





'''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					: Optional[Any]      =    logging.getLogger(__name__)





def    _lowerCAmelCase						(						)					->					Optional[Any]:
			__lowerCAmelCase											=				argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""	)
			parser.add_argument("""--model_name"""					,	type=lowercase					,	default="""transfo-xl-wt103"""					,	help="""pretrained model name"""	)
			parser.add_argument(
			    """--split"""					,	type=lowercase					,	default="""test"""					,	choices=["""all""", """valid""", """test"""]					,	help="""which split to evaluate"""	)
			parser.add_argument("""--batch_size"""					,	type=lowercase					,	default=10					,	help="""batch size"""	)
			parser.add_argument("""--tgt_len"""					,	type=lowercase					,	default=128					,	help="""number of tokens to predict"""	)
			parser.add_argument("""--ext_len"""					,	type=lowercase					,	default=0					,	help="""length of the extended context"""	)
			parser.add_argument("""--mem_len"""					,	type=lowercase					,	default=1600					,	help="""length of the retained previous heads"""	)
			parser.add_argument("""--clamp_len"""					,	type=lowercase					,	default=1000					,	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=lowercase					,	required=lowercase					,	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=lowercase					,	default=""""""					,	help="""Can be used for distant debugging."""	)
			parser.add_argument("""--server_port"""					,	type=lowercase					,	default=""""""					,	help="""Can be used for distant debugging."""	)
			__lowerCAmelCase											=				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=lowercase	)
						ptvsd.wait_for_attach()

			__lowerCAmelCase											=				torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
			logger.info("""device: {}""".format(lowercase	)	)

			# 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 )
			__lowerCAmelCase											=				TransfoXLCorpus.from_pretrained(args.model_name	)

			__lowerCAmelCase											=				corpus.get_iterator("""valid"""					,	args.batch_size					,	args.tgt_len					,	device=lowercase					,	ext_len=args.ext_len	)
			__lowerCAmelCase											=				corpus.get_iterator("""test"""					,	args.batch_size					,	args.tgt_len					,	device=lowercase					,	ext_len=args.ext_len	)

			# Load a pre-trained model
			__lowerCAmelCase											=				TransfoXLLMHeadModel.from_pretrained(args.model_name	)
			model.to(lowercase	)

			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:
						__lowerCAmelCase											=				args.clamp_len
			if args.same_length:
						__lowerCAmelCase											=				True

			###############################################################################
			# Evaluation code
			###############################################################################
			def evaluate(lowercase	):
						# Turn on evaluation mode which disables dropout.
						model.eval()
						__lowerCAmelCase						,       __lowerCAmelCase											=				0, 0.0
						__lowerCAmelCase											=				time.time()
						with torch.no_grad():
									__lowerCAmelCase											=				None
									for idx, (data, target, seq_len) in enumerate(lowercase	):
												__lowerCAmelCase											=				model(lowercase					,	lm_labels=lowercase					,	mems=lowercase	)
												__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				ret
												__lowerCAmelCase											=				loss.mean()
												total_loss += seq_len * loss.item()
												total_len += seq_len
									__lowerCAmelCase											=				time.time() - start_time
						logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(lowercase					,	1000 * total_time / (idx + 1)	)	)
						return total_loss / total_len

			# Run on test data.
			if args.split == "all":
						__lowerCAmelCase											=				evaluate(lowercase	)
						__lowerCAmelCase											=				evaluate(lowercase	)
			elif args.split == "valid":
						__lowerCAmelCase											=				evaluate(lowercase	)
						__lowerCAmelCase											=				None
			elif args.split == "test":
						__lowerCAmelCase											=				evaluate(lowercase	)
						__lowerCAmelCase											=				None

			def format_log(lowercase					,	lowercase	):
						__lowerCAmelCase											=				"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(lowercase					,	lowercase					,	math.exp(lowercase	)	)
						return log_str

			__lowerCAmelCase											=				""""""
			if valid_loss is not None:
						log_str += format_log(lowercase					,	"""valid"""	)
			if test_loss is not None:
						log_str += format_log(lowercase					,	"""test"""	)

			logger.info("""=""" * 100	)
			logger.info(lowercase	)
			logger.info("""=""" * 100	)


if __name__ == "__main__":
	main()








'''simple docstring'''







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > threshold).type(inputs.type()			)
					else:
								__lowerCAmelCase											=				(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
								__lowerCAmelCase											=				inputs.flatten().kthvalue(max(nb_elems - nb_min,1			)			).values
								__lowerCAmelCase											=				(inputs > k_threshold).type(inputs.type()			)
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask

