






# 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	(	A__       ):
			_lowercase		:     List[Any]        =				(
			    '''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.'''
			)
			_lowercase		:     Any        =				'''CIDAS/clipseg-rd64-refined'''
			_lowercase		:     List[str]        =				'''image_segmenter'''
			_lowercase		:     str        =				CLIPSegForImageSegmentation

			_lowercase		:     List[Any]        =				['''image''', '''text''']
			_lowercase		:     List[str]        =				['''image''']






			def __init__(							self		,       *a		,       **a)   ->							str:
						requires_backends(self		,       ['vision'])
						super().__init__(*a		,       **a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							Optional[Any]:
						return self.pre_processor(text=[label]		,       images=[image]		,       padding=a		,       return_tensors='pt')






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							str:
						with torch.no_grad():
									SCREAMING_SNAKE_CASE							=			self.model(**a).logits
						return logits







			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			outputs.cpu().detach().numpy()
						SCREAMING_SNAKE_CASE							=			0
						SCREAMING_SNAKE_CASE							=			1
						return Image.fromarray((array * 255).astype(np.uinta))











import os




def  __UpperCAmelCase(   ):
       with open(os.path.dirname(lowercase_   ) + '''/p022_names.txt'''   ) as file:
              _lowerCamelCase				:  Optional[int]						=		str(file.readlines()[0]   )
              _lowerCamelCase				:  List[Any]						=		names.replace('''"'''			,     ''''''   ).split(''','''   )

       names.sort()

       _lowerCamelCase				:  Optional[int]						=		0
       _lowerCamelCase				:  Tuple						=		0

       for i, name in enumerate(lowercase_   ):
              for letter in name:
                     name_score += ord(lowercase_   ) - 64

              total_score += (i + 1) * name_score
              _lowerCamelCase				:  Optional[int]						=		0
       return total_score


if __name__ == "__main__":
       print(solution())

