



from __future__ import annotations

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

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






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   list[str]:
						__lowercase											=					{start}
						__lowercase											=					[start]
						__lowercase											=					Queue()
						queue.put(lowercase__ )
						while not queue.empty():
												__lowercase											=					queue.get()
												for w in graph[v]:
																		if w not in explored:
																								explored.add(lowercase__ )
																								result.append(lowercase__ )
																								queue.put(lowercase__ )
						return result






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   list[str]:
						__lowercase											=					{start}
						__lowercase											=					[start]
						__lowercase											=					deque([start] )
						while queue:
												__lowercase											=					queue.popleft()
												for child in graph[v]:
																		if child not in visited:
																								visited.add(lowercase__ )
																								result.append(lowercase__ )
																								queue.append(lowercase__ )
						return result






def 			UpperCAmelCase__     (     lowercase__ )			->   None:
						__lowercase											=					F"from __main__ import G, {name}"
						__lowercase											=					10_000
						__lowercase											=					timeit(F"{name}(G, 'A')"    ,							setup=lowercase__    ,							number=lowercase__ )
						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










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


UpperCamelCase__			=    {
    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
    "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__			=    ["DocumentQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageCaptioningTool"]
			UpperCamelCase__			=    ["ImageQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageSegmentationTool"]
			UpperCamelCase__			=    ["SpeechToTextTool"]
			UpperCamelCase__			=    ["TextClassificationTool"]
			UpperCamelCase__			=    ["TextQuestionAnsweringTool"]
			UpperCamelCase__			=    ["TextSummarizationTool"]
			UpperCamelCase__			=    ["TextToSpeechTool"]
			UpperCamelCase__			=    ["TranslationTool"]

if TYPE_CHECKING:
			from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
			from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .document_question_answering import DocumentQuestionAnsweringTool
						from .image_captioning import ImageCaptioningTool
						from .image_question_answering import ImageQuestionAnsweringTool
						from .image_segmentation import ImageSegmentationTool
						from .speech_to_text import SpeechToTextTool
						from .text_classification import TextClassificationTool
						from .text_question_answering import TextQuestionAnsweringTool
						from .text_summarization import TextSummarizationTool
						from .text_to_speech import TextToSpeechTool
						from .translation import TranslationTool
else:
			import sys

			UpperCamelCase__			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





