code: 
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )
  
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import AutoImageProcessor
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    str=13    ,				UpperCAmelCase_			:    int=32    ,				UpperCAmelCase_			:    Optional[int]=2    ,				UpperCAmelCase_			:    Dict=3    ,				UpperCAmelCase_			:    str=16    ,				UpperCAmelCase_			:    int=[1, 2, 1]    ,				UpperCAmelCase_			:    Tuple=[2, 2, 4]    ,				UpperCAmelCase_			:    str=2    ,				UpperCAmelCase_			:    Dict=2.0    ,				UpperCAmelCase_			:    int=True    ,				UpperCAmelCase_			:    str=0.0    ,				UpperCAmelCase_			:    Any=0.0    ,				UpperCAmelCase_			:    Tuple=0.1    ,				UpperCAmelCase_			:    str="gelu"    ,				UpperCAmelCase_			:    Optional[Any]=False    ,				UpperCAmelCase_			:    List[str]=True    ,				UpperCAmelCase_			:    List[Any]=0.02    ,				UpperCAmelCase_			:    Union[str, Any]=1E-5    ,				UpperCAmelCase_			:    str=True    ,				UpperCAmelCase_			:    Dict=None    ,				UpperCAmelCase_			:    str=True    ,				UpperCAmelCase_			:    List[str]=10    ,				UpperCAmelCase_			:    str=8    ,				):
          lowerCAmelCase				:			List[str]    			=      parent
          lowerCAmelCase				:			Any    			=      batch_size
          lowerCAmelCase				:			Any    			=      image_size
          lowerCAmelCase				:			Optional[Any]    			=      patch_size
          lowerCAmelCase				:			Dict    			=      num_channels
          lowerCAmelCase				:			Any    			=      embed_dim
          lowerCAmelCase				:			Dict    			=      depths
          lowerCAmelCase				:			Optional[int]    			=      num_heads
          lowerCAmelCase				:			Optional[int]    			=      window_size
          lowerCAmelCase				:			List[str]    			=      mlp_ratio
          lowerCAmelCase				:			List[str]    			=      qkv_bias
          lowerCAmelCase				:			Union[str, Any]    			=      hidden_dropout_prob
          lowerCAmelCase				:			Dict    			=      attention_probs_dropout_prob
          lowerCAmelCase				:			Tuple    			=      drop_path_rate
          lowerCAmelCase				:			Tuple    			=      hidden_act
          lowerCAmelCase				:			List[str]    			=      use_absolute_embeddings
          lowerCAmelCase				:			Optional[Any]    			=      patch_norm
          lowerCAmelCase				:			List[Any]    			=      layer_norm_eps
          lowerCAmelCase				:			Any    			=      initializer_range
          lowerCAmelCase				:			Tuple    			=      is_training
          lowerCAmelCase				:			Dict    			=      scope
          lowerCAmelCase				:			Optional[int]    			=      use_labels
          lowerCAmelCase				:			Tuple    			=      type_sequence_label_size
          lowerCAmelCase				:			Optional[int]    			=      encoder_stride
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Optional[Any]    			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
  
          lowerCAmelCase				:			Optional[int]    			=      None
          if self.use_labels:
              lowerCAmelCase				:			Optional[int]    			=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size    )
  
          lowerCAmelCase				:			str    			=      self.get_config()
  
          return config, pixel_values, labels
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          return SwinConfig(
              image_size=self.image_size    ,				patch_size=self.patch_size    ,				num_channels=self.num_channels    ,				embed_dim=self.embed_dim    ,				depths=self.depths    ,				num_heads=self.num_heads    ,				window_size=self.window_size    ,				mlp_ratio=self.mlp_ratio    ,				qkv_bias=self.qkv_bias    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				drop_path_rate=self.drop_path_rate    ,				hidden_act=self.hidden_act    ,				use_absolute_embeddings=self.use_absolute_embeddings    ,				path_norm=self.patch_norm    ,				layer_norm_eps=self.layer_norm_eps    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[str]    ):
          lowerCAmelCase				:			List[str]    			=      TFSwinModel(config=UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      model(UpperCAmelCase_    )
  
          lowerCAmelCase				:			List[Any]    			=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
          lowerCAmelCase				:			Union[str, Any]    			=      int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )
  
          self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, expected_seq_len, expected_dim)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    List[str]    ):
          lowerCAmelCase				:			str    			=      TFSwinForMaskedImageModeling(config=UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      model(UpperCAmelCase_    )
          self.parent.assertEqual(
              result.logits.shape    ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)    )
  
          # test greyscale images
          lowerCAmelCase				:			int    			=      1
          lowerCAmelCase				:			List[Any]    			=      TFSwinForMaskedImageModeling(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Dict    			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          lowerCAmelCase				:			Tuple    			=      model(UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, 1, self.image_size, self.image_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    str    ):
          lowerCAmelCase				:			List[Any]    			=      self.type_sequence_label_size
          lowerCAmelCase				:			Optional[Any]    			=      TFSwinForImageClassification(UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)    )
  
