


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		A_     :      Optional[int]				   =    42
		A_     :      Union[str, Any]				   =    42







class _lowerCamelCase	:



		'''simple docstring'''







		def __init__( self		:						Any  ,					_A		:						Optional[Any]      )  ->			Optional[Any]:
			__magic_name__	:   List[Any]					     =					[[] for _ in range(__a      )]
			__magic_name__	:   str					     =					size







		def __getitem__( self		:						Optional[int]  ,					_A		:						Optional[int]      )  ->			Iterator[Edge]:
			return iter(self._graph[vertex]      )







		@property
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Tuple:
			return self._size







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						Any  ,					_A		:						List[Any]  ,					_A		:						Any      )  ->			Any:
			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        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						str  ,					_A		:						List[Any]      )  ->			int | None:
			__magic_name__	:   List[str]					     =					deque([start_vertex]      )
			__magic_name__	:   Dict					     =					[None] * self.size
			__magic_name__	:   Tuple					     =					0

			while queue:
				__magic_name__	:   Union[str, Any]					     =					queue.popleft()
				__magic_name__	:   List[str]					     =					distances[current_vertex]
				if current_distance is None:
					continue

				for edge in self[current_vertex]:
					__magic_name__	:   int					     =					current_distance + edge.weight
					__magic_name__	:   Optional[Any]					     =					distances[edge.destination_vertex]
					if (
					    isinstance(__a  ,					__a      )
					    and new_distance >= dest_vertex_distance
					):
						continue
					__magic_name__	:   Tuple					     =					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

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


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """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."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







		def __init__( self		:						Dict  ,					*_A		:						Dict  ,					**_A		:						Any      )  ->			Dict:
			requires_backends(self  ,					['vision']      )
			super().__init__(*_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()