




"""simple docstring"""



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_  (						__lowercase							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''BlipImageProcessor'''
    lowercase__						 =				('''BertTokenizer''', '''BertTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	int				)				->							Union[str, Any]:




        '''simple docstring'''

        A__	=    False
        super().__init__(_a     ,			_a				)
        A__	=    self.image_processor




    def __call__(   self						:	Optional[int]     ,			snake_case_						:	Any = None     ,			snake_case_						:	str = None     ,			snake_case_						:	List[Any] = True     ,			snake_case_						:	List[str] = False     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Dict = None     ,			snake_case_						:	str = 0     ,			snake_case_						:	List[str] = None     ,			snake_case_						:	List[Any] = None     ,			snake_case_						:	str = False     ,			snake_case_						:	Union[str, Any] = False     ,			snake_case_						:	Any = False     ,			snake_case_						:	Tuple = False     ,			snake_case_						:	Dict = False     ,			snake_case_						:	Optional[int] = True     ,			snake_case_						:	Optional[int] = None     ,			**snake_case_						:	Dict     ,			)				->							BatchEncoding:




        '''simple docstring'''

        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 							__magic_name__							(   self						:	List[Any]     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	Any				)				->							Tuple:




        '''simple docstring'''

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




    def 							__magic_name__							(   self						:	List[Any]     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	Any				)				->							Dict:




        '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




        '''simple docstring'''

        A__	=    self.tokenizer.model_input_names
        A__	=    self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)











"""simple docstring"""



import warnings

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




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     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."     ,			snake_case_     ,			)
      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__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''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__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "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__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

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




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




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

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





