





import warnings

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






class 		__snake_case (		__SCREAMING_SNAKE_CASE					):





 """simple docstring"""


 _lowerCamelCase				=					["""image_processor""", """tokenizer"""]
 _lowerCamelCase				=					"""ViTImageProcessor"""
 _lowerCamelCase				=					("""CLIPTokenizer""", """CLIPTokenizerFast""")

 def __init__(   self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Dict									=					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__ ,						)
         __A   :						List[Any]									=					kwargs.pop('''feature_extractor'''  )

     __A   :						Optional[Any]									=					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 ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase  ):



     '''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:
         __A   :						int									=					self.tokenizer(UpperCamelCase__ ,						return_tensors=UpperCamelCase__ ,						**UpperCamelCase__  )

     if visual_prompt is not None:
         __A   :						Dict									=					self.image_processor(UpperCamelCase__ ,						return_tensors=UpperCamelCase__ ,						**UpperCamelCase__  )

     if images is not None:
         __A   :						List[str]									=					self.image_processor(UpperCamelCase__ ,						return_tensors=UpperCamelCase__ ,						**UpperCamelCase__  )

     if visual_prompt is not None and images is not None:
         __A   :						List[str]									=					{
             '''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:
         __A   :						Dict									=					image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     elif visual_prompt is not None:
         __A   :						Optional[Any]									=					{
             '''conditional_pixel_values''': prompt_features.pixel_values,
         }
         return encoding
     else:
         return BatchEncoding(data=dict(**UpperCamelCase__  ) ,						tensor_type=UpperCamelCase__  )

 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 @property
 def  UpperCamelCase__(   self  ):



     '''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  UpperCamelCase__(   self  ):



     '''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 typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




