

"""simple docstring"""




from typing import TYPE_CHECKING

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


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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








"""simple docstring"""




import argparse
import os
import re

import packaging.version


__UpperCAmelCase =				'examples/'
__UpperCAmelCase =				{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__UpperCAmelCase =				{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
__UpperCAmelCase =				'README.md'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 with open(__UpperCamelCase							,    """r"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  UpperCAmelCase__					:  Dict					   =					f.read()
 UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					REPLACE_PATTERNS[pattern]
 UpperCAmelCase__					:  List[str]					   =					replace.replace("""VERSION"""							,    __UpperCamelCase      )
 UpperCAmelCase__					:  Tuple					   =					re_pattern.sub(__UpperCamelCase							,    __UpperCamelCase      )
 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  f.write(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for folder, directories, fnames in os.walk(__UpperCamelCase      ):
  # Removing some of the folders with non-actively maintained examples from the walk
  if "research_projects" in directories:
   directories.remove("""research_projects"""      )
  if "legacy" in directories:
   directories.remove("""legacy"""      )
  for fname in fnames:
   if fname.endswith(""".py"""      ):
    update_version_in_file(os.path.join(__UpperCamelCase							,    __UpperCamelCase      )							,    __UpperCamelCase							,    pattern="""examples"""      )
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=False      ):
 '''simple docstring'''





 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      )
 if not patch:
  update_version_in_examples(__UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					"""🤗 Transformers currently provides the following architectures"""
 UpperCAmelCase__					:  Union[str, Any]					   =					"""1. Want to contribute a new model?"""
 with open(__UpperCamelCase							,    """r"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  UpperCAmelCase__					:  str					   =					f.readlines()

 # Find the start of the list.
 UpperCAmelCase__					:  List[Any]					   =					0
 while not lines[start_index].startswith(_start_prompt      ):
  start_index += 1
 start_index += 1

 UpperCAmelCase__					:  int					   =					start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt      ):
  if lines[index].startswith("""1."""      ):
   UpperCAmelCase__					:  List[Any]					   =					lines[index].replace(
       """https://huggingface.co/docs/transformers/main/model_doc"""							,    """https://huggingface.co/docs/transformers/model_doc"""							,    )
  index += 1

 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  f.writelines(__UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 with open(REPLACE_FILES["""init"""]							,    """r"""      ) as f:
  UpperCAmelCase__					:  Optional[Any]					   =					f.read()
 UpperCAmelCase__					:  int					   =					REPLACE_PATTERNS["""init"""][0].search(__UpperCamelCase      ).groups()[0]
 return packaging.version.parse(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase=False      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					get_version()
 if patch and default_version.is_devrelease:
  raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
 if default_version.is_devrelease:
  UpperCAmelCase__					:  List[str]					   =					default_version.base_version
 elif patch:
  UpperCAmelCase__					:  Any					   =					F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  UpperCAmelCase__					:  Optional[Any]					   =					F"{default_version.major}.{default_version.minor + 1}.0"

 # Now let's ask nicely if that's the right one.
 UpperCAmelCase__					:  Optional[int]					   =					input(F"Which version are you releasing? [{default_version}]"      )
 if len(__UpperCamelCase      ) == 0:
  UpperCAmelCase__					:  Union[str, Any]					   =					default_version

 print(F"Updating version to {version}."      )
 global_version_update(__UpperCamelCase							,    patch=__UpperCamelCase      )
 if not patch:
  print("""Cleaning main README, don't forget to run `make fix-copies`."""      )
  clean_main_ref_in_model_list()
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					get_version()
 UpperCAmelCase__					:  Any					   =					F"{current_version.major}.{current_version.minor + 1}.0.dev0"
 UpperCAmelCase__					:  List[Any]					   =					current_version.base_version

 # Check with the user we got that right.
 UpperCAmelCase__					:  Dict					   =					input(F"Which version are we developing now? [{dev_version}]"      )
 if len(__UpperCamelCase      ) == 0:
  UpperCAmelCase__					:  List[Any]					   =					dev_version

 print(F"Updating version to {version}."      )
 global_version_update(__UpperCamelCase      )
 print("""Cleaning main README, don't forget to run `make fix-copies`."""      )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()
      parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
      parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
      __UpperCAmelCase =				parser.parse_args()
      if not args.post_release:
            pre_release_work(patch=args.patch)
      elif args.patch:
            print('Nothing to do after a patch :-)')
      else:
            post_release_work()





