




import argparse
from collections import defaultdict






def 					snake_case(				__magic_name__ ,		__magic_name__ ,		__magic_name__ ,		__magic_name__ ,		__magic_name__    ) ->							Dict:





     '''simple docstring'''


     lowercase					:   Optional[Any]								= F"""{file}_{class_name}_{test_name}"""
     done_test[_id] += 1

     with open(_UpperCAmelCase ,		'''r'''    ) as f:
          lowercase					:   Union[str, Any]								= f.readlines()

     lowercase					:   List[Any]								= F"""class {class_name}("""
     lowercase					:   int								= F"""{4 * ' '}def {test_name}("""
     lowercase					:   Union[str, Any]								= F"""{8 * ' '}{correct_line.split()[0]}"""
     lowercase					:   Union[str, Any]								= F"""{16 * ' '}{correct_line.split()[0]}"""
     lowercase					:   Union[str, Any]								= False
     lowercase					:   Optional[Any]								= False
     lowercase					:   List[str]								= False
     lowercase					:   Optional[Any]								= False
     lowercase					:   List[Any]								= 0
     lowercase					:   str								= 0

     lowercase					:   Tuple								= []
     for line in lines:
          if line.startswith(_UpperCAmelCase    ):
               lowercase					:   List[Any]								= True
          elif in_class and line.startswith(_UpperCAmelCase    ):
               lowercase					:   List[str]								= True
          elif in_class and in_func and (line.startswith(_UpperCAmelCase    ) or line.startswith(_UpperCAmelCase    )):
               lowercase					:   Dict								= len(line.split(correct_line.split()[0]    )[0]    )
               count += 1

               if count == done_test[_id]:
                    lowercase					:   Tuple								= True

          if in_class and in_func and in_line:
               if ")" not in line:
                    continue
               else:
                    lowercase					:   str								= True

          if in_class and in_func and in_line and insert_line:
               new_lines.append(F"""{spaces * ' '}{correct_line}"""    )
               lowercase					:   str								= False
          else:
               new_lines.append(_UpperCAmelCase    )

     with open(_UpperCAmelCase ,		'''w'''    ) as f:
          for line in new_lines:
               f.write(_UpperCAmelCase    )






def 					snake_case(				__magic_name__ ,		__magic_name__=None    ) ->							Optional[int]:





     '''simple docstring'''


     if fail is not None:
          with open(_UpperCAmelCase ,		'''r'''    ) as f:
               lowercase					:   Any								= {l.strip() for l in f.readlines()}
     else:
          lowercase					:   int								= None

     with open(_UpperCAmelCase ,		'''r'''    ) as f:
          lowercase					:   Any								= f.readlines()

     lowercase					:   Any								= defaultdict(_UpperCAmelCase    )
     for line in correct_lines:
          lowercase					:   Optional[Any]								= line.split(''';'''    )
          if test_failures is None or "::".join([file, class_name, test_name]    ) in test_failures:
               overwrite_file(_UpperCAmelCase ,		_UpperCAmelCase ,		_UpperCAmelCase ,		_UpperCAmelCase ,		_UpperCAmelCase    )


if __name__ == "__main__":
 lowerCAmelCase_		      = argparse.ArgumentParser()
 parser.add_argument('--correct_filename', help='filename of tests with expected result')
 parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
 lowerCAmelCase_		      = parser.parse_args()

 main(args.correct_filename, args.fail_filename)
'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Tuple     =  ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Dict     =  [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE       :     Any     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

