






# 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_      (			snake_case_				):



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




	def 							_UpperCamelCase			(       self       ,			a       ,			a					)       ->    List[str]:
								return self.pre_processor(text=[label]       ,			images=[image]       ,			padding=a       ,			return_tensors='pt'					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Tuple:
								with torch.no_grad():
															snake_case_				=							self.model(**a					).logits
								return logits






	def 							_UpperCamelCase			(       self       ,			a					)       ->    Dict:
								snake_case_				=							outputs.cpu().detach().numpy()
								snake_case_				=							0
								snake_case_				=							1
								return Image.fromarray((array * 2_55).astype(np.uinta					)					)













lowercase  							=					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase  							=					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase  							=					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





