'''simple docstring'''

import warnings

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







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE	:			Optional[Any]       =   ["""image_processor""", """tokenizer"""]
 _SCREAMING_SNAKE_CASE	:			List[str]       =   """CLIPImageProcessor"""
 _SCREAMING_SNAKE_CASE	:			Dict       =   ("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__( self  :      int ,			SCREAMING_SNAKE_CASE__  :      List[str]=None ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=None ,			**SCREAMING_SNAKE_CASE__  :      Tuple				)    ->       str:
  __lowerCAmelCase											=		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_ ,			)
   __lowerCAmelCase											=		kwargs.pop("""feature_extractor"""				)

  __lowerCAmelCase											=		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  :      Any ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]=None ,			SCREAMING_SNAKE_CASE__  :      str=None ,			SCREAMING_SNAKE_CASE__  :      Optional[int]=None ,			**SCREAMING_SNAKE_CASE__  :      int				)    ->       str:

  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:
   __lowerCAmelCase											=		self.tokenizer(A_ ,			return_tensors=A_ ,			**A_				)

  if images is not None:
   __lowerCAmelCase											=		self.image_processor(A_ ,			return_tensors=A_ ,			**A_				)

  if text is not None and images is not None:
   __lowerCAmelCase											=		image_features.pixel_values
   return encoding
  elif text is not None:
   return encoding
  else:
   return BatchEncoding(data=dict(**A_				) ,			tensor_type=A_				)
 def a   ( self  :      Any ,			*SCREAMING_SNAKE_CASE__  :      Optional[int] ,			**SCREAMING_SNAKE_CASE__  :      Tuple				)    ->       List[Any]:
  return self.tokenizer.batch_decode(*A_ ,			**A_				)
 def a   ( self  :      Dict ,			*SCREAMING_SNAKE_CASE__  :      Any ,			**SCREAMING_SNAKE_CASE__  :      Any				)    ->       Dict:
  return self.tokenizer.decode(*A_ ,			**A_				)
 @property
 def a   ( self  :      Union[str, Any]				)    ->       Union[str, Any]:
  __lowerCAmelCase											=		self.tokenizer.model_input_names
  __lowerCAmelCase											=		self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def a   ( self  :      Dict				)    ->       Union[str, Any]:
  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 a   ( self  :      List[str]				)    ->       int:
  warnings.warn(
      """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,			A_ ,			)
  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=A     )
class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    field(default='''image-classification'''							,	metadata={'''include_in_asdict_even_if_is_default''': True}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''image''': Image()}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''labels''': ClassLabel}     )
     __SCREAMING_SNAKE_CASE     					=    "image"
     __SCREAMING_SNAKE_CASE     					=    "labels"


     def        __snake_case(  self							,					A_    ):
       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]							,					A_    ):
         raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''    )
       _UpperCAmelCase		:							Tuple							=     copy.deepcopy(self    )
       _UpperCAmelCase		:							str							=     self.label_schema.copy()
       _UpperCAmelCase		:							Optional[Any]							=     features[self.label_column]
       _UpperCAmelCase		:							int							=     label_schema
       return task_template




     @property
     def        __snake_case(  self    ):
       return {
           self.image_column: "image",
           self.label_column: "labels",
       }





