





"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__							:Tuple	     =					"CLIPImageProcessor"
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					("CLIPTokenizer", "CLIPTokenizerFast")

  def __init__(		self   :       int					,   __a   :       Any=None					,   __a   :       Optional[Any]=None					,   **__a   :       Any	)		->				Optional[int]:
        _UpperCamelCase      :	List[Any]						=       None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."					,   __a					,   )
              _UpperCamelCase      :	Optional[Any]						=       kwargs.pop("feature_extractor"	)

        _UpperCamelCase      :	List[str]						=       image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."	)
        if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."	)

        super().__init__(__a					,   __a	)

  def __call__(		self   :       Any					,   __a   :       int=None					,   __a   :       Optional[int]=None					,   __a   :       List[Any]=None					,   **__a   :       Union[str, Any]	)		->				Union[str, Any]:

        if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none."	)

        if text is not None:
              _UpperCamelCase      :	List[str]						=       self.tokenizer(__a					,   return_tensors=__a					,   **__a	)

        if images is not None:
              _UpperCamelCase      :	List[Any]						=       self.image_processor(__a					,   return_tensors=__a					,   **__a	)

        if text is not None and images is not None:
              _UpperCamelCase      :	Union[str, Any]						=       image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a	)					,   tensor_type=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   *__a   :       int					,   **__a   :       Tuple	)		->				int:
        return self.tokenizer.batch_decode(*__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   *__a   :       Tuple					,   **__a   :       int	)		->				int:
        return self.tokenizer.decode(*__a					,   **__a	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       self.tokenizer.model_input_names
        _UpperCamelCase      :	List[str]						=       self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,   __a					,   )
        return self.image_processor_class


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				List[str]:
        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,   __a					,   )
        return self.image_processor







"""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__    (					lowercase_			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       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      :	str						=       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      :	Dict						=       components[:-1] + [test_fn.replace(".py"		,""			)]
      _UpperCamelCase      :	List[str]						=       ".".join(lowercase_			)

      return test_module_path




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




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       get_module_path(lowercase_			)
      _UpperCamelCase      :	str						=       importlib.import_module(lowercase_			)

      return test_module




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       []
      _UpperCamelCase      :	List[Any]						=       get_test_module(lowercase_			)
      for attr in dir(lowercase_			):
            if attr.endswith("ModelTester"			):
                  tester_classes.append(getattr(lowercase_		,lowercase_			)			)

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




def 					lowercase__    (					lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       []
      _UpperCamelCase      :	Any						=       get_test_module(lowercase_			)
      for attr in dir(lowercase_			):
            _UpperCamelCase      :	int						=       getattr(lowercase_		,lowercase_			)
            # (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      :	Optional[Any]						=       getattr(lowercase_		,"all_model_classes"		,[]			)
            if len(lowercase_			) > 0:
                  test_classes.append(lowercase_			)

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




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       get_test_classes(lowercase_			)
      _UpperCamelCase      :	int						=       set()
      for test_class in test_classes:
            model_classes.update(test_class.all_model_classes			)

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




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       test_class()
      if hasattr(lowercase_		,"setUp"			):
            test.setUp()

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

      return model_tester




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




      """simple docstring"""

      _UpperCamelCase      :	str						=       get_test_classes(lowercase_			)

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

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




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Dict:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       get_test_classes_for_model(lowercase_		,lowercase_			)

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

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




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




      """simple docstring"""

      _UpperCamelCase      :	Any						=       get_test_classes(lowercase_			)
      _UpperCamelCase      :	Tuple						=       {test_class: get_model_tester_from_test_class(lowercase_			) for test_class in test_classes}
      return test_tester_mapping




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




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       get_model_classes(lowercase_			)
      _UpperCamelCase      :	Optional[int]						=       {
          model_class: get_test_classes_for_model(lowercase_		,lowercase_			) for model_class in model_classes
      }
      return model_test_mapping




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




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       get_model_classes(lowercase_			)
      _UpperCamelCase      :	Tuple						=       {
          model_class: get_tester_classes_for_model(lowercase_		,lowercase_			) for model_class in model_classes
      }
      return model_to_tester_mapping




def 					lowercase__    (					lowercase_			)					->		Optional[int]:




      """simple docstring"""

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