
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
)
lowercase__							:							Tuple													=     logging.getLogger(__name__)




def 		A_	(							) ->      List[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     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=10	, help='''batch size'''	)
						parser.add_argument('''--tgt_len'''	, type=snake_case	, default=128	, 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=1600	, help='''length of the retained previous heads'''	)
						parser.add_argument('''--clamp_len'''	, type=snake_case	, 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=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.'''	)
						__UpperCamelCase       					=     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()

						__UpperCamelCase       					=     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 )
						__UpperCamelCase       					=     TransfoXLCorpus.from_pretrained(args.model_name	)

						__UpperCamelCase       					=     corpus.get_iterator('''valid'''	, args.batch_size	, args.tgt_len	, device=snake_case	, ext_len=args.ext_len	)
						__UpperCamelCase       					=     corpus.get_iterator('''test'''	, args.batch_size	, args.tgt_len	, device=snake_case	, ext_len=args.ext_len	)

						# Load a pre-trained model
						__UpperCamelCase       					=     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:
												__UpperCamelCase       					=     args.clamp_len
						if args.same_length:
												__UpperCamelCase       					=     True

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

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

						def format_log(snake_case							:				Optional[int]	, snake_case							:				Union[str, Any]	):
												__UpperCamelCase       					=     '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(snake_case	, snake_case	, math.exp(snake_case	)	)
												return log_str

						__UpperCamelCase       					=     ''''''
						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('''=''' * 100	)
						logger.info(snake_case	)
						logger.info('''=''' * 100	)


if __name__ == "__main__":
				main()






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					CustomConfig



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.nn.Linear(config.hidden_size       ,		config.hidden_size     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													return self.linear(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													pass






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					NoSuperInitConfig



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.nn.Linear(config.attribute       ,		config.attribute     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													return self.linear(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													pass




