

from __future__ import annotations

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

lowerCAmelCase__						=	{
    """A""": ["""B""", """C"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E"""],
}


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict		,    SCREAMING_SNAKE_CASE_:							str )     -> list[str]:




				'''simple docstring'''





				A__            =     {start}
				A__            =     [start]
				A__            =     Queue()
				queue.put(SCREAMING_SNAKE_CASE_ )
				while not queue.empty():
								A__            =     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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict		,    SCREAMING_SNAKE_CASE_:							str )     -> list[str]:




				'''simple docstring'''





				A__            =     {start}
				A__            =     [start]
				A__            =     deque([start] )
				while queue:
								A__            =     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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> None:




				'''simple docstring'''





				A__            =     F'from __main__ import G, {name}'
				A__            =     1_0_0_0_0
				A__            =     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




from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowerCAmelCase__						=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowerCAmelCase__						=	get_logger(__name__)


lowerCAmelCase__						=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict )     -> Optional[TaskTemplate]:




				'''simple docstring'''





				A__            =     task_template_dict.get("task" )
				if task_name is None:
								logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(SCREAMING_SNAKE_CASE_ )}' )
								return None
				A__            =     NAMEaTEMPLATE.get(SCREAMING_SNAKE_CASE_ )
				return template.from_dict(SCREAMING_SNAKE_CASE_ )

