


"""simple docstring"""





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=2 ,				snake_case_=True ,				snake_case_=False ,				snake_case_=10 ,				snake_case_=3 ,				snake_case_=32 * 4 ,				snake_case_=32 * 6 ,				snake_case_=4 ,				snake_case_=32 ,				)		->       str:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_auxiliary_loss
       UpperCamelCase__         =    num_queries
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    min_size
       UpperCamelCase__         =    max_size
       UpperCamelCase__         =    num_labels
       UpperCamelCase__         =    mask_feature_size

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
           lowerCamelCase__      )

       UpperCamelCase__         =    torch.ones([self.batch_size, self.min_size, self.max_size] ,				device=lowerCamelCase__      )

       UpperCamelCase__         =    (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,				device=lowerCamelCase__      ) > 0.5
       ).float()
       UpperCamelCase__         =    (torch.rand((self.batch_size, self.num_labels) ,				device=lowerCamelCase__      ) > 0.5).long()

       UpperCamelCase__         =    self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       return MaskFormerConfig.from_backbone_and_decoder_configs(
           backbone_config=SwinConfig(
               depths=[1, 1, 1, 1] ,				) ,				decoder_config=DetrConfig(
               decoder_ffn_dim=128 ,				num_queries=self.num_queries ,				decoder_attention_heads=2 ,				d_model=self.mask_feature_size ,				) ,				mask_feature_size=self.mask_feature_size ,				fpn_feature_size=self.mask_feature_size ,				num_channels=self.num_channels ,				num_labels=self.num_labels ,				)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       UpperCamelCase__         =    {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
       return config, inputs_dict

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       UpperCamelCase__         =    output.encoder_hidden_states
       UpperCamelCase__         =    output.pixel_decoder_hidden_states
       UpperCamelCase__         =    output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(lowerCamelCase__      ) ,				len(config.backbone_config.depths      )      )
       self.parent.assertTrue(len(lowerCamelCase__      ) ,				len(config.backbone_config.depths      )      )
       self.parent.assertTrue(len(lowerCamelCase__      ) ,				config.decoder_config.decoder_layers      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_=False      )		->       Tuple:






       with torch.no_grad():
           UpperCamelCase__         =    MaskFormerModel(config=lowerCamelCase__      )
           model.to(lowerCamelCase__      )
           model.eval()

           UpperCamelCase__         =    model(pixel_values=lowerCamelCase__ ,				pixel_mask=lowerCamelCase__      )
           UpperCamelCase__         =    model(lowerCamelCase__ ,				output_hidden_states=lowerCamelCase__      )
       # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
       # encoder and pixel decoder
       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape ,				(self.batch_size, self.num_queries, self.mask_feature_size) ,				)
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None      )
       self.parent.assertTrue(output.encoder_last_hidden_state is not None      )

       if output_hidden_states:
           self.check_output_hidden_state(lowerCamelCase__ ,				lowerCamelCase__      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Dict:
       UpperCamelCase__         =    MaskFormerForInstanceSegmentation(config=lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.eval()

       def comm_check_on_output(snake_case_      ):
           # let's still check that all the required stuff is there
           self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None      )
           self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None      )
           self.parent.assertTrue(result.encoder_last_hidden_state is not None      )
           # okay, now we need to check the logits shape
           # due to the encoder compression, masks have a //4 spatial size
           self.parent.assertEqual(
               result.masks_queries_logits.shape ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,				)
           # + 1 for null class
           self.parent.assertEqual(
               result.class_queries_logits.shape ,				(self.batch_size, self.num_queries, self.num_labels + 1)      )

       with torch.no_grad():
           UpperCamelCase__         =    model(pixel_values=lowerCamelCase__ ,				pixel_mask=lowerCamelCase__      )
           UpperCamelCase__         =    model(lowerCamelCase__      )

           comm_check_on_output(lowerCamelCase__      )

           UpperCamelCase__         =    model(
               pixel_values=lowerCamelCase__ ,				pixel_mask=lowerCamelCase__ ,				mask_labels=lowerCamelCase__ ,				class_labels=lowerCamelCase__      )

       comm_check_on_output(lowerCamelCase__      )

       self.parent.assertTrue(result.loss is not None      )
       self.parent.assertEqual(result.loss.shape ,				torch.Size([1]      )      )







@require_torch
class    __lowerCamelCase    (  __lowerCAmelCase							,	__lowerCAmelCase							,	unittest.TestCase  ):
   a      :				Optional[int] 						=(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
   a      :				Dict 						=(
       {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
       if is_torch_available()
       else {}
   )

   a      :				Union[str, Any] 						=False
   a      :				Optional[Any] 						=False
   a      :				Union[str, Any] 						=False
   a      :				Optional[Any] 						=False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    MaskFormerModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=lowerCamelCase__ ,				has_text_modality=lowerCamelCase__      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(lowerCamelCase__ ,				**lowerCamelCase__ ,				output_hidden_states=lowerCamelCase__      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCamelCase__      )

