






'''simple docstring'''





def 			__UpperCAmelCase				(lowercase__ = "mytext.txt"	,lowercase__ = False				)		->				None:

						'''simple docstring'''


						import socket

						a_				     =   12312  # Reserve a port for your service.
						a_				     =   socket.socket()  # Create a socket object
						a_				     =   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:
												a_     ,       a_				     =   sock.accept()  # Establish connection with client.
												print(F"""Got connection from {addr}"""				)
												a_				     =   conn.recv(1024				)
												print(F"""Server received: {data = }"""				)

												with open(lowercase__	,"rb"				) as in_file:
																		a_				     =   in_file.read(1024				)
																		while data:
																								conn.send(lowercase__				)
																								print(F"""Sent {data!r}"""				)
																								a_				     =   in_file.read(1024				)

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

						sock.shutdown(1				)
						sock.close()


if __name__ == "__main__":
							send_file()











'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



