






"""simple docstring"""



import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__A		:	Union[str, Any]      =		logging.getLogger(__name__)



def 						lowercase   (    ):
		lowercase_			:			Any								=       argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''     )
		parser.add_argument('''--model_name'''		, type=__snake_case		, default='''transfo-xl-wt103'''		, help='''pretrained model name'''     )
		parser.add_argument(
		    '''--split'''		, type=__snake_case		, default='''test'''		, choices=['''all''', '''valid''', '''test''']		, help='''which split to evaluate'''     )
		parser.add_argument('''--batch_size'''		, type=__snake_case		, default=1_0		, help='''batch size'''     )
		parser.add_argument('''--tgt_len'''		, type=__snake_case		, default=1_2_8		, help='''number of tokens to predict'''     )
		parser.add_argument('''--ext_len'''		, type=__snake_case		, default=0		, help='''length of the extended context'''     )
		parser.add_argument('''--mem_len'''		, type=__snake_case		, default=1_6_0_0		, help='''length of the retained previous heads'''     )
		parser.add_argument('''--clamp_len'''		, type=__snake_case		, default=1_0_0_0		, help='''max positional embedding index'''     )
		parser.add_argument('''--no_cuda'''		, action='''store_true'''		, help='''Do not use CUDA even though CUA is available'''     )
		parser.add_argument('''--work_dir'''		, type=__snake_case		, required=__snake_case		, help='''path to the work_dir'''     )
		parser.add_argument('''--no_log'''		, action='''store_true'''		, help='''do not log the eval result'''     )
		parser.add_argument('''--same_length'''		, action='''store_true'''		, help='''set same length attention with masking'''     )
		parser.add_argument('''--server_ip'''		, type=__snake_case		, default=''''''		, help='''Can be used for distant debugging.'''     )
		parser.add_argument('''--server_port'''		, type=__snake_case		, default=''''''		, help='''Can be used for distant debugging.'''     )
		lowercase_			:			Tuple								=       parser.parse_args()
		assert args.ext_len >= 0, "extended context length must be non-negative"

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('''Waiting for debugger attach'''     )
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)		, redirect_output=__snake_case     )
				ptvsd.wait_for_attach()

		lowercase_			:			int								=       torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
		logger.info('''device: {}'''.format(__snake_case     )     )

		# Load a pre-processed dataset
		# You can also build the corpus yourself using TransfoXLCorpus methods
		# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
		# and tokenizing the dataset
		# The pre-processed corpus is a convertion (using the conversion script )
		lowercase_			:			Dict								=       TransfoXLCorpus.from_pretrained(args.model_name     )

		lowercase_			:			Any								=       corpus.get_iterator('''valid'''		, args.batch_size		, args.tgt_len		, device=__snake_case		, ext_len=args.ext_len     )
		lowercase_			:			List[str]								=       corpus.get_iterator('''test'''		, args.batch_size		, args.tgt_len		, device=__snake_case		, ext_len=args.ext_len     )

		# Load a pre-trained model
		lowercase_			:			Optional[Any]								=       TransfoXLLMHeadModel.from_pretrained(args.model_name     )
		model.to(__snake_case     )

		logger.info(
		    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
		        args.batch_size		, args.tgt_len		, args.ext_len		, args.mem_len		, args.clamp_len     )     )

		model.reset_memory_length(args.mem_len     )
		if args.clamp_len > 0:
				lowercase_			:			Dict								=       args.clamp_len
		if args.same_length:
				lowercase_			:			List[str]								=       True

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(__snake_case    :			Tuple     ):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				lowercase_	, lowercase_			:			int								=       0, 0.0
				lowercase_			:			Tuple								=       time.time()
				with torch.no_grad():
						lowercase_			:			Any								=       None
						for idx, (data, target, seq_len) in enumerate(__snake_case     ):
								lowercase_			:			List[Any]								=       model(__snake_case		, lm_labels=__snake_case		, mems=__snake_case     )
								lowercase_	, lowercase_	, lowercase_			:			Tuple								=       ret
								lowercase_			:			int								=       loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						lowercase_			:			Union[str, Any]								=       time.time() - start_time
				logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(__snake_case		, 1_0_0_0 * total_time / (idx + 1)     )     )
				return total_loss / total_len

		# Run on test data.
		if args.split == "all":
				lowercase_			:			Optional[Any]								=       evaluate(__snake_case     )
				lowercase_			:			Tuple								=       evaluate(__snake_case     )
		elif args.split == "valid":
				lowercase_			:			List[str]								=       evaluate(__snake_case     )
				lowercase_			:			int								=       None
		elif args.split == "test":
				lowercase_			:			Any								=       evaluate(__snake_case     )
				lowercase_			:			List[Any]								=       None

		def format_log(__snake_case    :			int		, __snake_case    :			Any     ):
				lowercase_			:			Dict								=       '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(__snake_case		, __snake_case		, math.exp(__snake_case     )     )
				return log_str

		lowercase_			:			Union[str, Any]								=       ''''''
		if valid_loss is not None:
				log_str += format_log(__snake_case		, '''valid'''     )
		if test_loss is not None:
				log_str += format_log(__snake_case		, '''test'''     )

		logger.info('''=''' * 1_0_0     )
		logger.info(__snake_case     )
		logger.info('''=''' * 1_0_0     )


if __name__ == "__main__":
						main()











"""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()



