






"""simple docstring"""







import argparse
from collections import defaultdict

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	F'''{file}_{class_name}_{test_name}'''
      done_test[_id] += 1

      with open(_snake_case				,	"""r"""							) as f:
            UpperCAmelCase          =	f.readlines()

      UpperCAmelCase          =	F'''class {class_name}('''
      UpperCAmelCase          =	F'''{4 * ' '}def {test_name}('''
      UpperCAmelCase          =	F'''{8 * ' '}{correct_line.split()[0]}'''
      UpperCAmelCase          =	F'''{16 * ' '}{correct_line.split()[0]}'''
      UpperCAmelCase          =	False
      UpperCAmelCase          =	False
      UpperCAmelCase          =	False
      UpperCAmelCase          =	False
      UpperCAmelCase          =	0
      UpperCAmelCase          =	0

      UpperCAmelCase          =	[]
      for line in lines:
            if line.startswith(_snake_case							):
                  UpperCAmelCase          =	True
            elif in_class and line.startswith(_snake_case							):
                  UpperCAmelCase          =	True
            elif in_class and in_func and (line.startswith(_snake_case							) or line.startswith(_snake_case							)):
                  UpperCAmelCase          =	len(line.split(correct_line.split()[0]							)[0]							)
                  count += 1

                  if count == done_test[_id]:
                        UpperCAmelCase          =	True

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

            if in_class and in_func and in_line and insert_line:
                  new_lines.append(F'''{spaces * ' '}{correct_line}'''							)
                  UpperCAmelCase          =	UpperCAmelCase          =	UpperCAmelCase          =	UpperCAmelCase          =	False
            else:
                  new_lines.append(_snake_case							)

      with open(_snake_case				,	"""w"""							) as f:
            for line in new_lines:
                  f.write(_snake_case							)

def _a			(		_snake_case				,	_snake_case=None							):







      """simple docstring"""






      if fail is not None:
            with open(_snake_case				,	"""r"""							) as f:
                  UpperCAmelCase          =	{l.strip() for l in f.readlines()}
      else:
            UpperCAmelCase          =	None

      with open(_snake_case				,	"""r"""							) as f:
            UpperCAmelCase          =	f.readlines()

      UpperCAmelCase          =	defaultdict(_snake_case							)
      for line in correct_lines:
            UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	line.split(""";"""							)
            if test_failures is None or "::".join([file, class_name, test_name]							) in test_failures:
                  overwrite_file(_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							)


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__)



