






'''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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[str]   		=			['image_processor', 'tokenizer']
       snake_case__    :     Optional[Any]   		=			'BlipImageProcessor'
       snake_case__    :     Optional[int]   		=			'AutoTokenizer'




       def __init__(					self      : List[Any]	,    A__      : Union[str, Any]	,    A__      : Any	,    A__      : Dict					):


            """simple docstring"""







            super().__init__(A__	,    A__					)

            # add QFormer tokenizer
            __lowerCamelCase						:       Any				   =				qformer_tokenizer




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


            """simple docstring"""







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

            __lowerCamelCase						:       Optional[Any]				   =				BatchFeature()

            if text is not None:
                 __lowerCamelCase						:       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__					)
                 __lowerCamelCase						:       Dict				   =				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__	,    )
                 __lowerCamelCase						:       int				   =				qformer_text_encoding.pop("""input_ids"""					)
                 __lowerCamelCase						:       List[str]				   =				qformer_text_encoding.pop("""attention_mask"""					)

            if images is not None:
                 __lowerCamelCase						:       Optional[Any]				   =				self.image_processor(A__	,    return_tensors=A__					)
                 encoding.update(A__					)

            return encoding




       def 					a_					(					self      : List[str]	,    *A__      : Optional[int]	,    **A__      : List[str]					):


            """simple docstring"""







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




       def 					a_					(					self      : int	,    *A__      : List[str]	,    **A__      : Tuple					):


            """simple docstring"""







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




       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[int]	,    A__      : Dict	,    **A__      : Tuple					):


            """simple docstring"""







            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__					)
            __lowerCamelCase						:       int				   =				os.path.join(A__	,    """qformer_tokenizer"""					)
            self.qformer_tokenizer.save_pretrained(A__					)
            return super().save_pretrained(A__	,    **A__					)




       @classmethod
       def 					a_					(					cls      : Tuple	,    A__      : Optional[int]	,    **A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				AutoTokenizer.from_pretrained(A__	,    subfolder="""qformer_tokenizer"""					)
            __lowerCamelCase						:       int				   =				cls._get_arguments_from_pretrained(A__	,    **A__					)
            args.append(A__					)
            return cls(*A__					)











'''simple docstring'''

UpperCAmelCase__						:List[Any]          =			256
# Modulus to hash a string
UpperCAmelCase__						:str          =			1_000_003
def     __lowercase  (_lowercase,						_lowercase     )   ->     bool:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				len(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				len(_lowercase     )
     if p_len > t_len:
          return False

     __lowerCamelCase						:       Optional[Any]				   =				0
     __lowerCamelCase						:       Tuple				   =				0
     __lowerCamelCase						:       Dict				   =				1

     # Calculating the hash of pattern and substring of text
     for i in range(_lowercase     ):
          __lowerCamelCase						:       Optional[int]				   =				(ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
          __lowerCamelCase						:       Dict				   =				(ord(text[i]     ) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          __lowerCamelCase						:       Dict				   =				(modulus_power * alphabet_size) % modulus

     for i in range(0,						t_len - p_len + 1     ):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          __lowerCamelCase						:       Dict				   =				(
              (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
              + ord(text[i + p_len]     )
          ) % modulus
     return False
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				"""abc1abc12"""
     __lowerCamelCase						:       Optional[Any]				   =				"""alskfjaldsabc1abc1abc12k23adsfabcabc"""
     __lowerCamelCase						:       List[Any]				   =				"""alskfjaldsk23adsfabcabc"""
     assert rabin_karp(_lowercase,						_lowercase     ) and not rabin_karp(_lowercase,						_lowercase     )

     # Test 2)
     __lowerCamelCase						:       Optional[int]				   =				"""ABABX"""
     __lowerCamelCase						:       Dict				   =				"""ABABZABABYABABX"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 3)
     __lowerCamelCase						:       Any				   =				"""AAAB"""
     __lowerCamelCase						:       int				   =				"""ABAAAAAB"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 4)
     __lowerCamelCase						:       Any				   =				"""abcdabcy"""
     __lowerCamelCase						:       Dict				   =				"""abcxabcdabxabcdabcdabcy"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 5)
     __lowerCamelCase						:       str				   =				"""Lü"""
     __lowerCamelCase						:       str				   =				"""Lüsai"""
     assert rabin_karp(_lowercase,						_lowercase     )
     __lowerCamelCase						:       Tuple				   =				"""Lue"""
     assert not rabin_karp(_lowercase,						_lowercase     )
     print("""Success."""     )


if __name__ == "__main__":
   test_rabin_karp()



