"""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     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	False
        super().__init__(__UpperCamelCase     ,__UpperCamelCase			)
        lowercase_	:				Tuple													=	self.image_processor
   def __call__(			self     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = True     ,__UpperCamelCase = False     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = 0     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = False     ,__UpperCamelCase = False     ,__UpperCamelCase = False     ,__UpperCamelCase = False     ,__UpperCamelCase = False     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      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:
             lowercase_	:				str													=	self.tokenizer
             lowercase_	:				List[str]													=	self.tokenizer(
                 text=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,padding=__UpperCamelCase     ,truncation=__UpperCamelCase     ,max_length=__UpperCamelCase     ,stride=__UpperCamelCase     ,pad_to_multiple_of=__UpperCamelCase     ,return_attention_mask=__UpperCamelCase     ,return_overflowing_tokens=__UpperCamelCase     ,return_special_tokens_mask=__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,return_token_type_ids=__UpperCamelCase     ,return_length=__UpperCamelCase     ,verbose=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,**__UpperCamelCase     ,)
             return text_encoding

        # add pixel_values
        lowercase_	:				Union[str, Any]													=	self.image_processor(__UpperCamelCase     ,return_tensors=__UpperCamelCase			)

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

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

        return encoding_image_processor
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


        return self.tokenizer.batch_decode(*__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


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







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


        '''simple docstring'''


        lowercase_	:				str													=	self.tokenizer.model_input_names
        lowercase_	:				Optional[Any]													=	self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int | float], int | float]     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int = 1_00     ,			):
     lowercase_	:				Dict													=	x_start
     lowercase_	:				Optional[Any]													=	fnc(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	0.0

     for _ in range(__SCREAMING_SNAKE_CASE      ):
          # Approximates curve as a sequence of linear lines and sums their length
          lowercase_	:				Union[str, Any]													=	(x_end - x_start) / steps + xa
          lowercase_	:				str													=	fnc(__SCREAMING_SNAKE_CASE      )
          length += math.hypot(xa - xa     ,			fxa - fxa      )

          # Increment step
          lowercase_	:				List[Any]													=	xa
          lowercase_	:				Optional[Any]													=	fxa

     return length


if __name__ == "__main__":



       def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
            return math.sin(10 * x      )

       print("f(x) = sin(10 * x)")
       print("The length of the curve from x = -10 to x = 10 is:")
       __SCREAMING_SNAKE_CASE    	=10
       while i <= 10_0000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10

