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

	UpperCamelCase_  :					Optional[Any]    		=	['image', 'text']
	UpperCamelCase_  :					Union[str, Any]    		=	['image']







	def __init__(     self      :Union[str, Any]   ,     *lowercase      :Any   ,     **lowercase      :Union[str, Any]			)		->      Optional[Any]:







				"""simple docstring"""






				requires_backends(self   ,     ['''vision''']			)
				super().__init__(*lowercase   ,     **lowercase			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :"Image"   ,     lowercase      :str			)		->      Any:







				"""simple docstring"""






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







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







				"""simple docstring"""






				with torch.no_grad():
							SCREAMING_SNAKE_CASE     						= self.model(**lowercase			).logits
				return logits







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= outputs.cpu().detach().numpy()
				SCREAMING_SNAKE_CASE     						= 0
				SCREAMING_SNAKE_CASE     						= 1
				return Image.fromarray((array * 2_5_5).astype(np.uinta			)			)

import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					   =       logging.get_logger(__name__)





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[Any]   ,     *lowercase      :List[str]   ,     **lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				warnings.warn(
				    '''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use YolosImageProcessor instead.'''   ,     lowercase   ,     )
				super().__init__(*lowercase   ,     **lowercase			)