          # test greyscale images
          lowerCAmelCase				:			Any    			=      1
          lowerCAmelCase				:			Optional[int]    			=      TFSwinForImageClassification(UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          lowerCAmelCase				:			Dict    			=      model(UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase				:			Union[str, Any]    			=      self.prepare_config_and_inputs()
          lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[Any]    			=      config_and_inputs
          lowerCAmelCase				:			Optional[Any]    			=      {'pixel_values': pixel_values}
          return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		Dict						=							(
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      lowerCAmelCase_							:		Any						=							(
          {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )
  
      lowerCAmelCase_							:		List[str]						=							False
      lowerCAmelCase_							:		Tuple						=							False
      lowerCAmelCase_							:		Dict						=							False
      lowerCAmelCase_							:		int						=							False
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Optional[int]    			=      TFSwinModelTester(self    )
          lowerCAmelCase				:			Any    			=      ConfigTester(self    ,				config_class=UpperCAmelCase_    ,				embed_dim=37    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          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			:    Tuple    ):
          return
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          lowerCAmelCase				:			str    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			int    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )
  
  
  
  
  
  
      @unittest.skip(reason='Swin does not use inputs_embeds'    )
      def 	lowercase__ (	self			:    List[Any]    ):
          pass
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase			,  lowerCAmelCase				:			Any    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			int    			=      model_class(UpperCAmelCase_    )
              self.assertIsInstance(model.get_input_embeddings()    ,				tf.keras.layers.Layer    )
              lowerCAmelCase				:			Tuple    			=      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCAmelCase_    ,				tf.keras.layers.Dense    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			List[str]    			=      model_class(UpperCAmelCase_    )
              lowerCAmelCase				:			Union[str, Any]    			=      inspect.signature(model.call    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowerCAmelCase				:			List[str]    			=      [*signature.parameters.keys()]
  
              lowerCAmelCase				:			Optional[Any]    			=      ['pixel_values']
              self.assertListEqual(arg_names[:1]    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs_for_common()
          lowerCAmelCase				:			str    			=      True
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			str    			=      True
              lowerCAmelCase				:			str    			=      False
              lowerCAmelCase				:			Optional[int]    			=      True
              lowerCAmelCase				:			List[Any]    			=      model_class(UpperCAmelCase_    )
              lowerCAmelCase				:			Optional[Any]    			=      model(**self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )    )
              lowerCAmelCase				:			List[str]    			=      outputs.attentions
              lowerCAmelCase				:			Optional[Any]    			=      len(self.model_tester.depths    )
              self.assertEqual(len(UpperCAmelCase_    )    ,				UpperCAmelCase_    )
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              lowerCAmelCase				:			Dict    			=      True
              lowerCAmelCase				:			int    			=      config.window_size**2
              lowerCAmelCase				:			List[Any]    			=      model_class(UpperCAmelCase_    )
              lowerCAmelCase				:			Tuple    			=      model(**self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )    )
              lowerCAmelCase				:			int    			=      outputs.attentions
              self.assertEqual(len(UpperCAmelCase_    )    ,				UpperCAmelCase_    )
  
              self.assertListEqual(
                  list(attentions[0].shape[-3:]    )    ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,				)
              lowerCAmelCase				:			int    			=      len(UpperCAmelCase_    )
  
              # Check attention is always last and order is fine
              lowerCAmelCase				:			Union[str, Any]    			=      True
              lowerCAmelCase				:			str    			=      True
              lowerCAmelCase				:			int    			=      model_class(UpperCAmelCase_    )
              lowerCAmelCase				:			Optional[int]    			=      model(**self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )    )
  
              if hasattr(self.model_tester    ,				'num_hidden_states_types'    ):
                  lowerCAmelCase				:			int    			=      self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  lowerCAmelCase				:			Optional[int]    			=      2
              self.assertEqual(out_len + added_hidden_states    ,				len(UpperCAmelCase_    )    )
  
              lowerCAmelCase				:			List[Any]    			=      outputs.attentions
  
              self.assertEqual(len(UpperCAmelCase_    )    ,				UpperCAmelCase_    )
  
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]    )    ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          lowerCAmelCase				:			List[str]    			=      model_class(UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      model(**self._prepare_for_class(UpperCAmelCase_    ,				UpperCAmelCase_    )    )
          lowerCAmelCase				:			Tuple    			=      outputs.hidden_states
  
          lowerCAmelCase				:			List[str]    			=      getattr(
              self.model_tester    ,				'expected_num_hidden_layers'    ,				len(self.model_tester.depths    ) + 1    )
          self.assertEqual(len(UpperCAmelCase_    )    ,				UpperCAmelCase_    )
  
          # Swin has a different seq_length
          lowerCAmelCase				:			str    			=      to_atuple(config.patch_size    )
  
          lowerCAmelCase				:			Tuple    			=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]    )    ,				[num_patches, self.model_tester.embed_dim]    ,				)
  
          lowerCAmelCase				:			List[Any]    			=      outputs.reshaped_hidden_states
          self.assertEqual(len(UpperCAmelCase_    )    ,				UpperCAmelCase_    )
  
          lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			Tuple    			=      reshaped_hidden_states[0].shape
  
          lowerCAmelCase				:			Optional[Any]    			=      tf.reshape(reshaped_hidden_states[0]    ,				(batch_size, num_channels, height * width)    )
          lowerCAmelCase				:			Tuple    			=      tf.transpose(UpperCAmelCase_    ,				(0, 2, 1)    )
  
