

import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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_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 transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           EfficientFormerForImageClassification,
           EfficientFormerForImageClassificationWithTeacher,
           EfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_efficientformer import (
           EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor



class  __snake_case					:

  def __init__(   self  ,						_A  ,						_A = 13  ,						_A = 64  ,						_A = 2  ,						_A = 3  ,						_A = 3  ,						_A = True  ,						_A = True  ,						_A = 128  ,						_A=[16, 32, 64, 128]  ,						_A = 7  ,						_A = 4  ,						_A = 37  ,						_A = "gelu"  ,						_A = 0.1  ,						_A = 0.1  ,						_A = 10  ,						_A = 0.0_2  ,						_A = 2  ,						_A = 1  ,						_A = 128  ,						_A = [2, 2, 2, 2]  ,						_A = 2  ,						_A = 2  ,						):
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  patch_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  use_labels
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  hidden_dropout_prob
      SCREAMING_SNAKE_CASE_													=  attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_													=  type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  encoder_stride
      SCREAMING_SNAKE_CASE_													=  num_attention_outputs
      SCREAMING_SNAKE_CASE_													=  embed_dim
      SCREAMING_SNAKE_CASE_													=  embed_dim + 1
      SCREAMING_SNAKE_CASE_													=  resolution
      SCREAMING_SNAKE_CASE_													=  depths
      SCREAMING_SNAKE_CASE_													=  hidden_sizes
      SCREAMING_SNAKE_CASE_													=  dim
      SCREAMING_SNAKE_CASE_													=  mlp_expansion_ratio

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      SCREAMING_SNAKE_CASE_													=  None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_													=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)

      SCREAMING_SNAKE_CASE_													=  self.get_config()
      return config, pixel_values, labels

  def   lowerCAmelCase__       (   self):
      return EfficientFormerConfig(
          image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						is_decoder=_A  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						resolution=self.resolution  ,						depths=self.depths  ,						hidden_sizes=self.hidden_sizes  ,						dim=self.dim  ,						mlp_expansion_ratio=self.mlp_expansion_ratio  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerModel(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_													=  model(_A)
      self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size))

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassification(_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						labels=_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      SCREAMING_SNAKE_CASE_													=  1
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassification(_A)
      model.to(_A)
      model.eval()

      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      SCREAMING_SNAKE_CASE_													=  model(_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_config_and_inputs()
      (
          (
          SCREAMING_SNAKE_CASE_
      )							,     (
          SCREAMING_SNAKE_CASE_
      )							,     (
          SCREAMING_SNAKE_CASE_
      )							,     
      )													=  config_and_inputs
      SCREAMING_SNAKE_CASE_													=  {'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		int  	=					(
      (
          EfficientFormerModel,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerForImageClassification,
      )
      if is_torch_available()
      else ()
  )
  __lowerCAmelCase       :		List[str]  	=					(
      {
          'feature-extraction': EfficientFormerModel,
          'image-classification': (
              EfficientFormerForImageClassification,
              EfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase       :		Optional[Any]  	=					False

  __lowerCAmelCase       :		List[Any]  	=					False
  __lowerCAmelCase       :		Tuple  	=					False
  __lowerCAmelCase       :		List[Any]  	=					False

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerModelTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(
          self  ,						config_class=_A  ,						has_text_modality=_A  ,						hidden_size=37)

  def   lowerCAmelCase__       (   self):
      self.config_tester.run_common_tests()

  @unittest.skip(reason='EfficientFormer does not use inputs_embeds')
  def   lowerCAmelCase__       (   self):
      pass

  @unittest.skip(reason='EfficientFormer does not support input and output embeddings')
  def   lowerCAmelCase__       (   self):
      pass

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

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

          SCREAMING_SNAKE_CASE_													=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						_A)

  def   lowerCAmelCase__       (   self):
      def check_hidden_states_output(_A  ,						_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.eval()

          with torch.no_grad():
              SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))

          SCREAMING_SNAKE_CASE_													=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          SCREAMING_SNAKE_CASE_													=  getattr(
              self.model_tester  ,						'expected_num_hidden_layers'  ,						self.model_tester.num_hidden_layers + 1)
          self.assertEqual(len(_A)  ,						_A)

          if hasattr(self.model_tester  ,						'encoder_seq_length'):
              SCREAMING_SNAKE_CASE_													=  self.model_tester.encoder_seq_length
              if hasattr(self.model_tester  ,						'chunk_length') and self.model_tester.chunk_length > 1:
                  SCREAMING_SNAKE_CASE_													=  seq_length * self.model_tester.chunk_length
          else:
              SCREAMING_SNAKE_CASE_													=  self.model_tester.seq_length

          self.assertListEqual(
              list(hidden_states[-1].shape[-2:])  ,						[seq_length, self.model_tester.hidden_size]  ,						)

          if config.is_encoder_decoder:
              SCREAMING_SNAKE_CASE_													=  outputs.decoder_hidden_states

              self.assertIsInstance(_A  ,						(list, tuple))
              self.assertEqual(len(_A)  ,						_A)
              SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'seq_length'  ,						_A)
              SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'decoder_seq_length'  ,						_A)

              self.assertListEqual(
                  list(hidden_states[-1].shape[-2:])  ,						[decoder_seq_length, self.model_tester.hidden_size]  ,						)

