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


def 		_UpperCAmelCase			(					)      ->  Optional[Any]:
  _lowerCAmelCase    :					int							=   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=1_28					, 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=16_00					, help="""length of the retained previous heads"""						)
  parser.add_argument("""--clamp_len"""					, type=_lowerCamelCase					, default=10_00					, 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."""						)
  _lowerCAmelCase    :					str							=   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()

  _lowerCAmelCase    :					Tuple							=   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 )
  _lowerCAmelCase    :					Optional[Any]							=   TransfoXLCorpus.from_pretrained(args.model_name						)

  _lowerCAmelCase    :					Union[str, Any]							=   corpus.get_iterator("""valid"""					, args.batch_size					, args.tgt_len					, device=_lowerCamelCase					, ext_len=args.ext_len						)
  _lowerCAmelCase    :					Tuple							=   corpus.get_iterator("""test"""					, args.batch_size					, args.tgt_len					, device=_lowerCamelCase					, ext_len=args.ext_len						)

  # Load a pre-trained model
  _lowerCAmelCase    :					List[str]							=   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:
    _lowerCAmelCase    :					int							=   args.clamp_len
  if args.same_length:
    _lowerCAmelCase    :					Any							=   True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(_lowerCamelCase  :    int						):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    _lowerCAmelCase			,   _lowerCAmelCase    :					Any							=   0, 0.0
    _lowerCAmelCase    :					Any							=   time.time()
    with torch.no_grad():
      _lowerCAmelCase    :					str							=   None
      for idx, (data, target, seq_len) in enumerate(_lowerCamelCase						):
        _lowerCAmelCase    :					Union[str, Any]							=   model(_lowerCamelCase					, lm_labels=_lowerCamelCase					, mems=_lowerCamelCase						)
        _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					Tuple							=   ret
        _lowerCAmelCase    :					List[Any]							=   loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      _lowerCAmelCase    :					Dict							=   time.time() - start_time
    logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(_lowerCamelCase					, 10_00 * total_time / (idx + 1)						)						)
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    _lowerCAmelCase    :					List[str]							=   evaluate(_lowerCamelCase						)
    _lowerCAmelCase    :					Any							=   evaluate(_lowerCamelCase						)
  elif args.split == "valid":
    _lowerCAmelCase    :					int							=   evaluate(_lowerCamelCase						)
    _lowerCAmelCase    :					Union[str, Any]							=   None
  elif args.split == "test":
    _lowerCAmelCase    :					Optional[int]							=   evaluate(_lowerCamelCase						)
    _lowerCAmelCase    :					List[Any]							=   None

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

  _lowerCAmelCase    :					List[Any]							=   """"""
  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("""=""" * 1_00						)
  logger.info(_lowerCamelCase						)
  logger.info("""=""" * 1_00						)


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,
)


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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


