code: 
  import inspect
  import unittest
  
  from transformers import ResNetConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_backbone_common import BackboneTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
     import torch
     from torch import nn
  
     from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
     from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
     from PIL import Image
  
     from transformers import AutoImageProcessor
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Union[str, Any]	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       Dict=3	,      UpperCamelCase       :       Optional[int]=32	,      UpperCamelCase       :       Optional[Any]=3	,      UpperCamelCase       :       int=10	,      UpperCamelCase       :       str=[10, 20, 30, 40]	,      UpperCamelCase       :       List[Any]=[1, 1, 2, 1]	,      UpperCamelCase       :       int=True	,      UpperCamelCase       :       int=True	,      UpperCamelCase       :       str="relu"	,      UpperCamelCase       :       Optional[Any]=3	,      UpperCamelCase       :       Optional[Any]=None	,      UpperCamelCase       :       Any=["stage2", "stage3", "stage4"]	,      UpperCamelCase       :       List[Any]=[2, 3, 4]	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			parent
              _lowercase					:				str           =			batch_size
              _lowercase					:				List[str]           =			image_size
              _lowercase					:				Dict           =			num_channels
              _lowercase					:				int           =			embeddings_size
              _lowercase					:				Any           =			hidden_sizes
              _lowercase					:				Union[str, Any]           =			depths
              _lowercase					:				List[Any]           =			is_training
              _lowercase					:				Dict           =			use_labels
              _lowercase					:				Any           =			hidden_act
              _lowercase					:				Tuple           =			num_labels
              _lowercase					:				List[Any]           =			scope
              _lowercase					:				Union[str, Any]           =			len(UpperCamelCase		)
              _lowercase					:				List[Any]           =			out_features
              _lowercase					:				List[Any]           =			out_indices
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
              _lowercase					:				Dict           =			None
              if self.use_labels:
                     _lowercase					:				List[str]           =			ids_tensor([self.batch_size]	,      self.num_labels		)
  
              _lowercase					:				Optional[Any]           =			self.get_config()
  
              return config, pixel_values, labels
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return ResNetConfig(
                  num_channels=self.num_channels	,      embeddings_size=self.embeddings_size	,      hidden_sizes=self.hidden_sizes	,      depths=self.depths	,      hidden_act=self.hidden_act	,      num_labels=self.num_labels	,      out_features=self.out_features	,      out_indices=self.out_indices	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       Tuple	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			ResNetModel(config=UpperCamelCase		)
              model.to(UpperCamelCase		)
              model.eval()
              _lowercase					:				Tuple           =			model(UpperCamelCase		)
              # expected last hidden states: B, C, H // 32, W // 32
              self.parent.assertEqual(
                  result.last_hidden_state.shape	,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			self.num_labels
              _lowercase					:				int           =			ResNetForImageClassification(UpperCamelCase		)
              model.to(UpperCamelCase		)
              model.eval()
              _lowercase					:				Dict           =			model(UpperCamelCase	,      labels=UpperCamelCase		)
              self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			ResNetBackbone(config=UpperCamelCase		)
              model.to(UpperCamelCase		)
              model.eval()
              _lowercase					:				List[str]           =			model(UpperCamelCase		)
  
              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps		)	,      len(config.out_features		)		)
              self.parent.assertListEqual(list(result.feature_maps[0].shape		)	,      [self.batch_size, self.hidden_sizes[1], 4, 4]		)
  
              # verify channels
              self.parent.assertEqual(len(model.channels		)	,      len(config.out_features		)		)
              self.parent.assertListEqual(model.channels	,      config.hidden_sizes[1:]		)
  
              # verify backbone works with out_features=None
              _lowercase					:				Any           =			None
              _lowercase					:				Union[str, Any]           =			ResNetBackbone(config=UpperCamelCase		)
              model.to(UpperCamelCase		)
              model.eval()
              _lowercase					:				str           =			model(UpperCamelCase		)
  
              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps		)	,      1		)
              self.parent.assertListEqual(list(result.feature_maps[0].shape		)	,      [self.batch_size, self.hidden_sizes[-1], 1, 1]		)
  
