

"""simple docstring"""


import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

  from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=30	,    _a=2	,    _a=3	,    _a=True	,    _a=True	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=10	,    _a=0.02	,    _a=None	,    _a=2	,    )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	parent
           SCREAMING_SNAKE_CASE__   :      str      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Dict      		=	image_size
           SCREAMING_SNAKE_CASE__   :      int      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Dict      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      str      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      str      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Dict      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      int      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	scope
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	encoder_stride

           # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(image_size // patch_size) ** 2
           SCREAMING_SNAKE_CASE__   :      str      		=	num_patches + 1

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size]	,    self.type_sequence_label_size  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_config()

           return config, pixel_values, labels

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return ViTConfig(
               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=__lowerCamelCase	,    initializer_range=self.initializer_range	,    encoder_stride=self.encoder_stride	,    )

     def 					_a			(						self	,    _a	,    _a	,    _a  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	ViTModel(config=__lowerCamelCase  )
           model.to(__lowerCamelCase  )
           model.eval()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model(__lowerCamelCase  )
           self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)  )

     def 					_a			(						self	,    _a	,    _a	,    _a  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	ViTForMaskedImageModeling(config=__lowerCamelCase  )
           model.to(__lowerCamelCase  )
           model.eval()
           SCREAMING_SNAKE_CASE__   :      int      		=	model(__lowerCamelCase  )
           self.parent.assertEqual(
               result.reconstruction.shape	,    (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

           # test greyscale images
           SCREAMING_SNAKE_CASE__   :      Dict      		=	1
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ViTForMaskedImageModeling(__lowerCamelCase  )
           model.to(__lowerCamelCase  )
           model.eval()

           SCREAMING_SNAKE_CASE__   :      str      		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
           SCREAMING_SNAKE_CASE__   :      int      		=	model(__lowerCamelCase  )
           self.parent.assertEqual(result.reconstruction.shape	,    (self.batch_size, 1, self.image_size, self.image_size)  )

     def 					_a			(						self	,    _a	,    _a	,    _a  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      Dict      		=	ViTForImageClassification(__lowerCamelCase  )
           model.to(__lowerCamelCase  )
           model.eval()
           SCREAMING_SNAKE_CASE__   :      str      		=	model(__lowerCamelCase	,    labels=__lowerCamelCase  )
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)  )

           # test greyscale images
           SCREAMING_SNAKE_CASE__   :      str      		=	1
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	ViTForImageClassification(__lowerCamelCase  )
           model.to(__lowerCamelCase  )
           model.eval()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	model(__lowerCamelCase  )
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)  )





     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.prepare_config_and_inputs()
           (
               SCREAMING_SNAKE_CASE__	
           )   :      Tuple      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      Any      		=	{'''pixel_values''': pixel_values}
           return config, inputs_dict





@require_torch
class      __a							(lowerCamelCase__   ,	lowerCamelCase__   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE						:List[Any]       = (
         {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     _SCREAMING_SNAKE_CASE						:List[Any]       = True

     _SCREAMING_SNAKE_CASE						:str       = False
     _SCREAMING_SNAKE_CASE						:Any       = False
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = False

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	ViTModelTester(self  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	ConfigTester(self	,    config_class=__lowerCamelCase	,    has_text_modality=__lowerCamelCase	,    hidden_size=37  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           self.config_tester.run_common_tests()

     @unittest.skip(reason="""ViT does not use inputs_embeds"""  )
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           pass

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model_class(__lowerCamelCase  )
                 self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)  )
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__lowerCamelCase	,    nn.Linear  )  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model_class(__lowerCamelCase  )
                 SCREAMING_SNAKE_CASE__   :      str      		=	inspect.signature(model.forward  )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	[*signature.parameters.keys()]

                 SCREAMING_SNAKE_CASE__   :      Any      		=	['''pixel_values''']
                 self.assertListEqual(arg_names[:1]	,    __lowerCamelCase  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase  )





     @slow
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ViTModel.from_pretrained(__lowerCamelCase  )
                 self.assertIsNotNone(__lowerCamelCase  )





def  _lowercase					(    )       ->				str:
      SCREAMING_SNAKE_CASE__   :      int      		=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
      return image



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






     '''simple docstring'''

     @cached_property
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""  ) if is_vision_available() else None

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""  ).to(__lowerCamelCase  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.default_image_processor
           SCREAMING_SNAKE_CASE__   :      Any      		=	prepare_img()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	image_processor(images=__lowerCamelCase	,    return_tensors="""pt"""  ).to(__lowerCamelCase  )

           # forward pass
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(**__lowerCamelCase  )

           # verify the logits
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.Size((1, 1_000)  )
           self.assertEqual(outputs.logits.shape	,    __lowerCamelCase  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.tensor([-0.2_744, 0.8_215, -0.0_836]  ).to(__lowerCamelCase  )

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

     @slow
     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ViTModel.from_pretrained("""facebook/dino-vits8"""  ).to(__lowerCamelCase  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""	,    size=480  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prepare_img()
           SCREAMING_SNAKE_CASE__   :      str      		=	image_processor(images=__lowerCamelCase	,    return_tensors="""pt"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	inputs.pixel_values.to(__lowerCamelCase  )

           # forward pass
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(__lowerCamelCase	,    interpolate_pos_encoding=__lowerCamelCase  )

           # verify the logits
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.Size((1, 3_601, 384)  )
           self.assertEqual(outputs.last_hidden_state.shape	,    __lowerCamelCase  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.tensor(
               [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]  ).to(__lowerCamelCase  )

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





     @slow
     @require_accelerate
     @require_torch_gpu
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ViTModel.from_pretrained("""facebook/dino-vits8"""	,    torch_dtype=torch.floataa	,    device_map="""auto"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.default_image_processor

           SCREAMING_SNAKE_CASE__   :      str      		=	prepare_img()
           SCREAMING_SNAKE_CASE__   :      Any      		=	image_processor(images=__lowerCamelCase	,    return_tensors="""pt"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	inputs.pixel_values.to(__lowerCamelCase  )

           # forward pass to make sure inference works in fp16
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(__lowerCamelCase  )







"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DDPMScheduler,)

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]	,    [0.002, 0.02, 0.2, 2]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

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


           """simple docstring"""


           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a  )

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


           """simple docstring"""


           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00_979  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      str      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 258.9_606  ) < 1E-2
           assert abs(result_mean.item() - 0.3_372  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 202.0_296  ) < 1E-2
           assert abs(result_mean.item() - 0.2_631  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler.timesteps

           for i, timestep in enumerate(_a  ):
                 if i == len(_a  ) - 1:
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	-1
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	timesteps[i + 1]

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.previous_timestep(_a  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prev_t.item()

                 self.assertEqual(_a	,    _a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[100, 87, 50, 51, 0]

           with self.assertRaises(_a	,    msg="""`custom_timesteps` must be in descending order."""  ):
                 scheduler.set_timesteps(timesteps=_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[100, 87, 50, 1, 0]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(_a  )

           with self.assertRaises(_a	,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""  ):
                 scheduler.set_timesteps(num_inference_steps=_a	,    timesteps=_a  )





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               _a	,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""	,    ):
                 scheduler.set_timesteps(timesteps=_a  )