   @unittest.skip(reason='MaskFormer does not use inputs_embeds'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       pass

   @unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       pass

   @unittest.skip(reason='MaskFormer is not a generative model'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       pass

   @unittest.skip(reason='MaskFormer does not use token embeddings'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       pass

   @require_torch_multi_gpu
   @unittest.skip(
       reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       pass

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       pass

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(lowerCamelCase__      )
           UpperCamelCase__         =    inspect.signature(model.forward      )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__         =    [*signature.parameters.keys()]

           UpperCamelCase__         =    ['''pixel_values''']
           self.assertListEqual(arg_names[:1] ,				lowerCamelCase__      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:






       for model_name in ["facebook/maskformer-swin-small-coco"]:
           UpperCamelCase__         =    MaskFormerModel.from_pretrained(lowerCamelCase__      )
           self.assertIsNotNone(lowerCamelCase__      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    (self.model_tester.min_size,) * 2
       UpperCamelCase__         =    {
           '''pixel_values''': torch.randn((2, 3, *size) ,				device=lowerCamelCase__      ),
           '''mask_labels''': torch.randn((2, 10, *size) ,				device=lowerCamelCase__      ),
           '''class_labels''': torch.zeros(2 ,				10 ,				device=lowerCamelCase__      ).long(),
       }

       UpperCamelCase__         =    MaskFormerForInstanceSegmentation(MaskFormerConfig()      ).to(lowerCamelCase__      )
       UpperCamelCase__         =    model(**lowerCamelCase__      )
       self.assertTrue(outputs.loss is not None      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(lowerCamelCase__ ,				**lowerCamelCase__ ,				output_hidden_states=lowerCamelCase__      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(lowerCamelCase__      ).to(lowerCamelCase__      )
           UpperCamelCase__         =    model(**lowerCamelCase__ ,				output_attentions=lowerCamelCase__      )
           self.assertTrue(outputs.attentions is not None      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:






       if not self.model_tester.is_training:
           return
       # only MaskFormerForInstanceSegmentation has the loss
       UpperCamelCase__         =    self.all_model_classes[1]
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()

       UpperCamelCase__         =    model_class(lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.train()

       UpperCamelCase__         =    model(lowerCamelCase__ ,				mask_labels=lowerCamelCase__ ,				class_labels=lowerCamelCase__      ).loss
       loss.backward()







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.all_model_classes[1]
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       UpperCamelCase__         =    True
       UpperCamelCase__         =    True

       UpperCamelCase__         =    model_class(lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.train()

       UpperCamelCase__         =    model(lowerCamelCase__ ,				mask_labels=lowerCamelCase__ ,				class_labels=lowerCamelCase__      )

       UpperCamelCase__         =    outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       UpperCamelCase__         =    outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()
       # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
       UpperCamelCase__         =    outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       UpperCamelCase__         =    outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=lowerCamelCase__      )

       self.assertIsNotNone(encoder_hidden_states.grad      )
       self.assertIsNotNone(pixel_decoder_hidden_states.grad      )
       self.assertIsNotNone(transformer_decoder_hidden_states.grad      )
       self.assertIsNotNone(attentions.grad      )







A__					: Optional[int]=	1E-4







def   lowerCAmelCase_( )					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image





@require_vision
@slow
class    __lowerCamelCase    (  unittest.TestCase  ):

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       return (
           MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'      )
           if is_vision_available()
           else None
       )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'      ).to(lowerCamelCase__      )
       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(lowerCamelCase__ ,				return_tensors='pt'      ).to(lowerCamelCase__      )
       UpperCamelCase__         =    inputs['''pixel_values'''].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
       # check size
       self.assertEqual(lowerCamelCase__ ,				(1, 3, 800, 1088)      )

       with torch.no_grad():
           UpperCamelCase__         =    model(**lowerCamelCase__      )

       UpperCamelCase__         =    torch.tensor(
           [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]      ).to(lowerCamelCase__      )
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )

       UpperCamelCase__         =    torch.tensor(
           [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]      ).to(lowerCamelCase__      )
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )

