






"""simple docstring"""





import warnings

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


class      _lowercase (     lowerCAmelCase						):
      _a   :     Tuple								=   ['''image_processor''', '''tokenizer''']
      _a   :     List[Any]								=   '''ViTImageProcessor'''
      _a   :     Optional[Any]								=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')


      def __init__(    self				:	str							,   a				:	Dict=None							,   a				:	Dict=None							,   **a				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    str  			=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							,   )
                  __snake_case      :    int  			=kwargs.pop('''feature_extractor'''						)

            __snake_case      :    int  			=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				:	str							,   a				:	str=None							,   a				:	Any=None							,   a				:	Any=None							,   a				:	str=None							,   **a				:	List[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:
                  __snake_case      :    Dict  			=self.tokenizer(a							,   return_tensors=a							,   **a						)

            if visual_prompt is not None:
                  __snake_case      :    Union[str, Any]  			=self.image_processor(a							,   return_tensors=a							,   **a						)

            if images is not None:
                  __snake_case      :    Union[str, Any]  			=self.image_processor(a							,   return_tensors=a							,   **a						)

            if visual_prompt is not None and images is not None:
                  __snake_case      :    Tuple  			={
                      '''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:
                  __snake_case      :    Dict  			=image_features.pixel_values
                  return encoding
            elif text is not None:
                  return encoding
            elif visual_prompt is not None:
                  __snake_case      :    Tuple  			={
                      '''conditional_pixel_values''': prompt_features.pixel_values,
                  }
                  return encoding
            else:
                  return BatchEncoding(data=dict(**a						)							,   tensor_type=a						)


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   *a				:	Union[str, Any]							,   **a				:	Tuple						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	int							,   *a				:	Tuple							,   **a				:	int						):





            """simple docstring"""







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


      @property
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            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 _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







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










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_	:				Union[str, Any]										= {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


