





'''simple docstring'''






from __future__ import annotations

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

__UpperCamelCase      :   Optional[int]								=     {
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}





def      lowercase  (  lowerCAmelCase :						List[Any]   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				List[str]				=  {start}
				_A :				Optional[int]				=  [start]
				_A :				Queue				=  Queue()
				queue.put(_UpperCAmelCase)
				while not queue.empty():
								_A :				int				=  queue.get()
								for w in graph[v]:
												if w not in explored:
																explored.add(_UpperCAmelCase)
																result.append(_UpperCAmelCase)
																queue.put(_UpperCAmelCase)
				return result





def      lowercase  (  lowerCAmelCase :						List[str]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				List[Any]				=  {start}
				_A :				Optional[Any]				=  [start]
				_A :				Optional[int]				=  deque([start])
				while queue:
								_A :				List[str]				=  queue.popleft()
								for child in graph[v]:
												if child not in visited:
																visited.add(_UpperCAmelCase)
																result.append(_UpperCAmelCase)
																queue.append(_UpperCAmelCase)
				return result





def      lowercase  (  lowerCAmelCase :						Any):



				"""simple docstring"""


				_A :				List[str]				=  f"""from __main__ import G, {name}"""
				_A :				int				=  1_0000
				_A :				List[str]				=  timeit(f"""{name}(G, 'A')"""   , setup=_UpperCAmelCase   , number=_UpperCAmelCase)
				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







'''simple docstring'''

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:
			lowerCAmelCase__      :   List[str]      	=			None


lowerCAmelCase__      :   Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Tuple      	=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__      :   List[str]      	=			{
    "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",
    },
}

lowerCAmelCase__      :   int      	=			{
    "facebook/xglm-564M": 20_48,
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE							=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE							=	['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE							=	XGLMTokenizer
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      str="<s>"		,	UpperCAmelCase_       :      List[Any]="</s>"		,	UpperCAmelCase_       :      Dict="</s>"		,	UpperCAmelCase_       :      Optional[Any]="<s>"		,	UpperCAmelCase_       :      List[str]="<unk>"		,	UpperCAmelCase_       :      Union[str, Any]="<pad>"		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							# Compatibility with the original tokenizer
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Tuple     =     [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

							__UpperCAmelCase	:							List[Any]     =     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__(
							    UpperCAmelCase_		,	tokenizer_file=UpperCAmelCase_		,	bos_token=UpperCAmelCase_		,	eos_token=UpperCAmelCase_		,	sep_token=UpperCAmelCase_		,	cls_token=UpperCAmelCase_		,	unk_token=UpperCAmelCase_		,	pad_token=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     vocab_file
							__UpperCAmelCase	:							Optional[int]     =     False if not self.vocab_file else True
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""

							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							__UpperCAmelCase	:							Any     =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     [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       :      List[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      Optional[str] = None		):







							"""simple docstring"""
							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(UpperCAmelCase_		):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
									return
							__UpperCAmelCase	:							str     =     os.path.join(
							    UpperCAmelCase_		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

							return (out_vocab_file,)




