






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
)
UpperCAmelCase_					:		str           = logging.getLogger(__name__)

def   SCREAMING_SNAKE_CASE_				(				)						->     Optional[Any]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      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."""   )
	UpperCamelCase	:Union[str, Any]		=      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()

	UpperCamelCase	:Union[str, Any]		=      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 )
	UpperCamelCase	:Any		=      TransfoXLCorpus.from_pretrained(args.model_name   )

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

	# Load a pre-trained model
	UpperCamelCase	:Any		=      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:
		UpperCamelCase	:Any		=      args.clamp_len
	if args.same_length:
		UpperCamelCase	:Optional[Any]		=      True

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(__magic_name__   :    Optional[int]   ):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		UpperCamelCase	:Optional[Any]		=      0, 0.0
		UpperCamelCase	:Union[str, Any]		=      time.time()
		with torch.no_grad():
			UpperCamelCase	:Union[str, Any]		=      None
			for idx, (data, target, seq_len) in enumerate(a_   ):
				UpperCamelCase	:Tuple		=      model(a_      ,     lm_labels=a_      ,     mems=a_   )
				UpperCamelCase	:List[Any]		=      ret
				UpperCamelCase	:Any		=      loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			UpperCamelCase	:Dict		=      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":
		UpperCamelCase	:Union[str, Any]		=      evaluate(a_   )
		UpperCamelCase	:int		=      evaluate(a_   )
	elif args.split == "valid":
		UpperCamelCase	:Union[str, Any]		=      evaluate(a_   )
		UpperCamelCase	:int		=      None
	elif args.split == "test":
		UpperCamelCase	:str		=      evaluate(a_   )
		UpperCamelCase	:Dict		=      None

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

	UpperCamelCase	:List[str]		=      ''''''
	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_torch_available, is_vision_available


UpperCamelCase_					=				{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				["FlavaFeatureExtractor"]
  UpperCamelCase_					=				["FlavaImageProcessor"]
  UpperCamelCase_					=				["FlavaProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				[
      "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlavaForPreTraining",
      "FlavaImageCodebook",
      "FlavaImageModel",
      "FlavaModel",
      "FlavaMultimodalModel",
      "FlavaPreTrainedModel",
      "FlavaTextModel",
  ]

if TYPE_CHECKING:
  from .configuration_flava import (
      FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
      FlavaConfig,
      FlavaImageCodebookConfig,
      FlavaImageConfig,
      FlavaMultimodalConfig,
      FlavaTextConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_flava import FlavaFeatureExtractor
    from .image_processing_flava import FlavaImageProcessor
    from .processing_flava import FlavaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flava import (
        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlavaForPreTraining,
        FlavaImageCodebook,
        FlavaImageModel,
        FlavaModel,
        FlavaMultimodalModel,
        FlavaPreTrainedModel,
        FlavaTextModel,
    )

else:
  import sys

  UpperCamelCase_					=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)