

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 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[Any]:



    '''simple docstring'''






    lowerCAmelCase :	Optional[int]				=					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 :	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." )

    lowerCAmelCase :	int				=					components[:-1] + [test_fn.replace('.py',					'' )]
    lowerCAmelCase :	Optional[Any]				=					'.'.join(a__ )

    return test_module_path
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       List[str]:



    '''simple docstring'''






    lowerCAmelCase :	Optional[Any]				=					get_module_path(a__ )
    lowerCAmelCase :	int				=					importlib.import_module(a__ )

    return test_module
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       List[str]:



    '''simple docstring'''






    lowerCAmelCase :	Tuple				=					[]
    lowerCAmelCase :	Optional[Any]				=					get_test_module(a__ )
    for attr in dir(a__ ):
        if attr.endswith('ModelTester' ):
            tester_classes.append(getattr(a__,					a__ ) )

    # sort with class names
    return sorted(a__,					key=lambda _UpperCAmelCase : x.__name__ )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[Any]:



    '''simple docstring'''






    lowerCAmelCase :	str				=					[]
    lowerCAmelCase :	Optional[int]				=					get_test_module(a__ )
    for attr in dir(a__ ):
        lowerCAmelCase :	Optional[int]				=					getattr(a__,					a__ )
        # (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 :	Any				=					getattr(a__,					'all_model_classes',					[] )
        if len(a__ ) > 0:
            test_classes.append(a__ )

    # sort with class names
    return sorted(a__,					key=lambda _UpperCAmelCase : x.__name__ )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[int]:



    '''simple docstring'''






    lowerCAmelCase :	Any				=					get_test_classes(a__ )
    lowerCAmelCase :	List[str]				=					set()
    for test_class in test_classes:
        model_classes.update(test_class.all_model_classes )

    # sort with class names
    return sorted(a__,					key=lambda _UpperCAmelCase : x.__name__ )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       List[Any]:



    '''simple docstring'''






    lowerCAmelCase :	int				=					test_class()
    if hasattr(a__,					'setUp' ):
        test.setUp()

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

    return model_tester
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase )    ->       Optional[Any]:



    '''simple docstring'''






    lowerCAmelCase :	int				=					get_test_classes(a__ )

    lowerCAmelCase :	Union[str, Any]				=					[]
    for test_class in test_classes:
        if model_class in test_class.all_model_classes:
            target_test_classes.append(a__ )

    # sort with class names
    return sorted(a__,					key=lambda _UpperCAmelCase : x.__name__ )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase )    ->       Optional[int]:



    '''simple docstring'''






    lowerCAmelCase :	Dict				=					get_test_classes_for_model(a__,					a__ )

    lowerCAmelCase :	Tuple				=					[]
    for test_class in test_classes:
        lowerCAmelCase :	Optional[Any]				=					get_model_tester_from_test_class(a__ )
        if tester_class is not None:
            tester_classes.append(a__ )

    # sort with class names
    return sorted(a__,					key=lambda _UpperCAmelCase : x.__name__ )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[Any]:



    '''simple docstring'''






    lowerCAmelCase :	List[str]				=					get_test_classes(a__ )
    lowerCAmelCase :	str				=					{test_class: get_model_tester_from_test_class(a__ ) for test_class in test_classes}
    return test_tester_mapping
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       List[str]:



    '''simple docstring'''






    lowerCAmelCase :	Dict				=					get_model_classes(a__ )
    lowerCAmelCase :	Tuple				=					{
        model_class: get_test_classes_for_model(a__,					a__ ) for model_class in model_classes
    }
    return model_test_mapping
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Union[str, Any]:



    '''simple docstring'''






    lowerCAmelCase :	int				=					get_model_classes(a__ )
    lowerCAmelCase :	Optional[int]				=					{
        model_class: get_tester_classes_for_model(a__,					a__ ) for model_class in model_classes
    }
    return model_to_tester_mapping






def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Union[str, Any]:



    '''simple docstring'''






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







import warnings

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







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    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__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								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:
												__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if images is not None:
												__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if text is not None and images is not None:
												__a										=    image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_snake_case				)		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								return self.tokenizer.batch_decode(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




								return self.tokenizer.decode(*_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class