


'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_			:  List[Any]										=   logging.getLogger()


def       _lowercase							(					):
 __A	:		List[Any]	  =      argparse.ArgumentParser()
 parser.add_argument('-f'      )
 __A	:		str	  =      parser.parse_args()
 return args.f


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      logging.StreamHandler(sys.stdout    )
  logger.addHandler(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      get_gpu_count()

  if n_gpu > 1:
   pass
   # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
   # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
   # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
   # cmd = [sys.executable] + distributed_args + args
   # execute_subprocess_async(cmd, env=self.get_env())
   # XXX: test the results - need to save them first into .json file
  else:
   args.insert(0   ,   'run_glue_deebert.py'    )
   with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
    __A	:		Tuple	  =      run_glue_deebert.main()
    for value in result.values():
     self.assertGreaterEqual(__lowercase   ,   0.6_6_6    )







 @slow
 @require_torch_non_multi_gpu
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      '\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
  self.run_and_check(__lowercase    )

  __A	:		Tuple	  =      '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
  self.run_and_check(__lowercase    )

  __A	:		int	  =      '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
  self.run_and_check(__lowercase    )










'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_			:  Optional[Any]										=   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Any										=   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[int]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






