
"""simple docstring"""





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				42






class    _UpperCAmelCase:



		def __init__( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  [[] for _ in range(__a)]
							_UpperCamelCase										=  size



		def __getitem__( self    ,		__a)	->	Iterator[Edge]:






							'''simple docstring'''
							return iter(self._graph[vertex])



		@property
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self._size



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							if weight not in (0, 1):
												raise ValueError('''Edge weight must be either 0 or 1.''')

							if to_vertex < 0 or to_vertex >= self.size:
												raise ValueError('''Vertex indexes must be in [0; size).''')

							self._graph[from_vertex].append(Edge(__a    ,		__a))




		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	int | None:






							'''simple docstring'''
							_UpperCamelCase										=  deque([start_vertex])
							_UpperCamelCase										=  [None] * self.size
							_UpperCamelCase										=  0

							while queue:
												_UpperCamelCase										=  queue.popleft()
												_UpperCamelCase										=  distances[current_vertex]
												if current_distance is None:
																	continue

												for edge in self[current_vertex]:
																	_UpperCamelCase										=  current_distance + edge.weight
																	_UpperCamelCase										=  distances[edge.destination_vertex]
																	if (
																	    isinstance(__a    ,		__a)
																	    and new_distance >= dest_vertex_distance
																	):
																						continue
																	_UpperCamelCase										=  new_distance
																	if edge.weight == 0:
																						queue.appendleft(edge.destination_vertex)
																	else:
																						queue.append(edge.destination_vertex)

							if distances[finish_vertex] is None:
												raise ValueError('''No path from start_vertex to finish_vertex.''')

							return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()








"""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 typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