       UpperCamelCase__         =    torch.tensor(
           [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]      ).to(lowerCamelCase__      )
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    (
           MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'      )
           .to(lowerCamelCase__      )
           .eval()
       )
       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(lowerCamelCase__ ,				return_tensors='pt'      ).to(lowerCamelCase__      )
       UpperCamelCase__         =    inputs['''pixel_values'''].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
       # check size
       self.assertEqual(lowerCamelCase__ ,				(1, 3, 800, 1088)      )

       with torch.no_grad():
           UpperCamelCase__         =    model(**lowerCamelCase__      )
       # masks_queries_logits
       UpperCamelCase__         =    outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,				)
       UpperCamelCase__         =    [
           [-1.3_737_124, -1.7_724_937, -1.9_364_233],
           [-1.5_977_281, -1.9_867_939, -2.1_523_695],
           [-1.5_795_398, -1.9_269_832, -2.093_942],
       ]
       UpperCamelCase__         =    torch.tensor(lowerCamelCase__      ).to(lowerCamelCase__      )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )
       # class_queries_logits
       UpperCamelCase__         =    outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
       UpperCamelCase__         =    torch.tensor(
           [
               [1.6512E00, -5.2572E00, -3.3519E00],
               [3.6169E-02, -5.9025E00, -2.9313E00],
               [1.0766E-04, -7.7630E00, -5.1263E00],
           ]      ).to(lowerCamelCase__      )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    (
           MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'      )
           .to(lowerCamelCase__      )
           .eval()
       )
       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(lowerCamelCase__ ,				return_tensors='pt'      ).to(lowerCamelCase__      )
       UpperCamelCase__         =    inputs['''pixel_values'''].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
       # check size
       self.assertEqual(lowerCamelCase__ ,				(1, 3, 800, 1088)      )

       with torch.no_grad():
           UpperCamelCase__         =    model(**lowerCamelCase__      )
       # masks_queries_logits
       UpperCamelCase__         =    outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,				)
       UpperCamelCase__         =    [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
       UpperCamelCase__         =    torch.tensor(lowerCamelCase__      ).to(lowerCamelCase__      )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )
       # class_queries_logits
       UpperCamelCase__         =    outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
       UpperCamelCase__         =    torch.tensor(
           [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]      ).to(lowerCamelCase__      )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,				lowerCamelCase__ ,				atol=lowerCamelCase__      )      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    (
           MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'      )
           .to(lowerCamelCase__      )
           .eval()
       )
       UpperCamelCase__         =    self.default_image_processor

       UpperCamelCase__         =    image_processor(
           [np.zeros((3, 800, 1333)      ), np.zeros((3, 800, 1333)      )] ,				segmentation_maps=[np.zeros((384, 384)      ).astype(np.floataa      ), np.zeros((384, 384)      ).astype(np.floataa      )] ,				return_tensors='pt' ,				)

       UpperCamelCase__         =    inputs['''pixel_values'''].to(lowerCamelCase__      )
       UpperCamelCase__         =    [el.to(lowerCamelCase__      ) for el in inputs['''mask_labels''']]
       UpperCamelCase__         =    [el.to(lowerCamelCase__      ) for el in inputs['''class_labels''']]

       with torch.no_grad():
           UpperCamelCase__         =    model(**lowerCamelCase__      )

       self.assertTrue(outputs.loss is not None      )












"""simple docstring"""





import warnings
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


A__					: str=	logging.get_logger(__name__)

A__					: List[Any]=	{
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class    __lowerCamelCase    (  _a  ):
   a      :				Any 						="""segformer"""

   def __init__(					self ,				snake_case_=3 ,				snake_case_=4 ,				snake_case_=[2, 2, 2, 2] ,				snake_case_=[8, 4, 2, 1] ,				snake_case_=[32, 64, 160, 256] ,				snake_case_=[7, 3, 3, 3] ,				snake_case_=[4, 2, 2, 2] ,				snake_case_=[1, 2, 5, 8] ,				snake_case_=[4, 4, 4, 4] ,				snake_case_="gelu" ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.1 ,				snake_case_=0.02 ,				snake_case_=0.1 ,				snake_case_=1E-6 ,				snake_case_=256 ,				snake_case_=255 ,				**snake_case_ ,				)		->       Tuple:
       super().__init__(**snake_case_      )

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
           warnings.warn(
               'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
               ' removed, as the behaviour will default to that of reshape_last_stage = True.' ,				snake_case_ ,				)

       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    num_encoder_blocks
       UpperCamelCase__         =    depths
       UpperCamelCase__         =    sr_ratios
       UpperCamelCase__         =    hidden_sizes
       UpperCamelCase__         =    patch_sizes
       UpperCamelCase__         =    strides
       UpperCamelCase__         =    mlp_ratios
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    classifier_dropout_prob
       UpperCamelCase__         =    initializer_range
       UpperCamelCase__         =    drop_path_rate
       UpperCamelCase__         =    layer_norm_eps
       UpperCamelCase__         =    decoder_hidden_size
       UpperCamelCase__         =    kwargs.get('reshape_last_stage' ,				snake_case_      )
       UpperCamelCase__         =    semantic_loss_ignore_index







class    __lowerCamelCase    (  _a  ):
   a      :				Any 						=version.parse("""1.11"""  )

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

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-4







   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12




