
"""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
)
__lowerCAmelCase							:  Tuple 				=		logging.getLogger(__name__)

def __snake_case (      )       ->    Union[str, Any]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''    )
		parser.add_argument('''--model_name'''						,					type=UpperCamelCase						,					default='''transfo-xl-wt103'''						,					help='''pretrained model name'''    )
		parser.add_argument(
		    '''--split'''						,					type=UpperCamelCase						,					default='''test'''						,					choices=['''all''', '''valid''', '''test''']						,					help='''which split to evaluate'''    )
		parser.add_argument('''--batch_size'''						,					type=UpperCamelCase						,					default=10						,					help='''batch size'''    )
		parser.add_argument('''--tgt_len'''						,					type=UpperCamelCase						,					default=128						,					help='''number of tokens to predict'''    )
		parser.add_argument('''--ext_len'''						,					type=UpperCamelCase						,					default=0						,					help='''length of the extended context'''    )
		parser.add_argument('''--mem_len'''						,					type=UpperCamelCase						,					default=1_600						,					help='''length of the retained previous heads'''    )
		parser.add_argument('''--clamp_len'''						,					type=UpperCamelCase						,					default=1_000						,					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=UpperCamelCase						,					required=UpperCamelCase						,					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=UpperCamelCase						,					default=''''''						,					help='''Can be used for distant debugging.'''    )
		parser.add_argument('''--server_port'''						,					type=UpperCamelCase						,					default=''''''						,					help='''Can be used for distant debugging.'''    )
		a__   		=     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=UpperCamelCase    )
				ptvsd.wait_for_attach()

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

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

		a__   		=     corpus.get_iterator('''valid'''						,					args.batch_size						,					args.tgt_len						,					device=UpperCamelCase						,					ext_len=args.ext_len    )
		a__   		=     corpus.get_iterator('''test'''						,					args.batch_size						,					args.tgt_len						,					device=UpperCamelCase						,					ext_len=args.ext_len    )

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

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

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(UpperCamelCase    ):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				a__		,	a__   		=     0, 0.0
				a__   		=     time.time()
				with torch.no_grad():
						a__   		=     None
						for idx, (data, target, seq_len) in enumerate(UpperCamelCase    ):
								a__   		=     model(UpperCamelCase						,					lm_labels=UpperCamelCase						,					mems=UpperCamelCase    )
								a__		,	a__		,	a__   		=     ret
								a__   		=     loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						a__   		=     time.time() - start_time
				logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(UpperCamelCase						,					1_000 * total_time / (idx + 1)    )    )
				return total_loss / total_len

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

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

		a__   		=     ''''''
		if valid_loss is not None:
				log_str += format_log(UpperCamelCase						,					'''valid'''    )
		if test_loss is not None:
				log_str += format_log(UpperCamelCase						,					'''test'''    )

		logger.info('''=''' * 100    )
		logger.info(UpperCamelCase    )
		logger.info('''=''' * 100    )


if __name__ == "__main__":
					main()






"""simple docstring"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




