






"""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[str]      = logging.getLogger(__name__)





def 						UpperCAmelCase__		(					):






  """simple docstring"""





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

  A_  :	Dict    						=  torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
  logger.info('device: {}'.format(_UpperCAmelCase ) )

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

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

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

  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:
    A_  :	Dict    						=  args.clamp_len
  if args.same_length:
    A_  :	Union[str, Any]    						=  True

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

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

  def format_log(_UpperCAmelCase			, _UpperCAmelCase ):
    A_  :	List[str]    						=  '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_UpperCAmelCase			, _UpperCAmelCase			, math.exp(_UpperCAmelCase ) )
    return log_str

  A_  :	Optional[int]    						=  ''
  if valid_loss is not None:
    log_str += format_log(_UpperCAmelCase			, 'valid' )
  if test_loss is not None:
    log_str += format_log(_UpperCAmelCase			, 'test' )

  logger.info('=' * 100 )
  logger.info(_UpperCAmelCase )
  logger.info('=' * 100 )


if __name__ == "__main__":
   main()






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_					:						List[str]      = logging.get_logger(__name__)

lowerCamelCase_					:						Optional[int]      = {
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Tuple        =       """electra"""

      def __init__(					self ,				snake_case_=3_0_5_2_2 ,				snake_case_=1_2_8 ,				snake_case_=2_5_6 ,				snake_case_=1_2 ,				snake_case_=4 ,				snake_case_=1_0_2_4 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=5_1_2 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-12 ,				snake_case_="first" ,				snake_case_=True ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0 ,				snake_case_="absolute" ,				snake_case_=True ,				snake_case_=None ,				**snake_case_ ,				):

        """simple docstring"""

        super().__init__(pad_token_id=snake_case_ ,				**snake_case_  )

        A_  :	Optional[Any]    						=  vocab_size
        A_  :	Any    						=  embedding_size
        A_  :	Optional[int]    						=  hidden_size
        A_  :	Tuple    						=  num_hidden_layers
        A_  :	Tuple    						=  num_attention_heads
        A_  :	List[Any]    						=  intermediate_size
        A_  :	Any    						=  hidden_act
        A_  :	str    						=  hidden_dropout_prob
        A_  :	Any    						=  attention_probs_dropout_prob
        A_  :	str    						=  max_position_embeddings
        A_  :	Optional[Any]    						=  type_vocab_size
        A_  :	Dict    						=  initializer_range
        A_  :	Optional[int]    						=  layer_norm_eps

        A_  :	int    						=  summary_type
        A_  :	List[str]    						=  summary_use_proj
        A_  :	Optional[Any]    						=  summary_activation
        A_  :	Optional[Any]    						=  summary_last_dropout
        A_  :	List[Any]    						=  position_embedding_type
        A_  :	Union[str, Any]    						=  use_cache
        A_  :	Tuple    						=  classifier_dropout


class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_  :	int    						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_  :	Optional[Any]    						=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]  )