


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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	['image_processor', 'tokenizer']
 SCREAMING_SNAKE_CASE__				=	'BlipImageProcessor'
 SCREAMING_SNAKE_CASE__				=	'AutoTokenizer'




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	False
       super().__init__(_lowerCamelCase ,  _lowerCamelCase   )
       a						:Any										=	self.image_processor




 def __call__(		self ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = True ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = 0 ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = False ,  _lowerCamelCase = False ,  _lowerCamelCase = False ,  _lowerCamelCase = False ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       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						:Optional[Any]										=	self.tokenizer
             a						:Union[str, Any]										=	self.tokenizer(
                 text=_lowerCamelCase ,  add_special_tokens=_lowerCamelCase ,  padding=_lowerCamelCase ,  truncation=_lowerCamelCase ,  max_length=_lowerCamelCase ,  stride=_lowerCamelCase ,  pad_to_multiple_of=_lowerCamelCase ,  return_attention_mask=_lowerCamelCase ,  return_overflowing_tokens=_lowerCamelCase ,  return_special_tokens_mask=_lowerCamelCase ,  return_offsets_mapping=_lowerCamelCase ,  return_token_type_ids=_lowerCamelCase ,  return_length=_lowerCamelCase ,  verbose=_lowerCamelCase ,  return_tensors=_lowerCamelCase ,  **_lowerCamelCase ,  )
             return text_encoding

       # add pixel_values
       a						:Any										=	self.image_processor(_lowerCamelCase ,  return_tensors=_lowerCamelCase   )

       if text is not None:
             a						:int										=	self.tokenizer(
                 text=_lowerCamelCase ,  add_special_tokens=_lowerCamelCase ,  padding=_lowerCamelCase ,  truncation=_lowerCamelCase ,  max_length=_lowerCamelCase ,  stride=_lowerCamelCase ,  pad_to_multiple_of=_lowerCamelCase ,  return_attention_mask=_lowerCamelCase ,  return_overflowing_tokens=_lowerCamelCase ,  return_special_tokens_mask=_lowerCamelCase ,  return_offsets_mapping=_lowerCamelCase ,  return_token_type_ids=_lowerCamelCase ,  return_length=_lowerCamelCase ,  verbose=_lowerCamelCase ,  return_tensors=_lowerCamelCase ,  **_lowerCamelCase ,  )
       else:
             a						:Union[str, Any]										=	None

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

       return encoding_image_processor




 def 	SCREAMING_SNAKE_CASE__    (		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       return self.tokenizer.batch_decode(*_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       return self.tokenizer.decode(*_lowerCamelCase ,  **_lowerCamelCase   )




 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.tokenizer.model_input_names
       a						:Any										=	self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









import math
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		float						):




      """simple docstring"""





      return math.pow(UpperCAmelCase_			,  2						) - a
def     __lowerCamelCase						(			UpperCAmelCase_						:		float						):




      """simple docstring"""





      return 2 * x
def     __lowerCamelCase						(			UpperCAmelCase_						:		float						):




      """simple docstring"""





      a						:int										=	2.0

      while start <= a:
            a						:int										=	math.pow(UpperCAmelCase_			,  2						)

      return start
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		int = 9999			,  UpperCAmelCase_						:		float = 0.00000000000001						):




      """simple docstring"""





      if a < 0:
            raise ValueError('''math domain error'''						)

      a						:List[Any]										=	get_initial_point(UpperCAmelCase_						)

      for _ in range(UpperCAmelCase_						):
            a						:Optional[int]										=	value
            a						:int										=	value - fx(UpperCAmelCase_			,  UpperCAmelCase_						) / fx_derivative(UpperCAmelCase_						)
            if abs(prev_value - value						) < tolerance:
                  return value

      return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()





