

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

	_UpperCAmelCase      :							Optional[Any]       =   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
	logger.info("""device: {}""".format(_lowerCAmelCase	)	)

	# 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      :							Dict       =   TransfoXLCorpus.from_pretrained(args.model_name	)

	_UpperCAmelCase      :							Optional[int]       =   corpus.get_iterator("""valid""",					args.batch_size,					args.tgt_len,					device=_lowerCAmelCase,					ext_len=args.ext_len	)
	_UpperCAmelCase      :							List[Any]       =   corpus.get_iterator("""test""",					args.batch_size,					args.tgt_len,					device=_lowerCAmelCase,					ext_len=args.ext_len	)

	# Load a pre-trained model
	_UpperCAmelCase      :							Tuple       =   TransfoXLLMHeadModel.from_pretrained(args.model_name	)
	model.to(_lowerCAmelCase	)

	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      :							Optional[int]       =   args.clamp_len
	if args.same_length:
		_UpperCAmelCase      :							Tuple       =   True

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

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

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

	_UpperCAmelCase      :							Dict       =   """"""
	if valid_loss is not None:
		log_str += format_log(_lowerCAmelCase,					"""valid"""	)
	if test_loss is not None:
		log_str += format_log(_lowerCAmelCase,					"""test"""	)

	logger.info("""=""" * 100	)
	logger.info(_lowerCAmelCase	)
	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_torch_available,
)


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			:					Any							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





