






"""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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
			from PIL import Image


class        __UpperCAmelCase(  __lowerCamelCase				):




						"""simple docstring"""




						__lowerCamelCase										=   (
						    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
						    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
						    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
						)
						__lowerCamelCase										=   'CIDAS/clipseg-rd64-refined'
						__lowerCamelCase										=   'image_segmenter'
						__lowerCamelCase										=   CLIPSegForImageSegmentation

						__lowerCamelCase										=   ['image', 'text']
						__lowerCamelCase										=   ['image']




						def __init__(  self			,    *snake_case__			,    **snake_case__     ):
												'''simple docstring'''
												requires_backends(self			,    ["vision"]     )
												super().__init__(*snake_case__			,    **snake_case__     )




						def 						UpperCAmelCase_			(  self			,    snake_case__			,    snake_case__     ):
												'''simple docstring'''
												return self.pre_processor(text=[label]			,    images=[image]			,    padding=snake_case__			,    return_tensors="pt"     )




						def 						UpperCAmelCase_			(  self			,    snake_case__     ):
												'''simple docstring'''






												with torch.no_grad():
																		lowercase__							: Optional[Any]=			self.model(**snake_case__     ).logits
												return logits




						def 						UpperCAmelCase_			(  self			,    snake_case__     ):
												'''simple docstring'''
												lowercase__							: List[Any]=			outputs.cpu().detach().numpy()
												lowercase__							: int=			0
												lowercase__							: Optional[int]=			1
												return Image.fromarray((array * 255).astype(np.uinta     )     )








from __future__ import annotations

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





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[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(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

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

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= 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()