
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




 '''simple docstring'''





 snake_case_    =					["image_processor", "tokenizer"]
 snake_case_    =					"BlipImageProcessor"
 snake_case_    =					("BertTokenizer", "BertTokenizerFast")






 def __init__(	self :			Dict							,A :			List[str]							,A :			Optional[Any]			):
     __A             =			False
     super().__init__(A							,A			)
     __A             =			self.image_processor






 def __call__(	self :			Any							,A :			ImageInput = None							,A :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,A :			bool = True							,A :			Union[bool, str, PaddingStrategy] = False							,A :			Union[bool, str, TruncationStrategy] = None							,A :			Optional[int] = None							,A :			int = 0							,A :			Optional[int] = None							,A :			Optional[bool] = None							,A :			bool = False							,A :			bool = False							,A :			bool = False							,A :			bool = False							,A :			bool = False							,A :			bool = True							,A :			Optional[Union[str, TensorType]] = None							,**A :			Optional[Any]							,):
     if images is None and text is None:
         raise ValueError("You have to specify either images or text."			)

     # Get only text
     if images is None:
         __A             =			self.tokenizer
         __A             =			self.tokenizer(
             text=A							,add_special_tokens=A							,padding=A							,truncation=A							,max_length=A							,stride=A							,pad_to_multiple_of=A							,return_attention_mask=A							,return_overflowing_tokens=A							,return_special_tokens_mask=A							,return_offsets_mapping=A							,return_token_type_ids=A							,return_length=A							,verbose=A							,return_tensors=A							,**A							,)
         return text_encoding

     # add pixel_values
     __A             =			self.image_processor(A							,return_tensors=A			)

     if text is not None:
         __A             =			self.tokenizer(
             text=A							,add_special_tokens=A							,padding=A							,truncation=A							,max_length=A							,stride=A							,pad_to_multiple_of=A							,return_attention_mask=A							,return_overflowing_tokens=A							,return_special_tokens_mask=A							,return_offsets_mapping=A							,return_token_type_ids=A							,return_length=A							,verbose=A							,return_tensors=A							,**A							,)
     else:
         __A             =			None

     if text_encoding is not None:
         encoding_image_processor.update(A			)

     return encoding_image_processor






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






 def     UpperCamelCase_  (	self :			Tuple							,*A :			int							,**A :			Dict			):
     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			)			)


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			)
