
"""simple docstring"""


from __future__ import annotations

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

__A 		= {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}



def 					_lowerCamelCase(__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 					_lowerCamelCase(__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 					_lowerCamelCase(__UpperCamelCase )       ->				None:
	_lowerCAmelCase			=F'''from __main__ import G, {name}'''
	_lowerCAmelCase			=10000
	_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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A 		= {
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['LEDTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LEDForConditionalGeneration',
			    'LEDForQuestionAnswering',
			    'LEDForSequenceClassification',
			    'LEDModel',
			    'LEDPreTrainedModel',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
			from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
			from .tokenization_led import LEDTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_led_fast import LEDTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_led import (
						    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LEDForConditionalGeneration,
						    LEDForQuestionAnswering,
						    LEDForSequenceClassification,
						    LEDModel,
						    LEDPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