      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  True
          check_hidden_states_output(_A  ,						_A  ,						_A)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          SCREAMING_SNAKE_CASE_													=  True

          check_hidden_states_output(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=False):
      SCREAMING_SNAKE_CASE_													=  super()._prepare_for_class(_A  ,						_A  ,						return_labels=_A)

      if return_labels:
          if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
              del inputs_dict["labels"]

      return inputs_dict

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)

  @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet')
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)

  def   lowerCAmelCase__       (   self):
      if not self.model_tester.is_training:
          return

      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  True

      for model_class in self.all_model_classes:
          # EfficientFormerForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(_A)
              or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
          ):
              continue
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.train()
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  model(**_A).loss
          loss.backward()

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  [
          {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
          {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
          {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
      ]

      for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(_A),
              ]
              or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
          ):
              continue

          for problem_type in problem_types:
              with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""):
                  SCREAMING_SNAKE_CASE_													=  problem_type['title']
                  SCREAMING_SNAKE_CASE_													=  problem_type['num_labels']

                  SCREAMING_SNAKE_CASE_													=  model_class(_A)
                  model.to(_A)
                  model.train()

                  SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)

                  if problem_type["num_labels"] > 1:
                      SCREAMING_SNAKE_CASE_													=  inputs['labels'].unsqueeze(1).repeat(1  ,						problem_type['num_labels'])

                  SCREAMING_SNAKE_CASE_													=  inputs['labels'].to(problem_type['dtype'])

                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                  # they have the same size." which is a symptom something in wrong for the regression problem.
                  # See https://github.com/huggingface/transformers/issues/11780
                  with warnings.catch_warnings(record=_A) as warning_list:
                      SCREAMING_SNAKE_CASE_													=  model(**_A).loss
                  for w in warning_list:
                      if "Using a target size that is different to the input size" in str(w.message):
                          raise ValueError(
                              f"""Something is going wrong in the regression problem: intercepted {w.message}""")

                  loss.backward()

  @slow
  def   lowerCAmelCase__       (   self):
      for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE_													=  EfficientFormerModel.from_pretrained(_A)
          self.assertIsNotNone(_A)


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  True

      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'seq_length'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'encoder_seq_length'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'key_length'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  getattr(self.model_tester  ,						'chunk_length'  ,						_A)

      if chunk_length is not None and hasattr(self.model_tester  ,						'num_hashes'):
          SCREAMING_SNAKE_CASE_													=  encoder_seq_length * self.model_tester.num_hashes

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  True
          SCREAMING_SNAKE_CASE_													=  False
          SCREAMING_SNAKE_CASE_													=  True
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.eval()
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A)  ,						self.model_tester.num_attention_outputs)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          SCREAMING_SNAKE_CASE_													=  True
          SCREAMING_SNAKE_CASE_													=  model_class(_A)
          model.to(_A)
          model.eval()
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A)  ,						self.model_tester.num_attention_outputs)

          if chunk_length is not None:
              self.assertListEqual(
                  list(attentions[0].shape[-4:])  ,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]  ,						)
          else:
              self.assertListEqual(
                  list(attentions[0].shape[-3:])  ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,						)






def    _UpperCAmelCase    (					):





    """simple docstring"""


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





@require_torch
@require_vision
class  __snake_case					(      unittest.TestCase    ):

  @cached_property
  def   lowerCAmelCase__       (   self):
      return (
          EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300')
          if is_vision_available()
          else None
      )

  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300').to(
          _A)

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  (1, 1000)
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]).to(_A)
      self.assertTrue(torch.allclose(outputs.logits[0][:3]  ,						_A  ,						atol=1E-4))


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  EfficientFormerForImageClassificationWithTeacher.from_pretrained(
          'snap-research/efficientformer-l1-300').to(_A)

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  (1, 1000)
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]).to(_A)
      self.assertTrue(torch.allclose(outputs.logits[0][:3]  ,						_A  ,						atol=1E-4))





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					(HeunDiscreteScheduler,)
  __lowerCAmelCase       :		Optional[int]  	=					10

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A  ,						beta_end=_A)

  def   lowerCAmelCase__       (   self):
      for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_A)

  def   lowerCAmelCase__       (   self):
      for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_A  ,						clip_sample=_A)

  def   lowerCAmelCase__       (   self):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
          self.check_over_configs(prediction_type=_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if str(_A).startswith('cpu'):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      elif str(_A).startswith('mps'):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-2
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A  ,						use_karras_sigmas=_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      assert abs(result_sum.item() - 0.0_0_0_1_5) < 1E-2
      assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07) < 1E-2


