


'''simple docstring'''
from heapq import heappop, heappush

import numpy as np


def       _lowercase							(					UpperCamelCase__							:       np.ndarray,					UpperCamelCase__							:       tuple[int, int],					UpperCamelCase__							:       tuple[int, int],					UpperCamelCase__							:       bool,					):
 __A  ,__A	:		Any	  =      grid.shape
 __A	:		Tuple	  =      [-1, 1, 0, 0]
 __A	:		Dict	  =      [0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A  ,__A	:		List[Any]	  =      [(0, source)], set()
 __A	:		Dict	  =      np.full((rows, cols),					np.inf      )
 __A	:		List[Any]	  =      0
 __A	:		Union[str, Any]	  =      np.empty((rows, cols),					dtype=UpperCamelCase__      )
 __A	:		str	  =      None

 while queue:
  ((__A)  ,(__A))	:		Tuple	  =      heappop(UpperCamelCase__      )
  if (x, y) in visited:
   continue
  visited.add((x, y)      )

  if (x, y) == destination:
   __A	:		int	  =      []
   while (x, y) != source:
    path.append((x, y)      )
    __A  ,__A	:		List[Any]	  =      predecessors[x, y]
   path.append(UpperCamelCase__      )  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__      )      ):
   __A  ,__A	:		Optional[int]	  =      x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A	:		Union[str, Any]	  =      grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,					(dist + 1, (nx, ny))      )
     __A	:		Union[str, Any]	  =      dist + 1
     __A	:		Dict	  =      (x, y)

 return np.inf, []


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     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		int     				=    (
     '''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					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  requires_backends(self   ,   ['vision']    )
  super().__init__(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  return self.pre_processor(__lowercase   ,   __lowercase   ,   return_tensors='pt'    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  with torch.no_grad():
   return self.model(**__lowercase    ).logits







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






