

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


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

A_						:     Optional[int]			  =				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A_						:     Tuple			  =				{
    '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'
        ),
    },
}

A_						:     List[Any]			  =				{
    'openai/clip-vit-base-patch32': 77,
}
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    VOCAB_FILES_NAMES
    a__       							=    PRETRAINED_VOCAB_FILES_MAP
    a__       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       							=    ["input_ids", "attention_mask"]
    a__       							=    CLIPTokenizer


    def __init__(self       , lowercase__=None       , lowercase__=None       , lowercase__=None       , lowercase__="<|endoftext|>"       , lowercase__="<|startoftext|>"       , lowercase__="<|endoftext|>"       , lowercase__="<|endoftext|>"       , **lowercase__       , )       -> List[str]:
        super().__init__(
            lowercase__       , lowercase__       , tokenizer_file=lowercase__       , unk_token=lowercase__       , bos_token=lowercase__       , eos_token=lowercase__       , pad_token=lowercase__       , **lowercase__       , )

        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        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase			   =     self.backend_tokenizer.decode

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

        __UpperCAmelCase			   =     new_decode_method


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[int]:
        __UpperCAmelCase			   =     [self.bos_token_id]
        __UpperCAmelCase			   =     [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        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[int]:
        __UpperCAmelCase			   =     [self.bos_token_id]
        __UpperCAmelCase			   =     [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        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> Tuple[str]:
        __UpperCAmelCase			   =     self._tokenizer.model.save(lowercase__       , name=lowercase__					)
        return tuple(lowercase__					)







from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

A_						:     Optional[int]			  =				{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     Queue()
    queue.put(SCREAMING_SNAKE_CASE )
    while not queue.empty():
        __UpperCAmelCase			   =     queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.put(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     deque([start] )
    while queue:
        __UpperCAmelCase			   =     queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.append(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE ) ->       None:






    '''simple docstring'''
    __UpperCAmelCase			   =     f'''from __main__ import G, {name}'''
    __UpperCAmelCase			   =     1_0_0_0_0
    __UpperCAmelCase			   =     timeit(f'''{name}(G, \'A\')'''    ,				setup=SCREAMING_SNAKE_CASE    ,				number=SCREAMING_SNAKE_CASE )
    print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       benchmark_function('breadth_first_search')
       benchmark_function('breadth_first_search_with_deque')
       # breadth_first_search                finished 10000 runs in 0.20999 seconds
       # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




