





from __future__ import annotations

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

UpperCAmelCase__    :      List[Any]       					={
    '''A''': ['''B''', '''C'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E'''],
}





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	list[str]:
				lowerCamelCase									={start}
				lowerCamelCase									=[start]
				lowerCamelCase									=Queue()
				queue.put(_UpperCAmelCase					)
				while not queue.empty():
								lowerCamelCase									=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       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	list[str]:
				lowerCamelCase									={start}
				lowerCamelCase									=[start]
				lowerCamelCase									=deque([start]					)
				while queue:
								lowerCamelCase									=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       (    _UpperCAmelCase					)    ->	None:
				lowerCamelCase									=F"""from __main__ import G, {name}"""
				lowerCamelCase									=1_00_00
				lowerCamelCase									=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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__    :      int       					={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      int       					=[
							    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaubertForMultipleChoice''',
							    '''FlaubertForQuestionAnswering''',
							    '''FlaubertForQuestionAnsweringSimple''',
							    '''FlaubertForSequenceClassification''',
							    '''FlaubertForTokenClassification''',
							    '''FlaubertModel''',
							    '''FlaubertWithLMHeadModel''',
							    '''FlaubertPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      List[str]       					=[
							    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFFlaubertForMultipleChoice''',
							    '''TFFlaubertForQuestionAnsweringSimple''',
							    '''TFFlaubertForSequenceClassification''',
							    '''TFFlaubertForTokenClassification''',
							    '''TFFlaubertModel''',
							    '''TFFlaubertPreTrainedModel''',
							    '''TFFlaubertWithLMHeadModel''',
							]


if TYPE_CHECKING:
							from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
							from .tokenization_flaubert import FlaubertTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flaubert import (
														    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaubertForMultipleChoice,
														    FlaubertForQuestionAnswering,
														    FlaubertForQuestionAnsweringSimple,
														    FlaubertForSequenceClassification,
														    FlaubertForTokenClassification,
														    FlaubertModel,
														    FlaubertPreTrainedModel,
														    FlaubertWithLMHeadModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_flaubert import (
														    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFFlaubertForMultipleChoice,
														    TFFlaubertForQuestionAnsweringSimple,
														    TFFlaubertForSequenceClassification,
														    TFFlaubertForTokenClassification,
														    TFFlaubertModel,
														    TFFlaubertPreTrainedModel,
														    TFFlaubertWithLMHeadModel,
														)

else:
							import sys

							UpperCAmelCase__    :      List[Any]       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





