





"""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 				snake_case				(      __a			):

      '''simple docstring'''



      _A  :							List[Any]								=							['image_processor', 'tokenizer']
      _A  :							Any								=							'BlipImageProcessor'
      _A  :							str								=							'AutoTokenizer'

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


        '''simple docstring'''




        super().__init__(lowercase_    ,					lowercase_			)

        # add QFormer tokenizer
        __UpperCAmelCase     :    List[str]							=    qformer_tokenizer

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


        '''simple docstring'''




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

        __UpperCAmelCase     :    Optional[Any]							=    BatchFeature()

        if text is not None:
          __UpperCAmelCase     :    Optional[Any]							=    self.tokenizer(
              text=lowercase_    ,					add_special_tokens=lowercase_    ,					padding=lowercase_    ,					truncation=lowercase_    ,					max_length=lowercase_    ,					stride=lowercase_    ,					pad_to_multiple_of=lowercase_    ,					return_attention_mask=lowercase_    ,					return_overflowing_tokens=lowercase_    ,					return_special_tokens_mask=lowercase_    ,					return_offsets_mapping=lowercase_    ,					return_token_type_ids=lowercase_    ,					return_length=lowercase_    ,					verbose=lowercase_    ,					return_tensors=lowercase_    ,					**lowercase_    ,					)
          encoding.update(lowercase_			)
          __UpperCAmelCase     :    Optional[Any]							=    self.qformer_tokenizer(
              text=lowercase_    ,					add_special_tokens=lowercase_    ,					padding=lowercase_    ,					truncation=lowercase_    ,					max_length=lowercase_    ,					stride=lowercase_    ,					pad_to_multiple_of=lowercase_    ,					return_attention_mask=lowercase_    ,					return_overflowing_tokens=lowercase_    ,					return_special_tokens_mask=lowercase_    ,					return_offsets_mapping=lowercase_    ,					return_token_type_ids=lowercase_    ,					return_length=lowercase_    ,					verbose=lowercase_    ,					return_tensors=lowercase_    ,					**lowercase_    ,					)
          __UpperCAmelCase     :    Union[str, Any]							=    qformer_text_encoding.pop('''input_ids'''			)
          __UpperCAmelCase     :    Any							=    qformer_text_encoding.pop('''attention_mask'''			)

        if images is not None:
          __UpperCAmelCase     :    int							=    self.image_processor(lowercase_    ,					return_tensors=lowercase_			)
          encoding.update(lowercase_			)

        return encoding

      def    A_       (				self							:   str    ,					*__lowercase							:   List[Any]    ,					**__lowercase							:   str			):


        '''simple docstring'''




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

      def    A_       (				self							:   int    ,					*__lowercase							:   Optional[int]    ,					**__lowercase							:   str			):


        '''simple docstring'''




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

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


        '''simple docstring'''




        __UpperCAmelCase     :    Dict							=    self.tokenizer.model_input_names
        __UpperCAmelCase     :    Tuple							=    self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

      def    A_       (				self							:   Any    ,					__lowercase							:   List[str]    ,					**__lowercase							:   Tuple			):


        '''simple docstring'''




        if os.path.isfile(lowercase_			):
          raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''			)
        os.makedirs(lowercase_    ,					exist_ok=lowercase_			)
        __UpperCAmelCase     :    Union[str, Any]							=    os.path.join(lowercase_    ,					'''qformer_tokenizer'''			)
        self.qformer_tokenizer.save_pretrained(lowercase_			)
        return super().save_pretrained(lowercase_    ,					**lowercase_			)







      @classmethod
      def    A_       (				cls							:   str    ,					__lowercase							:   Tuple    ,					**__lowercase							:   List[str]			):


        '''simple docstring'''




        __UpperCAmelCase     :    List[str]							=    AutoTokenizer.from_pretrained(lowercase_    ,					subfolder='''qformer_tokenizer'''			)
        __UpperCAmelCase     :    List[str]							=    cls._get_arguments_from_pretrained(lowercase_    ,					**lowercase_			)
        args.append(lowercase_			)
        return cls(*lowercase_			)
'''simple docstring'''


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case						  =		10

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  for i in range(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    if array[i] == target:
      return i
  return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowercase_     =       one_third - 1
    elif array[two_third] < target:
      lowercase_     =       two_third + 1

    else:
      lowercase_     =       one_third + 1
      lowercase_     =       two_third - 1
  else:
    return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __snake_case						  =		input("""Enter numbers separated by comma:\n""").strip()
       __snake_case						  =		[int(item.strip()) for item in user_input.split(""",""")]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       __snake_case						  =		int(input("""Enter the number to be found in the list:\n""").strip())
       __snake_case						  =		ite_ternary_search(collection, target)
       __snake_case						  =		rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(f'''Iterative search: {target} found at positions: {resulta}''')
              print(f'''Recursive search: {target} found at positions: {resulta}''')
       else:
              print("""Not found""")

