			:			Dict								=       socket.socket()  # Create a socket object
		lowercase_			:			int								=       socket.gethostname()  # Get local machine name
		sock.bind((host, port)     )  # Bind to the port
		sock.listen(5     )  # Now wait for client connection.

		print('''Server listening....'''     )

		while True:
				lowercase_	, lowercase_			:			int								=       sock.accept()  # Establish connection with client.
				print(F'''Got connection from {addr}'''     )
				lowercase_			:			List[Any]								=       conn.recv(1_0_2_4     )
				print(F'''Server received: {data = }'''     )

				with open(__snake_case		, '''rb'''     ) as in_file:
						lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )
						while data:
								conn.send(__snake_case     )
								print(F'''Sent {data!r}'''     )
								lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )

				print('''Done sending'''     )
				conn.close()
				if testing:  # Allow the test to complete
						break

		sock.shutdown(1     )
		sock.close()


if __name__ == "__main__":
						send_file()



