


import inspect
import unittest

from transformers import BitConfig
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image



class 		_lowercase		:

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Union[str, Any]=3							,   __lowerCAmelCase			:       Optional[Any]=32							,   __lowerCAmelCase			:       int=3							,   __lowerCAmelCase			:       Optional[int]=10							,   __lowerCAmelCase			:       str=[8, 16, 32, 64]							,   __lowerCAmelCase			:       Optional[Any]=[1, 1, 2, 1]							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       str=True							,   __lowerCAmelCase			:       Union[str, Any]="relu"							,   __lowerCAmelCase			:       List[Any]=3							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Union[str, Any]=["stage2", "stage3", "stage4"]							,   __lowerCAmelCase			:       Tuple=[2, 3, 4]							,   __lowerCAmelCase			:       Any=1							,   )       ->				str:


    """simple docstring"""






    a						=		parent
    a						=		batch_size
    a						=		image_size
    a						=		num_channels
    a						=		embeddings_size
    a						=		hidden_sizes
    a						=		depths
    a						=		is_training
    a						=		use_labels
    a						=		hidden_act
    a						=		num_labels
    a						=		scope
    a						=		len(__A							)
    a						=		out_features
    a						=		out_indices
    a						=		num_groups

   def 		A							(      self			:       int							)       ->				Optional[int]:


    """simple docstring"""






    a						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

    a						=		None
    if self.use_labels:
     a						=		ids_tensor([self.batch_size]							,   self.num_labels							)

    a						=		self.get_config()

    return config, pixel_values, labels

   def 		A							(      self			:       List[Any]							)       ->				int:


    """simple docstring"""






    return BitConfig(
        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							,   num_groups=self.num_groups							,   )

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    a						=		BitModel(config=__A							)
    model.to(__A							)
    model.eval()
    a						=		model(__A							)
    self.parent.assertEqual(
        result.last_hidden_state.shape							,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,   )

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		self.num_labels
    a						=		BitForImageClassification(__A							)
    model.to(__A							)
    model.eval()
    a						=		model(__A							,   labels=__A							)
    self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		BitBackbone(config=__A							)
    model.to(__A							)
    model.eval()
    a						=		model(__A							)

    # 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
    a						=		None
    a						=		BitBackbone(config=__A							)
    model.to(__A							)
    model.eval()
    a						=		model(__A							)

    # 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 		A							(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values}
    return config, inputs_dict



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   _UpperCAmelCase     	=			(
       {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    a						=		BitModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__A							,   has_text_modality=__A							)

   def 		A							(      self			:       Tuple							)       ->				Any:


    """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 		A							(      self			:       int							)       ->				str:


    """simple docstring"""






    return

   @unittest.skip(reason="Bit does not output attentions"							)
   def 		A							(      self			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






    pass

   @unittest.skip(reason="Bit does not use inputs_embeds"							)
   def 		A							(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    pass

   @unittest.skip(reason="Bit does not support input and output embeddings"							)
   def 		A							(      self			:       Optional[int]							)       ->				Optional[int]:


    """simple docstring"""






    pass

   def 		A							(      self			:       List[str]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs_for_common()

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

     a						=		["pixel_values"]
     self.assertListEqual(arg_names[:1]							,   __A							)

   def 		A							(      self			:       List[str]							)       ->				Tuple:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__A							)

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


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*__A							)

   def 		A							(      self			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a						=		model_class(config=__A							)
     for name, module in model.named_modules():
      if isinstance(__A							,   (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 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    def check_hidden_states_output(__lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]							):
     a						=		model_class(__A							)
     model.to(__A							)
     model.eval()

     with torch.no_grad():
      a						=		model(**self._prepare_for_class(__A							,   __A							)							)

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

     a						=		self.model_tester.num_stages
     self.assertEqual(len(__A							)							,   expected_num_stages + 1							)

     # Bit'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]							,   )

    a						=		self.model_tester.prepare_config_and_inputs_for_common()
    a						=		["preactivation", "bottleneck"]
    for model_class in self.all_model_classes:
     for layer_type in layers_type:
      a						=		layer_type
      a						=		True
      check_hidden_states_output(__A							,   __A							,   __A							)

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

      check_hidden_states_output(__A							,   __A							,   __A							)

   @unittest.skip(reason="Bit does not use feedforward chunking"							)
   def 		A							(      self			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    pass

   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__A							)




   @slow
   def 		A							(      self			:       Any							)       ->				Optional[Any]:


