'''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
)
__lowercase				   =  logging.getLogger(__name__)






def     snake_case__							(      )   ->     int:

		'''simple docstring'''


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

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

		# 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=_A				,       ext_len=args.ext_len			)
		lowerCAmelCase        =  corpus.get_iterator("""test"""				,       args.batch_size				,       args.tgt_len				,       device=_A				,       ext_len=args.ext_len			)

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

		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(_A: Optional[int]			):
				# 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(_A			):
								lowerCAmelCase        =  model(_A				,       lm_labels=_A				,       mems=_A			)
								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(_A				,       1000 * total_time / (idx + 1)			)			)
				return total_loss / total_len

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

		def format_log(_A: List[Any]				,       _A: Any			):
				lowerCAmelCase        =  """| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(_A				,       _A				,       math.exp(_A			)			)
				return log_str

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

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


if __name__ == "__main__":
						main()





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowercase				   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




