"""simple docstring"""







from __future__ import annotations

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

_a	:	int=					{
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							dict     ,				UpperCAmelCase_       :							str    )				->     list[str]:

	'''simple docstring'''





	__snake_case     :				Tuple 						=					{start}
	__snake_case     :				str 						=					[start]
	__snake_case     :				Queue 						=					Queue()
	queue.put(UpperCAmelCase_    )
	while not queue.empty():
		__snake_case     :				Union[str, Any] 						=					queue.get()
		for w in graph[v]:
			if w not in explored:
				explored.add(UpperCAmelCase_    )
				result.append(UpperCAmelCase_    )
				queue.put(UpperCAmelCase_    )
	return result
def     __UpperCAmelCase							(       UpperCAmelCase_       :							dict     ,				UpperCAmelCase_       :							str    )				->     list[str]:

	'''simple docstring'''





	__snake_case     :				Tuple 						=					{start}
	__snake_case     :				Optional[Any] 						=					[start]
	__snake_case     :				Tuple 						=					deque([start]    )
	while queue:
		__snake_case     :				Union[str, Any] 						=					queue.popleft()
		for child in graph[v]:
			if child not in visited:
				visited.add(UpperCAmelCase_    )
				result.append(UpperCAmelCase_    )
				queue.append(UpperCAmelCase_    )
	return result
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str    )				->     None:

	'''simple docstring'''





	__snake_case     :				Dict 						=					F"from __main__ import G, {name}"
	__snake_case     :				Any 						=					1_00_00
	__snake_case     :				Optional[int] 						=					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"""







# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
				    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

					return output_ids.reshape(_A      ,    out_b // in_b      ,    *output_ids.shape[1:])[0][0]






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





