
"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'swinv2'

		lowercase__      =				{
		    'num_attention_heads': 'num_heads',
		    'num_hidden_layers': 'num_layers',
		}



		def __init__( self    ,		__a=2_24    ,		__a=4    ,		__a=3    ,		__a=96    ,		__a=[2, 2, 6, 2]    ,		__a=[3, 6, 12, 24]    ,		__a=7    ,		__a=4.0    ,		__a=True    ,		__a=0.0    ,		__a=0.0    ,		__a=0.1    ,		__a="gelu"    ,		__a=False    ,		__a=0.02    ,		__a=1e-5    ,		__a=32    ,		**__a    ,		)	->	Optional[int]:






							'''simple docstring'''
							super().__init__(**__a)

							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  embed_dim
							_UpperCamelCase										=  depths
							_UpperCamelCase										=  len(__a)
							_UpperCamelCase										=  num_heads
							_UpperCamelCase										=  window_size
							_UpperCamelCase										=  mlp_ratio
							_UpperCamelCase										=  qkv_bias
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  drop_path_rate
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  use_absolute_embeddings
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  encoder_stride
							# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							_UpperCamelCase										=  int(embed_dim * 2 ** (len(__a) - 1))
							_UpperCamelCase										=  (0, 0, 0, 0)








"""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     ):
		lowercase__      =				(
		    '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__      =				'CIDAS/clipseg-rd64-refined'
		lowercase__      =				'image_segmenter'
		lowercase__      =				CLIPSegForImageSegmentation

		lowercase__      =				['image', 'text']
		lowercase__      =				['image']



		def __init__( self    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






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



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												_UpperCamelCase										=  self.model(**__a).logits
							return logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.cpu().detach().numpy()
							_UpperCamelCase										=  0
							_UpperCamelCase										=  1
							return Image.fromarray((array * 2_55).astype(np.uinta))






