






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    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE     =					"BlipImageProcessor"
     SCREAMING_SNAKE_CASE     =					"AutoTokenizer"




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         super().__init__(UpperCamelCase_			,						UpperCamelCase_					)

         # add QFormer tokenizer
         __A       =						qformer_tokenizer




     def __call__(					self							:      str			,						UpperCamelCase_							:      ImageInput = None			,						UpperCamelCase_							:      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,						UpperCamelCase_							:      bool = True			,						UpperCamelCase_							:      Union[bool, str, PaddingStrategy] = False			,						UpperCamelCase_							:      Union[bool, str, TruncationStrategy] = None			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      int = 0			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      bool = True			,						UpperCamelCase_							:      Optional[Union[str, TensorType]] = None			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         if images is None and text is None:
             raise ValueError("""You have to specify at least images or text."""					)

         __A       =						BatchFeature()

         if text is not None:
             __A       =						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_			,						)
             encoding.update(UpperCamelCase_					)
             __A       =						self.qformer_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_			,						)
             __A       =						qformer_text_encoding.pop("""input_ids"""					)
             __A       =						qformer_text_encoding.pop("""attention_mask"""					)

         if images is not None:
             __A       =						self.image_processor(UpperCamelCase_			,						return_tensors=UpperCamelCase_					)
             encoding.update(UpperCamelCase_					)

         return encoding




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						*UpperCamelCase_							:      List[Any]			,						**UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



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




     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """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					)					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         if os.path.isfile(UpperCamelCase_					):
             raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"					)
         os.makedirs(UpperCamelCase_			,						exist_ok=UpperCamelCase_					)
         __A       =						os.path.join(UpperCamelCase_			,						"""qformer_tokenizer"""					)
         self.qformer_tokenizer.save_pretrained(UpperCamelCase_					)
         return super().save_pretrained(UpperCamelCase_			,						**UpperCamelCase_					)





     @classmethod
     def 		lowerCAmelCase_  (					cls							:      Tuple			,						UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						AutoTokenizer.from_pretrained(UpperCamelCase_			,						subfolder="""qformer_tokenizer"""					)
         __A       =						cls._get_arguments_from_pretrained(UpperCamelCase_			,						**UpperCamelCase_					)
         args.append(UpperCamelCase_					)
         return cls(*UpperCamelCase_					)












from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


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




