"""simple docstring"""





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Dict   =  ["image_processor", "tokenizer"]
      lowerCamelCase__		:    Dict   =  "BlipImageProcessor"
      lowerCamelCase__		:    Union[str, Any]   =  "AutoTokenizer"


      def __init__(   self		,			a		,			a		,			a							)       ->					Optional[int]:
             super().__init__(a		,			a							)

             # add QFormer tokenizer
             lowercase__			:		Dict       =	qformer_tokenizer


      def __call__(   self		,			a = None		,			a = None		,			a = True		,			a = False		,			a = None		,			a = None		,			a = 0		,			a = None		,			a = None		,			a = False		,			a = False		,			a = False		,			a = False		,			a = False		,			a = True		,			a = None		,			**a		,			)       ->					BatchFeature:
             if images is None and text is None:
                    raise ValueError('You have to specify at least images or text.'							)

             lowercase__			:		List[Any]       =	BatchFeature()

             if text is not None:
                    lowercase__			:		Optional[int]       =	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		,			)
                    encoding.update(a							)
                    lowercase__			:		Optional[int]       =	self.qformer_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		,			)
                    lowercase__			:		List[str]       =	qformer_text_encoding.pop('input_ids'							)
                    lowercase__			:		Any       =	qformer_text_encoding.pop('attention_mask'							)

             if images is not None:
                    lowercase__			:		List[Any]       =	self.image_processor(a		,			return_tensors=a							)
                    encoding.update(a							)

             return encoding


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					List[str]:
             return self.tokenizer.batch_decode(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Tuple:
             return self.tokenizer.decode(*a		,			**a							)


      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		str       =	self.tokenizer.model_input_names
             lowercase__			:		List[Any]       =	self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)


      def 		_UpperCAmelCase       (   self		,			a		,			**a							)       ->					Optional[int]:
             if os.path.isfile(a							):
                    raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""							)
             os.makedirs(a		,			exist_ok=a							)
             lowercase__			:		int       =	os.path.join(a		,			'qformer_tokenizer'							)
             self.qformer_tokenizer.save_pretrained(a							)
             return super().save_pretrained(a		,			**a							)


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a		,			**a							)       ->					str:
             lowercase__			:		str       =	AutoTokenizer.from_pretrained(a		,			subfolder='qformer_tokenizer'							)
             lowercase__			:		int       =	cls._get_arguments_from_pretrained(a		,			**a							)
             args.append(a							)
             return cls(*a							)






"""simple docstring"""







from __future__ import annotations

def 				UpperCamelCase_    (    lowerCAmelCase__      :				list[int]    ,   lowerCAmelCase__      :				int						)						->   bool:

  """simple docstring"""




  if len(lowerCAmelCase__						) == 0:
    return False
  lowerCAmelCase_   :				Union[str, Any]								= len(lowerCAmelCase__						) // 2
  if a_list[midpoint] == item:
    return True
  if item < a_list[midpoint]:
    return binary_search(a_list[:midpoint]    ,   lowerCAmelCase__						)
  else:
    return binary_search(a_list[midpoint + 1 :]    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	input("""Enter numbers separated by comma:\n""").strip()
     lowercase__		:				Optional[int]          =	[int(item.strip()) for item in user_input.split(""",""")]
     lowercase__		:				Optional[Any]          =	int(input("""Enter the number to be found in the list:\n""").strip())
     lowercase__		:				int          =	"""""" if binary_search(sequence, target) else """not """
     print(f'{target} was {not_str}found in {sequence}')





