


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					(    lowerCAmelCase_	,    lowerCAmelCase_ = False )    ->int:
   with open(lowerCAmelCase_	,    """r"""	,    encoding="""utf-8""" ) as f:
      UpperCAmelCase      					=  f.read()

   UpperCAmelCase      					=  content.split("""\n""" )
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  0
   while line_idx < len(lowerCAmelCase_ ):
      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(lowerCAmelCase_	,    key=lambda lowerCAmelCase_ : _re_identifier.search(lowerCAmelCase_ ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCAmelCase_	,    """w"""	,    encoding="""utf-8""" ) as f:
         f.write("""\n""".join(lowerCAmelCase_ ) )
   elif "\n".join(lowerCAmelCase_ ) != content:
      return True
def 	_UpperCamelCase					(    lowerCAmelCase_ = False )    ->List[Any]:
   UpperCAmelCase      					=  [os.path.join(lowerCAmelCase_	,    lowerCAmelCase_ ) for f in os.listdir(lowerCAmelCase_ ) if f.endswith(""".py""" )]
   UpperCAmelCase      					=  [sort_auto_mapping(lowerCAmelCase_	,    overwrite=lowerCAmelCase_ ) for fname in fnames]

   if not overwrite and any(lowerCAmelCase_ ):
      UpperCAmelCase      					=  [f for f, d in zip(lowerCAmelCase_	,    lowerCAmelCase_ ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {', '.join(lowerCAmelCase_ )}. 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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a	 =      {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ReformerAttention""",
     """ReformerForMaskedLM""",
     """ReformerForQuestionAnswering""",
     """ReformerForSequenceClassification""",
     """ReformerLayer""",
     """ReformerModel""",
     """ReformerModelWithLMHead""",
     """ReformerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

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



