





# 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        snake_case_	(		UpperCamelCase__						):
  A_      							=   (
      '''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.'''
  )
  A_      							=   '''CIDAS/clipseg-rd64-refined'''
  A_      							=   '''image_segmenter'''
  A_      							=   CLIPSegForImageSegmentation

  A_      							=   ['''image''', '''text''']
  A_      							=   ['''image''']






  def __init__(							self			:					List[str]       ,  *_snake_case			:					Tuple       ,  **_snake_case			:					str      )->Dict:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					"Image"       ,  _snake_case			:					str      )->int:






   '''simple docstring'''

   return self.pre_processor(text=[label]       ,  images=[image]       ,  padding=UpperCamelCase_       ,  return_tensors="""pt"""      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					List[str]      )->Optional[Any]:






   '''simple docstring'''

   with torch.no_grad():
    __lowerCAmelCase					:	Optional[int]      =       self.model(**UpperCamelCase_      ).logits
   return logits






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					str      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       outputs.cpu().detach().numpy()
   __lowerCAmelCase					:	Union[str, Any]      =       0
   __lowerCAmelCase					:	Union[str, Any]      =       1
   return Image.fromarray((array * 255).astype(np.uinta      )      )






_UpperCAmelCase             =							{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

_UpperCAmelCase             =							{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:dict[int, list[int]]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list[bool]   )      ->   list[int]:
 __lowerCAmelCase					:	str      =       True
 __lowerCAmelCase					:	str      =       []

 for neighbour in graph[vert]:
  if not visited[neighbour]:
   order += topology_sort(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 order.append(SCREAMING_SNAKE_CASE   )

 return order






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:dict[int, list[int]]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list[bool]   )      ->   list[int]:
 __lowerCAmelCase					:	Optional[Any]      =       True
 __lowerCAmelCase					:	Union[str, Any]      =       [vert]

 for neighbour in reversed_graph[vert]:
  if not visited[neighbour]:
   component += find_components(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 return component







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:dict[int, list[int]]   )      ->   list[list[int]]:
 __lowerCAmelCase					:	Optional[Any]      =       len(SCREAMING_SNAKE_CASE   ) * [False]
 __lowerCAmelCase					:	dict[int, list[int]]      =       {vert: [] for vert in range(len(SCREAMING_SNAKE_CASE   )   )}

 for vert, neighbours in graph.items():
  for neighbour in neighbours:
   reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Tuple      =       []
 for i, was_visited in enumerate(SCREAMING_SNAKE_CASE   ):
  if not was_visited:
   order += topology_sort(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Optional[Any]      =       []
 __lowerCAmelCase					:	Any      =       len(SCREAMING_SNAKE_CASE   ) * [False]

 for i in range(len(SCREAMING_SNAKE_CASE   )   ):
  __lowerCAmelCase					:	Optional[int]      =       order[len(SCREAMING_SNAKE_CASE   ) - i - 1]
  if not visited[vert]:
   __lowerCAmelCase					:	Any      =       find_components(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
   components_list.append(SCREAMING_SNAKE_CASE   )

 return components_list