              # verify channels
              self.parent.assertEqual(len(model.channels		)	,      1		)
              self.parent.assertListEqual(model.channels	,      [config.hidden_sizes[-1]]		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			self.prepare_config_and_inputs()
              _lowercase					:				Tuple           =			config_and_inputs
              _lowercase					:				List[Any]           =			{'''pixel_values''': pixel_values}
              return config, inputs_dict
  
  
  
  
  @require_torch
  class 						UpperCAmelCase__   (      A_      ,						A_      ,						unittest.TestCase		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  (
           (
               ResNetModel,
               ResNetForImageClassification,
               ResNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase_         =  (
           {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
           if is_torch_available()
           else {}
       )
  
       UpperCAmelCase_         =  True
       UpperCAmelCase_         =  False
       UpperCAmelCase_         =  False
       UpperCAmelCase_         =  False
       UpperCAmelCase_         =  False
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			ResNetModelTester(self		)
              _lowercase					:				List[Any]           =			ConfigTester(self	,      config_class=UpperCamelCase	,      has_text_modality=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """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   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return
  
  
  
  
       @unittest.skip(reason='''ResNet does not use inputs_embeds'''		)
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              pass
  
  
  
  
       @unittest.skip(reason='''ResNet does not support input and output embeddings'''		)
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              pass
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _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(UpperCamelCase		)
                     _lowercase					:				List[Any]           =			inspect.signature(model.forward		)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     _lowercase					:				Any           =			[*signature.parameters.keys()]
  
                     _lowercase					:				Any           =			['''pixel_values''']
                     self.assertListEqual(arg_names[:1]	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_backbone(*UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.model_tester.prepare_config_and_inputs_for_common()
  
              for model_class in self.all_model_classes:
                     _lowercase					:				Union[str, Any]           =			model_class(config=UpperCamelCase		)
                     for name, module in model.named_modules():
                            if isinstance(UpperCamelCase	,      (nn.BatchNormad, nn.GroupNorm)		):
                                   self.assertTrue(
                                       torch.all(module.weight == 1		)	,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'	,      )
                                   self.assertTrue(
                                       torch.all(module.bias == 0		)	,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              def check_hidden_states_output(UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       List[Any]	,      UpperCamelCase       :       Union[str, Any]		):
                     _lowercase					:				str           =			model_class(UpperCamelCase		)
                     model.to(UpperCamelCase		)
                     model.eval()
  
                     with torch.no_grad():
                            _lowercase					:				Any           =			model(**self._prepare_for_class(UpperCamelCase	,      UpperCamelCase		)		)
  
                     _lowercase					:				Optional[int]           =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
                     _lowercase					:				Dict           =			self.model_tester.num_stages
                     self.assertEqual(len(UpperCamelCase		)	,      expected_num_stages + 1		)
  
                     # ResNet'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					:				Union[str, Any]           =			self.model_tester.prepare_config_and_inputs_for_common()
              _lowercase					:				Union[str, Any]           =			['''basic''', '''bottleneck''']
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            _lowercase					:				List[Any]           =			layer_type
                            _lowercase					:				Union[str, Any]           =			True
                            check_hidden_states_output(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
                            # check that output_hidden_states also work using config
                            del inputs_dict["output_hidden_states"]
                            _lowercase					:				Dict           =			True
  
                            check_hidden_states_output(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       @unittest.skip(reason='''ResNet does not use feedforward chunking'''		)
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              pass
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*UpperCamelCase		)
  
  
  
  
       @slow
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _lowercase					:				List[str]           =			ResNetModel.from_pretrained(UpperCamelCase		)
                     self.assertIsNotNone(UpperCamelCase		)
  
  
  
  
  def   UpperCamelCase__   (    )						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[Any]           =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
         return image
  
  @require_torch
  @require_vision
  class 						UpperCAmelCase__   (      unittest.TestCase		):
  
  
  
       '''simple docstring'''
  
  
  
  
       @cached_property
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return (
                  AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
                  if is_vision_available()
                  else None
              )
  
  
  
  
       @slow
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(UpperCamelCase		)
  