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]    )    ,				[num_patches, self.model_tester.embed_dim]    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase			,  lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
          lowerCAmelCase				:			Any    			=      to_atuple(self.model_tester.image_size    )
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			Union[str, Any]    			=      True
              self.check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              lowerCAmelCase				:			Tuple    			=      True
  
              self.check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs_for_common()
          lowerCAmelCase				:			List[Any]    			=      3
  
          lowerCAmelCase				:			List[str]    			=      to_atuple(self.model_tester.image_size    )
          lowerCAmelCase				:			List[Any]    			=      to_atuple(config.patch_size    )
  
          lowerCAmelCase				:			str    			=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          lowerCAmelCase				:			Any    			=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
          for model_class in self.all_model_classes:
              lowerCAmelCase				:			Optional[int]    			=      True
              self.check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				(padded_height, padded_width)    )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              lowerCAmelCase				:			int    			=      True
              self.check_hidden_states_output(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				(padded_height, padded_width)    )
  
  
  
  
  
  
      @slow
      def 	lowercase__ (	self			:    Any    ):
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase				:			List[str]    			=      TFSwinModel.from_pretrained(UpperCAmelCase_    )
              self.assertIsNotNone(UpperCAmelCase_    )
  
  
  
  
  
  
  @require_vision
  @require_tf
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      @cached_property
      def 	lowercase__ (	self			:    Dict    ):
          return (
              AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
              if is_vision_available()
              else None
          )
  
  
  
  
  
  
      @slow
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          lowerCAmelCase				:			List[str]    			=      TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
          lowerCAmelCase				:			Optional[Any]    			=      self.default_image_processor
  
          lowerCAmelCase				:			Tuple    			=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
          lowerCAmelCase				:			List[Any]    			=      image_processor(images=UpperCAmelCase_    ,				return_tensors='tf'    )
  
          # forward pass
          lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    )
  
          # verify the logits
          lowerCAmelCase				:			Optional[Any]    			=      tf.TensorShape((1, 1000)    )
          self.assertEqual(outputs.logits.shape    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      tf.constant([-0.09_48, -0.64_54, -0.09_21]    )
          self.assertTrue(np.allclose(outputs.logits[0, :3]    ,				UpperCAmelCase_    ,				atol=1E-4    )    )
  
  
  
  
  
  
style_context: 
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		List[str]						=							(KDPMaAncestralDiscreteScheduler,)
      lowerCAmelCase_							:		Dict						=							10
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ,				**UpperCAmelCase_			:    List[Any]    ):
          lowerCAmelCase				:			int    			=      {
              'num_train_timesteps': 1100,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
          }
  
          config.update(**UpperCAmelCase_    )
          return config
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,				[0.00_02, 0.0_02, 0.02]    ):
              self.check_over_configs(beta_start=UpperCAmelCase_    ,				beta_end=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			Any    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			Optional[int]    			=      self.get_scheduler_config()
          lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    )
  
          lowerCAmelCase				:			Any    			=      torch.manual_seed(0    )
  
          lowerCAmelCase				:			List[Any]    			=      self.dummy_model()
          lowerCAmelCase				:			List[Any]    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )
  
          for i, t in enumerate(scheduler.timesteps    ):
              lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Any    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[Any]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			Union[str, Any]    			=      output.prev_sample
  
          lowerCAmelCase				:			List[Any]    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Any    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 1_38_49.38_77    ) < 1E-2
          assert abs(result_mean.item() - 18.03_31    ) < 5E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			Optional[Any]    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			List[Any]    			=      self.get_scheduler_config(prediction_type='v_prediction'    )
          lowerCAmelCase				:			Any    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    )
  
          lowerCAmelCase				:			Optional[int]    			=      self.dummy_model()
          lowerCAmelCase				:			Any    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[int]    			=      torch.manual_seed(0    )
  
          for i, t in enumerate(scheduler.timesteps    ):
              lowerCAmelCase				:			Dict    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[int]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			str    			=      output.prev_sample
  
          lowerCAmelCase				:			str    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Tuple    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 3_28.99_70    ) < 1E-2
          assert abs(result_mean.item() - 0.42_84    ) < 1E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			str    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			Dict    			=      self.get_scheduler_config()
          lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    ,				device=UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      torch.manual_seed(0    )
  
          lowerCAmelCase				:			Tuple    			=      self.dummy_model()
          lowerCAmelCase				:			Optional[Any]    			=      self.dummy_sample_deter.to(UpperCAmelCase_    ) * scheduler.init_noise_sigma
  
          for t in scheduler.timesteps:
              lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Dict    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			Dict    			=      output.prev_sample
  
          lowerCAmelCase				:			Union[str, Any]    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			str    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 1_38_49.38_18    ) < 1E-1
          assert abs(result_mean.item() - 18.03_31    ) < 1E-3
  
  
  
  
  
  