




"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}

class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''swin'''

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







   def __init__(		self       ,					lowerCamelCase_=2_2_4       ,					lowerCamelCase_=4       ,					lowerCamelCase_=3       ,					lowerCamelCase_=9_6       ,					lowerCamelCase_=[2, 2, 6, 2]       ,					lowerCamelCase_=[3, 6, 1_2, 2_4]       ,					lowerCamelCase_=7       ,					lowerCamelCase_=4.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=False       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1e-5       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					**lowerCamelCase_       ,					)				->   Any:
       super().__init__(**lowerCamelCase_)

       UpperCamelCase              =	image_size
       UpperCamelCase              =	patch_size
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	embed_dim
       UpperCamelCase              =	depths
       UpperCamelCase              =	len(lowerCamelCase_)
       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 Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       UpperCamelCase              =	int(embed_dim * 2 ** (len(lowerCamelCase_) - 1))
       UpperCamelCase              =	['''stem'''] + [F'stage{idx}' for idx in range(1       ,					len(lowerCamelCase_) + 1)]
       UpperCamelCase       ,	UpperCamelCase              =	get_aligned_output_features_output_indices(
           out_features=lowerCamelCase_       ,					out_indices=lowerCamelCase_       ,					stage_names=self.stage_names)

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       version.parse('''1.11'''			)







   @property
   def   UpperCAmelCase__			(		self)				->   Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ])







   @property
   def   UpperCAmelCase__			(		self)				->   float:
       return 1e-4



# 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							(       __A							):


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


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



