


import argparse
import os
import re


_A		       =						'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_A		       =						re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_A		       =						re.compile(R'\s*\(\s*"(\S[^"]+)"')

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					bool = False				):
     with open(SCREAMING_SNAKE_CASE__			,     'r'			,     encoding='utf-8'				) as f:
          __UpperCamelCase    							=f.read()

     __UpperCamelCase    							=content.split('\n'				)
     __UpperCamelCase    							=[]
     __UpperCamelCase    							=0
     while line_idx < len(SCREAMING_SNAKE_CASE__				):
          if _re_intro_mapping.search(lines[line_idx]				) is not None:
               __UpperCamelCase    							=len(re.search(r'^(\s*)\S'			,     lines[line_idx]				).groups()[0]				) + 8
               # Start of a new mapping!
               while not lines[line_idx].startswith(' ' * indent + '('				):
                    new_lines.append(lines[line_idx]				)
                    line_idx += 1

               __UpperCamelCase    							=[]
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         __UpperCamelCase    							=line_idx
                         while not lines[line_idx].startswith(' ' * indent + ')'				):
                              line_idx += 1
                         blocks.append('\n'.join(lines[start_idx : line_idx + 1]				)				)
                    else:
                         blocks.append(lines[line_idx]				)
                    line_idx += 1

               # Sort blocks by their identifiers
               __UpperCamelCase    							=sorted(SCREAMING_SNAKE_CASE__			,     key=lambda SCREAMING_SNAKE_CASE__				: _re_identifier.search(SCREAMING_SNAKE_CASE__				).groups()[0]				)
               new_lines += blocks
          else:
               new_lines.append(lines[line_idx]				)
               line_idx += 1

     if overwrite:
          with open(SCREAMING_SNAKE_CASE__			,     'w'			,     encoding='utf-8'				) as f:
               f.write('\n'.join(SCREAMING_SNAKE_CASE__				)				)
     elif "\n".join(SCREAMING_SNAKE_CASE__				) != content:
          return True

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					bool = False				):
     __UpperCamelCase    							=[os.path.join(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) for f in os.listdir(SCREAMING_SNAKE_CASE__				) if f.endswith('.py'				)]
     __UpperCamelCase    							=[sort_auto_mapping(SCREAMING_SNAKE_CASE__			,     overwrite=SCREAMING_SNAKE_CASE__				) for fname in fnames]

     if not overwrite and any(SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=[f for f, d in zip(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				) if d]
          raise ValueError(
              F'The following files have auto mappings that need sorting: {", ".join(SCREAMING_SNAKE_CASE__				)}. Run `make style` to fix'
              ' this.'				)


if __name__ == "__main__":
   _A		       =						argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   _A		       =						parser.parse_args()

   sort_all_auto_mappings(not args.check_only)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		       =						{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['ConvBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ConvBertForMaskedLM',
       'ConvBertForMultipleChoice',
       'ConvBertForQuestionAnswering',
       'ConvBertForSequenceClassification',
       'ConvBertForTokenClassification',
       'ConvBertLayer',
       'ConvBertModel',
       'ConvBertPreTrainedModel',
       'load_tf_weights_in_convbert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFConvBertForMaskedLM',
       'TFConvBertForMultipleChoice',
       'TFConvBertForQuestionAnswering',
       'TFConvBertForSequenceClassification',
       'TFConvBertForTokenClassification',
       'TFConvBertLayer',
       'TFConvBertModel',
       'TFConvBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





