


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     __lowerCAmelCase						(							snake_case	:    Tuple )       ->							Any:
   __lowerCamelCase:				str     =	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.' )
   __lowerCamelCase:				Optional[Any]     =	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.' )

   __lowerCamelCase:				Any     =	components[:-1] + [test_fn.replace(""".py"""						,      """""" )]
   __lowerCamelCase:				str     =	""".""".join(snake_case )

   return test_module_path



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Tuple:
   __lowerCamelCase:				str     =	get_module_path(snake_case )
   __lowerCamelCase:				str     =	importlib.import_module(snake_case )

   return test_module



def     __lowerCAmelCase						(							snake_case	:    str )       ->							List[str]:
   __lowerCamelCase:				Optional[int]     =	[]
   __lowerCamelCase:				Tuple     =	get_test_module(snake_case )
   for attr in dir(snake_case ):
      if attr.endswith("""ModelTester""" ):
         tester_classes.append(getattr(snake_case						,      snake_case ) )

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



def     __lowerCAmelCase						(							snake_case	:    int )       ->							Tuple:
   __lowerCamelCase:				List[str]     =	[]
   __lowerCamelCase:				List[str]     =	get_test_module(snake_case )
   for attr in dir(snake_case ):
      __lowerCamelCase:				Optional[int]     =	getattr(snake_case						,      snake_case )
      # (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).
      __lowerCamelCase:				Optional[int]     =	getattr(snake_case						,      """all_model_classes"""						,      [] )
      if len(snake_case ) > 0:
         test_classes.append(snake_case )

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



def     __lowerCAmelCase						(							snake_case	:    List[Any] )       ->							Dict:
   __lowerCamelCase:				List[str]     =	get_test_classes(snake_case )
   __lowerCamelCase:				Optional[int]     =	set()
   for test_class in test_classes:
      model_classes.update(test_class.all_model_classes )

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



def     __lowerCAmelCase						(							snake_case	:    Optional[int] )       ->							List[Any]:
   __lowerCamelCase:				Optional[int]     =	test_class()
   if hasattr(snake_case						,      """setUp""" ):
      test.setUp()

   __lowerCamelCase:				Optional[int]     =	None
   if hasattr(snake_case						,      """model_tester""" ):
      # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
      if test.model_tester is not None:
         __lowerCamelCase:				List[Any]     =	test.model_tester.__class__

   return model_tester



def     __lowerCAmelCase						(							snake_case	:    Optional[Any]						,      snake_case	:    List[Any] )       ->							str:
   __lowerCamelCase:				int     =	get_test_classes(snake_case )

   __lowerCamelCase:				List[str]     =	[]
   for test_class in test_classes:
      if model_class in test_class.all_model_classes:
         target_test_classes.append(snake_case )

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



def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Dict )       ->							Optional[int]:
   __lowerCamelCase:				Dict     =	get_test_classes_for_model(snake_case						,      snake_case )

   __lowerCamelCase:				List[Any]     =	[]
   for test_class in test_classes:
      __lowerCamelCase:				str     =	get_model_tester_from_test_class(snake_case )
      if tester_class is not None:
         tester_classes.append(snake_case )

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



def     __lowerCAmelCase						(							snake_case	:    List[Any] )       ->							Dict:
   __lowerCamelCase:				Optional[int]     =	get_test_classes(snake_case )
   __lowerCamelCase:				str     =	{test_class: get_model_tester_from_test_class(snake_case ) for test_class in test_classes}
   return test_tester_mapping



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Optional[int]:
   __lowerCamelCase:				Tuple     =	get_model_classes(snake_case )
   __lowerCamelCase:				str     =	{
       model_class: get_test_classes_for_model(snake_case						,      snake_case ) for model_class in model_classes
   }
   return model_test_mapping



def     __lowerCAmelCase						(							snake_case	:    Optional[int] )       ->							Any:
   __lowerCamelCase:				List[str]     =	get_model_classes(snake_case )
   __lowerCamelCase:				int     =	{
       model_class: get_tester_classes_for_model(snake_case						,      snake_case ) for model_class in model_classes
   }
   return model_to_tester_mapping







def     __lowerCAmelCase						(							snake_case	:    Tuple )       ->							Tuple:
   if isinstance(snake_case						,      snake_case ):
      return o
   elif isinstance(snake_case						,      snake_case ):
      return o.__name__
   elif isinstance(snake_case						,      (list, tuple) ):
      return [to_json(snake_case ) for x in o]
   elif isinstance(snake_case						,      snake_case ):
      return {to_json(snake_case ): to_json(snake_case ) for k, v in o.items()}
   else:
      return o






from typing import TYPE_CHECKING

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


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