    """simple docstring"""






    for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a						=		BitModel.from_pretrained(__A							)
     self.assertIsNotNone(__A							)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image



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

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				Dict:


    """simple docstring"""






    return (
        BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
    )




   @slow
   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    a						=		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__A							)

    a						=		self.default_image_processor
    a						=		prepare_img()
    a						=		image_processor(images=__A							,   return_tensors="pt"							).to(__A							)

    # forward pass
    with torch.no_grad():
     a						=		model(**__A							)

    # verify the logits
    a						=		torch.Size((1, 1000)							)
    self.assertEqual(outputs.logits.shape							,   __A							)

    a						=		torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]							).to(__A							)

    self.assertTrue(torch.allclose(outputs.logits[0, :3]							,   __A							,   atol=1E-4							)							)



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(BitBackbone,) if is_torch_available() else ()
   _UpperCAmelCase     	=			BitConfig

   _UpperCAmelCase     	=			False

   def 		A							(      self			:       Optional[Any]							)       ->				str:


    """simple docstring"""






    a						=		BitModelTester(self							)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(UniPCMultistepScheduler,)
   _UpperCAmelCase     	=			(('''num_inference_steps''', 25),)

   def 		A							(      self			:       List[Any]							,   **__lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1000,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "solver_order": 2,
        "solver_type": "bh2",
    }

    config.update(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]=0							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)
     # copy over dummy past residuals
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      new_scheduler.set_timesteps(__lowerCAmelCase							)
      # copy over dummy past residuals
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a		,  a						=		sample, sample
     for t in range(__lowerCAmelCase							,   time_step + scheduler.config.solver_order + 1							):
      a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
      a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

      assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]=0							,   **__lowerCAmelCase			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)

     # copy over dummy past residuals (must be after setting timesteps)
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      # copy over dummy past residuals
      new_scheduler.set_timesteps(__lowerCAmelCase							)

      # copy over dummy past residual (must be after setting timesteps)
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any=None							,   **__lowerCAmelCase			:       List[str]							)       ->				Any:


    """simple docstring"""






    if scheduler is None:
     a						=		self.scheduler_classes[0]
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(**__lowerCAmelCase							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    return sample

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)

    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)

     a						=		self.dummy_sample
     a						=		0.1 * sample

     if num_inference_steps is not None and hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      scheduler.set_timesteps(__lowerCAmelCase							)
     elif num_inference_steps is not None and not hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      a						=		num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     a						=		scheduler.timesteps[5]
     a						=		scheduler.timesteps[6]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     self.assertEqual(output_a.shape							,   sample.shape							)
     self.assertEqual(output_a.shape							,   output_a.shape							)

   def 		A							(      self			:       List[str]							)       ->				Dict:


    """simple docstring"""






    a						=		UniPCMultistepScheduler(**self.get_scheduler_config()							)
    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

    a						=		DPMSolverSinglestepScheduler.from_config(scheduler.config							)
    a						=		DEISMultistepScheduler.from_config(scheduler.config							)
    a						=		DPMSolverMultistepScheduler.from_config(scheduler.config							)
    a						=		UniPCMultistepScheduler.from_config(scheduler.config							)

    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

   def 		A							(      self			:       List[Any]							)       ->				Dict:


    """simple docstring"""






    for timesteps in [25, 50, 100, 999, 1000]:
     self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






    self.check_over_configs(thresholding=__lowerCAmelCase							)
    for order in [1, 2, 3]:
     for solver_type in ["bh1", "bh2"]:
      for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            thresholding=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   sample_max_value=__lowerCAmelCase							,   solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   )

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






    for solver_type in ["bh1", "bh2"]:
     for order in [1, 2, 3]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       a						=		self.full_loop(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       assert not torch.isnan(__lowerCAmelCase							).any(), "Samples have nan numbers"

   def 		A							(      self			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    self.check_over_configs(lower_order_final=__lowerCAmelCase							)
    self.check_over_configs(lower_order_final=__lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
     self.check_over_forward(num_inference_steps=__lowerCAmelCase							,   time_step=0							)

   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop()
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

   def 		A							(      self			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop(prediction_type="v_prediction"							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.1_0_1_4							) < 1E-3

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


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(thresholding=__lowerCAmelCase							,   dynamic_thresholding_ratio=0							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter.half()
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    assert sample.dtype == torch.floataa




   def 		A							(      self			:       List[str]							,   **__lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

     scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
     assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps

