
"""simple docstring"""

import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')




def      lowercase__    (       lowerCAmelCase					:       List[Any]			)			->   List[Any]:






    """simple docstring"""




    UpperCAmelCase          =       test_file.split(os.path.sep			)
    if components[0:2] != ["tests", "models"]:
        raise ValueError(
            '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
            F"{test_file} instead."			)
    UpperCAmelCase          =       components[-1]
    if not test_fn.endswith('py'			):
        raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."			)
    if not test_fn.startswith('test_modeling_'			):
        raise ValueError(
            F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."			)

    UpperCAmelCase          =       components[:-1] + [test_fn.replace('.py'			,					''			)]
    UpperCAmelCase          =       '.'.join(_UpperCamelCase			)

    return test_module_path




def      lowercase__    (       lowerCAmelCase					:       List[Any]			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       get_module_path(_UpperCamelCase			)
    UpperCAmelCase          =       importlib.import_module(_UpperCamelCase			)

    return test_module




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       get_test_module(_UpperCamelCase			)
    for attr in dir(_UpperCamelCase			):
        if attr.endswith('ModelTester'			):
            tester_classes.append(getattr(_UpperCamelCase			,					_UpperCamelCase			)			)

    # sort with class names
    return sorted(_UpperCamelCase			,					key=lambda lowerCAmelCase			: x.__name__			)




def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			)			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       get_test_module(_UpperCamelCase			)
    for attr in dir(_UpperCamelCase			):
        UpperCAmelCase          =       getattr(_UpperCamelCase			,					_UpperCamelCase			)
        # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
        # `all_model_classes` is not empty (which also excludes other special classes).
        UpperCAmelCase          =       getattr(_UpperCamelCase			,					'all_model_classes'			,					[]			)
        if len(_UpperCamelCase			) > 0:
            test_classes.append(_UpperCamelCase			)

    # sort with class names
    return sorted(_UpperCamelCase			,					key=lambda lowerCAmelCase			: x.__name__			)




def      lowercase__    (       lowerCAmelCase					:       Dict			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       get_test_classes(_UpperCamelCase			)
    UpperCAmelCase          =       set()
    for test_class in test_classes:
        model_classes.update(test_class.all_model_classes			)

    # sort with class names
    return sorted(_UpperCamelCase			,					key=lambda lowerCAmelCase			: x.__name__			)




def      lowercase__    (       lowerCAmelCase					:       Dict			)			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase          =       test_class()
    if hasattr(_UpperCamelCase			,					'setUp'			):
        test.setUp()

    UpperCAmelCase          =       None
    if hasattr(_UpperCamelCase			,					'model_tester'			):
        # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
        if test.model_tester is not None:
            UpperCAmelCase          =       test.model_tester.__class__

    return model_tester




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Optional[Any]			)			->   Dict:






    """simple docstring"""




    UpperCAmelCase          =       get_test_classes(_UpperCamelCase			)

    UpperCAmelCase          =       []
    for test_class in test_classes:
        if model_class in test_class.all_model_classes:
            target_test_classes.append(_UpperCamelCase			)

    # sort with class names
    return sorted(_UpperCamelCase			,					key=lambda lowerCAmelCase			: x.__name__			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Optional[int]			)			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase          =       get_test_classes_for_model(_UpperCamelCase			,					_UpperCamelCase			)

    UpperCAmelCase          =       []
    for test_class in test_classes:
        UpperCAmelCase          =       get_model_tester_from_test_class(_UpperCamelCase			)
        if tester_class is not None:
            tester_classes.append(_UpperCamelCase			)

    # sort with class names
    return sorted(_UpperCamelCase			,					key=lambda lowerCAmelCase			: x.__name__			)




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       get_test_classes(_UpperCamelCase			)
    UpperCAmelCase          =       {test_class: get_model_tester_from_test_class(_UpperCamelCase			) for test_class in test_classes}
    return test_tester_mapping




def      lowercase__    (       lowerCAmelCase					:       str			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       get_model_classes(_UpperCamelCase			)
    UpperCAmelCase          =       {
        model_class: get_test_classes_for_model(_UpperCamelCase			,					_UpperCamelCase			) for model_class in model_classes
    }
    return model_test_mapping




def      lowercase__    (       lowerCAmelCase					:       Any			)			->   List[str]:






    """simple docstring"""




    UpperCAmelCase          =       get_model_classes(_UpperCamelCase			)
    UpperCAmelCase          =       {
        model_class: get_tester_classes_for_model(_UpperCamelCase			,					_UpperCamelCase			) for model_class in model_classes
    }
    return model_to_tester_mapping







def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			)			->   Tuple:






    """simple docstring"""




    if isinstance(_UpperCamelCase			,					_UpperCamelCase			):
        return o
    elif isinstance(_UpperCamelCase			,					_UpperCamelCase			):
        return o.__name__
    elif isinstance(_UpperCamelCase			,					(list, tuple)			):
        return [to_json(_UpperCamelCase			) for x in o]
    elif isinstance(_UpperCamelCase			,					_UpperCamelCase			):
        return {to_json(_UpperCamelCase			): to_json(_UpperCamelCase			) for k, v in o.items()}
    else:
        return o









'''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_		:     Dict          =		{
    '''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_		:     Union[str, Any]          =		['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Tuple          =		[
       '''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_		:     str          =		[
       '''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_		:     Tuple          =		[
       '''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_		:     Optional[int]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


