





"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCamelCase_	:		int         =   {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['DebertaV2TokenizerFast']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   [
       'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFDebertaV2ForMaskedLM',
       'TFDebertaV2ForQuestionAnswering',
       'TFDebertaV2ForSequenceClassification',
       'TFDebertaV2ForTokenClassification',
       'TFDebertaV2Model',
       'TFDebertaV2PreTrainedModel',
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DebertaV2ForMaskedLM',
       'DebertaV2ForMultipleChoice',
       'DebertaV2ForQuestionAnswering',
       'DebertaV2ForSequenceClassification',
       'DebertaV2ForTokenClassification',
       'DebertaV2Model',
       'DebertaV2PreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		Union[str, Any]         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_		:  int										=  '''src/transformers'''

# Matches is_xxx_available()
A_		:  Union[str, Any]										=  re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
A_		:  int										=  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A_		:  Tuple										=  re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


A_		:  str										=  '''
{0} = None
'''

A_		:  Tuple										=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


A_		:  Any										=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 if _re_test_backend.search(UpperCAmelCase__       ) is None:
  return None
 a							=		[b[0] for b in _re_backend.findall(UpperCAmelCase__       )]
 backends.sort()
 return "_and_".join(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 with open(os.path.join(UpperCAmelCase__			,       "__init__.py"       )			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
  a							=		f.readlines()

 # Get to the point we do the actual imports for type checking
 a							=		0
 while not lines[line_index].startswith("if TYPE_CHECKING"       ):
  line_index += 1

 a							=		{}
 # Go through the end of the file
 while line_index < len(UpperCAmelCase__       ):
  # If the line is an if is_backend_available, we grab all objects associated.
  a							=		find_backend(lines[line_index]       )
  if backend is not None:
   while not lines[line_index].startswith("    else:"       ):
    line_index += 1
   line_index += 1

   a							=		[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
    a							=		lines[line_index]
    a							=		_re_single_line_import.search(UpperCAmelCase__       )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(", "       )       )
    elif line.startswith(" " * 12       ):
     objects.append(line[12:-2]       )
    line_index += 1

   a							=		objects
  else:
   line_index += 1

 return backend_specific_objects


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 if name.isupper():
  return DUMMY_CONSTANT.format(UpperCAmelCase__       )
 elif name.islower():
  return DUMMY_FUNCTION.format(UpperCAmelCase__			,       UpperCAmelCase__       )
 else:
  return DUMMY_CLASS.format(UpperCAmelCase__			,       UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]=None       ):






 '''simple docstring'''

 if backend_specific_objects is None:
  a							=		read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 a							=		{}

 for backend, objects in backend_specific_objects.items():
  a							=		"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"       )       ) + "]"
  a							=		"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(UpperCAmelCase__			,       UpperCAmelCase__       ) for o in objects]       )
  a							=		dummy_file

 return dummy_files


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int=False       ):






 '''simple docstring'''

 a							=		create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 a							=		{"torch": "pt"}

 # Locate actual dummy modules and read their content.
 a							=		os.path.join(UpperCAmelCase__			,       "utils"       )
 a							=		{
     backend: os.path.join(UpperCAmelCase__			,       F"""dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py"""       )
     for backend in dummy_files.keys()
 }

 a							=		{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(UpperCAmelCase__       ):
   with open(UpperCAmelCase__			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
    a							=		f.read()
  else:
   a							=		""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"""Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py as the main """
        "__init__ has new objects."       )
    with open(dummy_file_paths[backend]			,       "w"			,       encoding="utf-8"			,       newline="\n"       ) as f:
     f.write(dummy_files[backend]       )
   else:
    raise ValueError(
        "The main __init__ has objects that are not present in "
        F"""transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py. Run `make fix-copies` """
        "to fix this."       )


if __name__ == "__main__":
     A_		:  Dict										=  argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     A_		:  List[str]										=  parser.parse_args()

     check_dummies(args.fix_and_overwrite)

