




'''simple docstring'''



import argparse
from collections import defaultdict




def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						f"{file}_{class_name}_{test_name}"
      done_test[_id] += 1

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

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

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

                  if count == done_test[_id]:
                        SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						True

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

            if in_class and in_func and in_line and insert_line:
                  new_lines.append(f"{spaces * ' '}{correct_line}"    )
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False
            else:
                  new_lines.append(__UpperCamelCase    )

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




def   lowercase__(			__UpperCamelCase:					List[str]      ,__UpperCamelCase:					List[Any]=None    ):

      """simple docstring"""






      if fail is not None:
            with open(__UpperCamelCase      ,'r'    ) as f:
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{l.strip() for l in f.readlines()}
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None

      with open(__UpperCamelCase      ,'r'    ) as f:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						f.readlines()

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						defaultdict(__UpperCamelCase    )
      for line in correct_lines:
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      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__":
      UpperCamelCase_    	=				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)
      UpperCamelCase_    	=				parser.parse_args()

      main(args.correct_filename, args.fail_filename)








'''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_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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


