



"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase

import pytest
@pytest.mark.integration
class 							_UpperCAmelCase							( SCREAMING_SNAKE_CASE_      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:

         with open(__UpperCAmelCase       ,	encoding='''utf-8'''		) as input_file:
             _UpperCAmelCase							:  List[str]											=    re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''		)
             _UpperCAmelCase							:  Any											=    input_file.read()
             _UpperCAmelCase							:  Any											=    regexp.search(__UpperCAmelCase		)

         return match





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[int]:

         with open(__UpperCAmelCase       ,	encoding='''utf-8'''		) as input_file:
             _UpperCAmelCase							:  str											=    re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''       ,	re.DOTALL		)
             _UpperCAmelCase							:  Optional[int]											=    input_file.read()
             # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
             _UpperCAmelCase							:  Any											=    regexp.finditer(__UpperCAmelCase		)

         _UpperCAmelCase							:  Union[str, Any]											=    [match for match in matches if match is not None and match.group(1		) is not None]
         return matches[0] if matches else None





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  str											=    Path('''./datasets'''		)
         _UpperCAmelCase							:  Dict											=    list(dataset_paths.absolute().glob('''**/*.py'''		)		)

         for dataset in dataset_files:
             if self._no_encoding_on_file_open(str(__UpperCAmelCase		)		):
                 raise AssertionError(f'open(...) must use utf-8 encoding in {dataset}'		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Union[str, Any]											=    Path('''./datasets'''		)
         _UpperCAmelCase							:  Optional[int]											=    list(dataset_paths.absolute().glob('''**/*.py'''		)		)

         for dataset in dataset_files:
             if self._no_print_statements(str(__UpperCAmelCase		)		):
                 raise AssertionError(f'print statement found in {dataset}. Use datasets.logger/logging instead.'		)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


