
"""simple docstring"""



import warnings

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




class 		a_						( _UpperCAmelCase	):
    a      :				Optional[Any]     =				['image_processor', 'tokenizer']
    a      :				Any     =				'ViTImageProcessor'
    a      :				Dict     =				('CLIPTokenizer', 'CLIPTokenizerFast')







    def __init__(			self			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       List[str]=None     ,					**__UpperCamelCase			:       Optional[int]     )		->str:






        '''simple docstring'''






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

        _UpperCAmelCase          =   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__(__UpperCamelCase     ,					__UpperCamelCase     )







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






        '''simple docstring'''






        if text is None and visual_prompt is None and images is None:
            raise ValueError("""You have to specify either text, visual prompt or images."""     )

        if text is not None and visual_prompt is not None:
            raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""     )

        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )

        if visual_prompt is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					return_tensors=__UpperCamelCase     ,					**__UpperCamelCase     )

        if visual_prompt is not None and images is not None:
            _UpperCAmelCase          =   {
                """pixel_values""": image_features.pixel_values,
                """conditional_pixel_values""": prompt_features.pixel_values,
            }
            return encoding
        elif text is not None and images is not None:
            _UpperCAmelCase          =   image_features.pixel_values
            return encoding
        elif text is not None:
            return encoding
        elif visual_prompt is not None:
            _UpperCAmelCase          =   {
                """conditional_pixel_values""": prompt_features.pixel_values,
            }
            return encoding
        else:
            return BatchEncoding(data=dict(**__UpperCamelCase     )     ,					tensor_type=__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					*__UpperCamelCase			:       List[Any]     ,					**__UpperCamelCase			:       str     )		->Optional[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Any     ,					*__UpperCamelCase			:       Any     ,					**__UpperCamelCase			:       Union[str, Any]     )		->Optional[Any]:






        '''simple docstring'''






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







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






        '''simple docstring'''






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







    @property
    def      _snake_case					(			self			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor

"""simple docstring"""



from __future__ import annotations


def  _UpperCamelCase	(						_A      )    ->  None:





    """simple docstring"""

    create_state_space_tree(_A					,      []					,      0					,      [0 for i in range(len(_A      )      )]      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  None:





    """simple docstring"""

    if index == len(_A      ):
        print(_A      )
        return

    for i in range(len(_A      )      ):
        if not index_used[i]:
            current_sequence.append(sequence[i]      )
            _UpperCAmelCase          =   True
            create_state_space_tree(_A					,      _A					,      index + 1					,      _A      )
            current_sequence.pop()
            _UpperCAmelCase          =   False
a					:	list[int | str]							=		[3, 1, 2, 4]
generate_all_permutations(sequence)

a					:	list[int | str]							=		["A", "B", "C"]







generate_all_permutations(sequence_a)