





import warnings

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







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      ["""image_processor""", """tokenizer"""]
      _lowercase    : Optional[int]            =      """CLIPImageProcessor"""
      _lowercase    : Optional[int]            =      ("""CLIPTokenizer""", """CLIPTokenizerFast""")







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, 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."		,				lowerCAmelCase__		,				)
          a__     : 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__(lowerCAmelCase__		,				lowerCAmelCase__							)







      def __call__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Union[str, 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__     : List[str]  =self.tokenizer(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)

        if images is not None:
          a__     : Any  =self.image_processor(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)

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







      def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						Dict:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




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







      @property
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Optional[int]  =self.tokenizer.model_input_names
        a__     : Any  =self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)







      @property
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




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







      @property
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




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













import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase__)
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      field(default="""image-classification"""						,    metadata={"""include_in_asdict_even_if_is_default""": True})
      _lowercase    : ClassVar[Features]            =      Features({"""image""": Image()})
      _lowercase    : ClassVar[Features]            =      Features({"""labels""": ClassLabel})
      _lowercase    : str            =      "image"
      _lowercase    : str            =      "labels"







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        if self.label_column not in features:
          raise ValueError(F'''Column {self.label_column} is not present in features.'''							)
        if not isinstance(features[self.label_column]		,				lowerCAmelCase__							):
          raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''							)
        a__     : int  =copy.deepcopy(self							)
        a__     : Dict  =self.label_schema.copy()
        a__     : int  =features[self.label_column]
        a__     : str  =label_schema
        return task_template







      @property
      def        _lowercase  (   self							)       ->						Dict[str, str]:



        '''simple docstring'''




        return {
            self.image_column: "image",
            self.label_column: "labels",
        }