              _lowercase					:				int           =			self.default_image_processor
              _lowercase					:				Optional[Any]           =			prepare_img()
              _lowercase					:				Optional[Any]           =			image_processor(images=UpperCamelCase	,      return_tensors='''pt'''		).to(UpperCamelCase		)
  
              # forward pass
              with torch.no_grad():
                     _lowercase					:				Optional[int]           =			model(**UpperCamelCase		)
  
              # verify the logits
              _lowercase					:				Optional[int]           =			torch.Size((1, 10_00)		)
              self.assertEqual(outputs.logits.shape	,      UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			torch.tensor([-11.1069, -9.7877, -8.3777]		).to(UpperCamelCase		)
  
              self.assertTrue(torch.allclose(outputs.logits[0, :3]	,      UpperCamelCase	,      atol=1E-4		)		)
  
  
  
  
  @require_torch
  class 						UpperCAmelCase__   (      A_      ,						unittest.TestCase		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  (ResNetBackbone,) if is_torch_available() else ()
       UpperCAmelCase_         =  False
       UpperCAmelCase_         =  ResNetConfig
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			ResNetModelTester(self		)
style_context: 
  
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  (DDIMScheduler,)
       UpperCAmelCase_         =  (('''eta''', 0.0), ('''num_inference_steps''', 5_0))
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]	,      **UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			{
                  '''num_train_timesteps''': 10_00,
                  '''beta_start''': 0.0001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }
  
              config.update(**UpperCamelCase		)
              return config
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str	,      **UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			self.scheduler_classes[0]
              _lowercase					:				Dict           =			self.get_scheduler_config(**UpperCamelCase		)
              _lowercase					:				List[str]           =			scheduler_class(**UpperCamelCase		)
  
              _lowercase       ,	_lowercase					:				int           =			10, 0.0
  
              _lowercase					:				List[str]           =			self.dummy_model()
              _lowercase					:				int           =			self.dummy_sample_deter
  
              scheduler.set_timesteps(UpperCamelCase		)
  
              for t in scheduler.timesteps:
                     _lowercase					:				Union[str, Any]           =			model(UpperCamelCase	,      UpperCamelCase		)
                     _lowercase					:				List[Any]           =			scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              return sample
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for timesteps in [1_00, 5_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			self.scheduler_classes[0]
              _lowercase					:				Tuple           =			self.get_scheduler_config(steps_offset=1		)
              _lowercase					:				Union[str, Any]           =			scheduler_class(**UpperCamelCase		)
              scheduler.set_timesteps(5		)
              assert torch.equal(scheduler.timesteps	,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]		)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,      [0.002, 0.02, 0.2, 2]		):
                     self.check_over_configs(beta_start=UpperCamelCase	,      beta_end=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              self.check_over_configs(thresholding=UpperCamelCase		)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=UpperCamelCase	,      prediction_type=UpperCamelCase	,      sample_max_value=UpperCamelCase	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t, num_inference_steps in zip([1, 10, 50]	,      [10, 50, 5_00]		):
                     self.check_over_forward(time_step=UpperCamelCase	,      num_inference_steps=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t, eta in zip([1, 10, 49]	,      [0.0, 0.5, 1.0]		):
                     self.check_over_forward(time_step=UpperCamelCase	,      eta=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.scheduler_classes[0]
              _lowercase					:				str           =			self.get_scheduler_config()
              _lowercase					:				int           =			scheduler_class(**UpperCamelCase		)
  
              assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_20	,      4_00		) - 0.14771		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_80	,      9_60		) - 0.32460		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87	,      4_86		) - 0.00979		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99	,      9_98		) - 0.02		)		) < 1E-5
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			self.full_loop()
  
              _lowercase					:				Optional[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 172.0067		) < 1E-2
              assert abs(result_mean.item() - 0.223967		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			self.full_loop(prediction_type='''v_prediction'''		)
  
              _lowercase					:				int           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 52.5302		) < 1E-2
              assert abs(result_mean.item() - 0.0684		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
              _lowercase					:				List[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Any           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 149.8295		) < 1E-2
              assert abs(result_mean.item() - 0.1951		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
              _lowercase					:				Optional[int]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Dict           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 149.0784		) < 1E-2
              assert abs(result_mean.item() - 0.1941		) < 1E-3