'''simple docstring'''




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


__snake_case  : List[Any]		       =    logging.get_logger(__name__)

__snake_case  : Dict		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : Any		       =    {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

__snake_case  : List[Any]		       =    {
    'openai/clip-vit-base-patch32': 77,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   Optional[Any]  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   List[Any]  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[int]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							CLIPTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<|endoftext|>"       ,	_SCREAMING_SNAKE_CASE="<|startoftext|>"       ,	_SCREAMING_SNAKE_CASE="<|endoftext|>"       ,	_SCREAMING_SNAKE_CASE="<|endoftext|>"       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	Dict:
           super().__init__(
               _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	bos_token=_SCREAMING_SNAKE_CASE       ,	eos_token=_SCREAMING_SNAKE_CASE       ,	pad_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE       ,	)

           if not isinstance(self.backend_tokenizer.pre_tokenizer       ,	pre_tokenizers.Sequence					):
               raise ValueError(
                   '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                   ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                   ''' to be compatible with this version.The easiest way to do so is'''
                   ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                   ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                   ''' transformers.'''					)

           self._wrap_decode_method_backend_tokenizer()

       def    __A			( self					)						->	Any:
           A_      =  self.backend_tokenizer.decode

           def new_decode_method(*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					):
               A_      =  orig_decode_method(*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_      =  text.replace(self.backend_tokenizer.model.end_of_word_suffix       ,	''' '''					).strip()
               return text

           A_      =  new_decode_method

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	List[int]:
           A_      =  [self.bos_token_id]
           A_      =  [self.eos_token_id]

           if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
           return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	List[int]:
           A_      =  [self.bos_token_id]
           A_      =  [self.eos_token_id]

           if token_ids_a is None:
               return len(bos_token + token_ids_a + eos_token					) * [0]
           return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token					) * [0]

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)







'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  int(''''''.join(c for c in ordinal if c in '''0123456789'''  )  )  # 1000th --> 1000
        print(
            F'''{ordinal} fibonacci number using matrix exponentiation is '''
            F'''{nth_fibonacci_matrix(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\n'''  )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






