






"""simple docstring"""



import numpy as np






class 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  List[str]							):
													'''simple docstring'''





													A__  :					Optional[int]											=					(0, 0)
													A__  :					Dict											=					None
													A__  :					int											=					0
													A__  :					str											=					0
													A__  :					Optional[Any]											=					0


							def __eq__(     self :  str     ,      snake_case :  Optional[int]							):
													'''simple docstring'''





													return self.position == cell.position


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													print(self.position							)






class 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  int     ,      snake_case :  Any=(5, 5)							):
													'''simple docstring'''





													A__  :					Optional[int]											=					np.zeros(snake_case							)
													A__  :					List[Any]											=					world_size[0]
													A__  :					Dict											=					world_size[1]


							def   _UpperCamelCase    (     self :  Any							):
													'''simple docstring'''





													print(self.w							)


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  List[Any]							):
													'''simple docstring'''





													A__  :					int											=					[
													    (-1, -1),
													    (-1, 0),
													    (-1, 1),
													    (0, -1),
													    (0, 1),
													    (1, -1),
													    (1, 0),
													    (1, 1),
													]
													A__  :					int											=					cell.position[0]
													A__  :					str											=					cell.position[1]
													A__  :					Any											=					[]
													for n in neughbour_cord:
																			A__  :					List[Any]											=					current_x + n[0]
																			A__  :					Tuple											=					current_y + n[1]
																			if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																									A__  :					List[Any]											=					Cell()
																									A__  :					str											=					(x, y)
																									A__  :					Optional[Any]											=					cell
																									neighbours.append(snake_case							)
													return neighbours




def        _lowerCAmelCase						(							UpperCAmelCase__    :   List[str],  UpperCAmelCase__    :   Optional[Any],  UpperCAmelCase__    :   Dict )						->Dict:
						A__  :					Union[str, Any]											=					[]
						A__  :					Optional[int]											=					[]
						_open.append(UpperCAmelCase__ )

						while _open:
												A__  :					List[Any]											=					np.argmin([n.f for n in _open] )
												A__  :					Union[str, Any]											=					_open[min_f]
												_closed.append(_open.pop(UpperCAmelCase__ ) )
												if current == goal:
																		break
												for n in world.get_neigbours(UpperCAmelCase__ ):
																		for c in _closed:
																								if c == n:
																														continue
																		A__  :					Dict											=					current.g + 1
																		A__					,					A__  :					int											=					n.position
																		A__					,					A__  :					Optional[int]											=					goal.position
																		A__  :					Union[str, Any]											=					(ya - ya) ** 2 + (xa - xa) ** 2
																		A__  :					Optional[int]											=					n.h + n.g

																		for c in _open:
																								if c == n and c.f < n.f:
																														continue
																		_open.append(UpperCAmelCase__ )
						A__  :					List[str]											=					[]
						while current.parent is not None:
												path.append(current.position )
												A__  :					Union[str, Any]											=					current.parent
						path.append(current.position )
						return path[::-1]


if __name__ == "__main__":
						A_      =							Gridworld()
						# Start position and goal
						A_      =							Cell()
						A_      =							(0, 0)
						A_      =							Cell()
						A_      =							(4, 4)
						print(F'path from {start.position} to {goal.position}')
						A_      =							astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												A_      =							1
						print(world.w)














"""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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							'Salesforce/blip-image-captioning-base'
							snake_case_       =							(
							    'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
							    'image to caption, and returns a text that contains the description in English.'
							)
							snake_case_       =							'image_captioner'
							snake_case_       =							AutoModelForVisionaSeq

							snake_case_       =							['image']
							snake_case_       =							['text']


							def __init__(     self :  int     ,      *snake_case :  Optional[int]     ,      **snake_case :  Optional[int]							):
													'''simple docstring'''





													requires_backends(self     ,      ["""vision"""]							)
													super().__init__(*snake_case     ,      **snake_case							)


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  "Image"							):
													'''simple docstring'''





													return self.pre_processor(images=snake_case     ,      return_tensors="""pt"""							)


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  List[Any]							):
													'''simple docstring'''





													return self.model.generate(**snake_case							)


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  Any							):
													'''simple docstring'''





													return self.pre_processor.batch_decode(snake_case     ,      skip_special_tokens=snake_case							)[0].strip()






