def      _a					(   lowercase__	:      list			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		len(lowercase__			)
     for _ in range(lowercase__			):
          for i in range(_ % 2  ,	arr_size - 1  ,	2			):
               if arr[i + 1] < arr[i]:
                    SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       list(range(10, 0, -1))
      print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")





"""simple docstring"""




import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import UperNetForSemanticSegmentation
  from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class       __magic_name__							:
       def __init__(  self			,					A_			,					A_=13			,					A_=32			,					A_=3			,					A_=4			,					A_=[10, 20, 30, 40]			,					A_=[2, 2, 3, 2]			,					A_=True			,					A_=True			,					A_=37			,					A_="gelu"			,					A_=10			,					A_=0.02			,					A_=["stage2", "stage3", "stage4"]			,					A_=3			,					A_=None			,					)  ->						List[str]:





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      Union[str, Any]   			=  batch_size
         _lowercase:      Tuple   			=  image_size
         _lowercase:      Tuple   			=  num_channels
         _lowercase:      Optional[int]   			=  num_stages
         _lowercase:      List[Any]   			=  hidden_sizes
         _lowercase:      Dict   			=  depths
         _lowercase:      int   			=  is_training
         _lowercase:      Tuple   			=  use_labels
         _lowercase:      Any   			=  intermediate_size
         _lowercase:      Union[str, Any]   			=  hidden_act
         _lowercase:      str   			=  type_sequence_label_size
         _lowercase:      int   			=  initializer_range
         _lowercase:      List[str]   			=  out_features
         _lowercase:      Any   			=  num_labels
         _lowercase:      Any   			=  scope
         _lowercase:      str   			=  num_stages
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _lowercase:      Any   			=  None
         if self.use_labels:
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.type_sequence_label_size							)

         _lowercase:      Tuple   			=  self.get_config()

         return config, pixel_values, labels
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         return ConvNextConfig(
             num_channels=self.num_channels			,					num_stages=self.num_stages			,					hidden_sizes=self.hidden_sizes			,					depths=self.depths			,					is_training=self.is_training			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					out_features=self.out_features			,					)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return UperNetConfig(
             backbone_config=self.get_backbone_config()			,					hidden_size=512			,					pool_scales=[1, 2, 3, 6]			,					use_auxiliary_head=A_			,					auxiliary_loss_weight=0.4			,					auxiliary_in_channels=40			,					auxiliary_channels=256			,					auxiliary_num_convs=1			,					auxiliary_concat_input=A_			,					loss_ignore_index=255			,					num_labels=self.num_labels			,					)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_							)  ->						int:





         """simple docstring"""




         _lowercase:      List[str]   			=  UperNetForSemanticSegmentation(config=A_							)
         model.to(A_							)
         model.eval()
         _lowercase:      int   			=  model(A_							)
         self.parent.assertEqual(
             result.logits.shape			,					(self.batch_size, self.num_labels, self.image_size, self.image_size)							)



       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.prepare_config_and_inputs()
         (
             (
             _lowercase
         )			,					(
             _lowercase
         )			,					(
             _lowercase
         )			,					
         ):      Tuple   			=  config_and_inputs
         _lowercase:      str   			=  {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
       UpperCamelCase_ =		{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  UperNetModelTester(self							)
         _lowercase:      Optional[Any]   			=  ConfigTester(self			,					config_class=A_			,					has_text_modality=A_			,					hidden_size=37							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase			,					_lowercase:      Union[str, Any]   			=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase:      Optional[Any]   			=  model_class(A_							)
           _lowercase:      int   			=  inspect.signature(model.forward							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowercase:      Dict   			=  [*signature.parameters.keys()]

           _lowercase:      int   			=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*A_							)
       @unittest.skip(reason='''UperNet does not use inputs_embeds'''							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not support input and output embeddings'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




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





         """simple docstring"""




         pass
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         def check_hidden_states_output(A_			,					A_			,					A_							):
           _lowercase:      Dict   			=  model_class(A_							)
           model.to(A_							)
           model.eval()

           with torch.no_grad():
             _lowercase:      List[str]   			=  model(**self._prepare_for_class(A_			,					A_							)							)

           _lowercase:      Tuple   			=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase:      Union[str, Any]   			=  self.model_tester.num_stages
           self.assertEqual(len(A_							)			,					expected_num_stages + 1							)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)			,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,					)

         _lowercase			,					_lowercase:      Dict   			=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase:      Optional[int]   			=  True
           check_hidden_states_output(A_			,					A_			,					A_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:      Union[str, Any]   			=  True

           check_hidden_states_output(A_			,					A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:      int   			=  _config_zero_init(A_							)
         _lowercase:      Optional[Any]   			=  _config_zero_init(configs_no_init.backbone_config							)
         for model_class in self.all_model_classes:
           _lowercase:      Any   			=  model_class(config=A_							)
           for name, param in model.named_parameters():
             if param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item()			,					[0.0, 1.0]			,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,					)
       @unittest.skip(reason='''UperNet does not have tied weights'''							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         pass



       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      Union[str, Any]   			=  UperNetForSemanticSegmentation.from_pretrained(A_							)
           self.assertIsNotNone(A_							)




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  hf_hub_download(
      repo_id='''hf-internal-testing/fixtures_ade20k'''			,					repo_type='''dataset'''			,					filename='''ADE_val_00000001.jpg'''    )
  _lowercase:      Optional[int]   			=  Image.open(_UpperCamelCase    ).convert('''RGB'''    )
  return image







@require_torch
@require_vision
@slow
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
         _lowercase:      Optional[Any]   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(A_							)

         _lowercase:      List[Any]   			=  prepare_img()
         _lowercase:      Any   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Union[str, Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Optional[Any]   			=  torch.tensor(
             [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Tuple   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
         _lowercase:      str   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(A_							)

         _lowercase:      Union[str, Any]   			=  prepare_img()
         _lowercase:      Optional[Any]   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Optional[Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Tuple   			=  torch.tensor(
             [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)
