

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xglm import XGLMTokenizer
else:
       A_						:     Any			  =				None


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

A_						:     Any			  =				{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A_						:     int			  =				{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

A_						:     int			  =				{
    'facebook/xglm-564M': 2048,
}
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__       							=    XGLMTokenizer


    def __init__(self       , lowercase__=None       , lowercase__=None       , lowercase__="<s>"       , lowercase__="</s>"       , lowercase__="</s>"       , lowercase__="<s>"       , lowercase__="<unk>"       , lowercase__="<pad>"       , **lowercase__       , )       -> Optional[Any]:
        # Compatibility with the original tokenizer
        __UpperCAmelCase			   =     7
        __UpperCAmelCase			   =     [F'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

        __UpperCAmelCase			   =     kwargs.get('''additional_special_tokens'''       , []					)
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            lowercase__       , tokenizer_file=lowercase__       , bos_token=lowercase__       , eos_token=lowercase__       , sep_token=lowercase__       , cls_token=lowercase__       , unk_token=lowercase__       , pad_token=lowercase__       , **lowercase__       , )

        __UpperCAmelCase			   =     vocab_file
        __UpperCAmelCase			   =     False if not self.vocab_file else True


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[int]:

        if token_ids_a is None:
            return [self.sep_token_id] + token_ids_a
        __UpperCAmelCase			   =     [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[int]:
        __UpperCAmelCase			   =     [self.sep_token_id]

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






    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> Tuple[str]:
        if not self.can_save_slow_tokenizer:
            raise ValueError(
                '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                '''tokenizer.'''					)

        if not os.path.isdir(lowercase__					):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
            return
        __UpperCAmelCase			   =     os.path.join(
            lowercase__       , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

        if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase__					):
            copyfile(self.vocab_file       , lowercase__					)

        return (out_vocab_file,)







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




