
import warnings

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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




 '''simple docstring'''





 snake_case_    =					["image_processor", "tokenizer"]
 snake_case_    =					"CLIPImageProcessor"
 snake_case_    =					("CLIPTokenizer", "CLIPTokenizerFast")






 def __init__(	self :			Union[str, Any]							,A :			Tuple=None							,A :			Any=None							,**A :			Dict			):
     __A             =			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							,)
         __A             =			kwargs.pop("feature_extractor"			)

     __A             =			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 :			Tuple							,A :			List[Any]=None							,A :			List[str]=None							,A :			str=None							,**A :			List[Any]			):




     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             =			self.tokenizer(A							,return_tensors=A							,**A			)

     if images is not None:
         __A             =			self.image_processor(A							,return_tensors=A							,**A			)

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






 def     UpperCamelCase_  (	self :			Union[str, Any]							,*A :			Union[str, Any]							,**A :			Optional[Any]			):
     return self.tokenizer.batch_decode(*A							,**A			)






 def     UpperCamelCase_  (	self :			List[Any]							,*A :			List[str]							,**A :			Optional[int]			):
     return self.tokenizer.decode(*A							,**A			)






 @property
 def     UpperCamelCase_  (	self :			Any			):
     __A             =			self.tokenizer.model_input_names
     __A             =			self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






 @property
 def     UpperCamelCase_  (	self :			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     UpperCamelCase_  (	self :			Optional[int]			):
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,A							,)
     return self.image_processor


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			int							,*A :			Dict							,**A :			Dict			):
					warnings.warn(
					    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
					    " use CLIPImageProcessor instead."							,A							,)
					super().__init__(*A							,**A			)
