





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
     from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.config_class(**self.inputs_dict		)
              self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "hidden_sizes"		)		)
              self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "num_encoder_blocks"		)		)



class 				_UpperCamelCase       :

       def __init__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[str]=13       , _SCREAMING_SNAKE_CASE:			List[Any]=64       , _SCREAMING_SNAKE_CASE:			Any=3       , _SCREAMING_SNAKE_CASE:			Optional[Any]=4       , _SCREAMING_SNAKE_CASE:			Dict=[2, 2, 2, 2]       , _SCREAMING_SNAKE_CASE:			Optional[int]=[8, 4, 2, 1]       , _SCREAMING_SNAKE_CASE:			Optional[Any]=[16, 32, 64, 128]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=[1, 4, 8, 16]       , _SCREAMING_SNAKE_CASE:			int=False       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Dict="gelu"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0.1       , _SCREAMING_SNAKE_CASE:			str=0.02       , _SCREAMING_SNAKE_CASE:			List[Any]=3       , _SCREAMING_SNAKE_CASE:			List[str]=None       , )							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	parent
              UpperCamelCase_ 							=	batch_size
              UpperCamelCase_ 							=	image_size
              UpperCamelCase_ 							=	num_channels
              UpperCamelCase_ 							=	num_encoder_blocks
              UpperCamelCase_ 							=	sr_ratios
              UpperCamelCase_ 							=	depths
              UpperCamelCase_ 							=	hidden_sizes
              UpperCamelCase_ 							=	downsampling_rates
              UpperCamelCase_ 							=	is_training
              UpperCamelCase_ 							=	use_labels
              UpperCamelCase_ 							=	hidden_act
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	num_labels
              UpperCamelCase_ 							=	scope

       def lowercase			(		self:			Dict		)							->			Dict:




              """simple docstring"""


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

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

              UpperCamelCase_ 							=	PoolFormerConfig(
                  image_size=self.image_size       , num_channels=self.num_channels       , num_encoder_blocks=self.num_encoder_blocks       , depths=self.depths       , hidden_sizes=self.hidden_sizes       , hidden_act=self.hidden_act       , hidden_dropout_prob=self.hidden_dropout_prob       , initializer_range=self.initializer_range       , )

              return config, pixel_values, labels

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




              """simple docstring"""


              UpperCamelCase_ 							=	PoolFormerModel(config=_SCREAMING_SNAKE_CASE		)
              model.to(_SCREAMING_SNAKE_CASE		)
              model.eval()
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	UpperCamelCase_ 							=	self.image_size // 32.0
              self.parent.assertEqual(
                  result.last_hidden_state.shape       , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)



       def lowercase			(		self:			str		)							->			int:




              """simple docstring"""


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



@require_torch
class 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       List[str]       = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
       _UpperCamelCase					:       Optional[Any]       = (
           {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       _UpperCamelCase					:       int       = False
       _UpperCamelCase					:       int       = False
       _UpperCamelCase					:       Optional[Any]       = False
       _UpperCamelCase					:       Union[str, Any]       = False
       _UpperCamelCase					:       int       = False

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




              """simple docstring"""


              UpperCamelCase_ 							=	PoolFormerModelTester(self		)
              UpperCamelCase_ 							=	PoolFormerConfigTester(self       , config_class=_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              self.config_tester.run_common_tests()

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

       @unittest.skip("PoolFormer does not use inputs_embeds"		)
       def lowercase			(		self:			Optional[int]		)							->			List[Any]:




              """simple docstring"""


              pass

       @unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"		)
       def lowercase			(		self:			Optional[Any]		)							->			Any:




              """simple docstring"""


              pass

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




              """simple docstring"""


              def check_hidden_states_output(_SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			str		):
                     UpperCamelCase_ 							=	model_class(_SCREAMING_SNAKE_CASE		)
                     model.to(_SCREAMING_SNAKE_CASE		)
                     model.eval()

                     with torch.no_grad():
                            UpperCamelCase_ 							=	model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)		)

                     UpperCamelCase_ 							=	outputs.hidden_states

                     UpperCamelCase_ 							=	self.model_tester.num_encoder_blocks
                     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE		)

                     # verify the first hidden states (first block)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-3:]		)       , [
                             self.model_tester.hidden_sizes[0],
                             self.model_tester.image_size // 4,
                             self.model_tester.image_size // 4,
                         ]       , )

              UpperCamelCase_							,			UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase_ 							=	True
                     check_hidden_states_output(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

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

                     check_hidden_states_output(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              if not self.model_tester.is_training:
                     return

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

              for model_class in self.all_model_classes:
                     if model_class in get_values(_SCREAMING_SNAKE_CASE		):
                            continue
                     UpperCamelCase_ 							=	model_class(_SCREAMING_SNAKE_CASE		)
                     model.to(_SCREAMING_SNAKE_CASE		)
                     model.train()
                     UpperCamelCase_ 							=	self._prepare_for_class(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , return_labels=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	model(**_SCREAMING_SNAKE_CASE		).loss
                     loss.backward()

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




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs_for_common()

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

                     UpperCamelCase_ 							=	["pixel_values"]
                     self.assertListEqual(arg_names[:1]       , _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase			(		self:			List[str]		)							->			Dict:




              """simple docstring"""


              for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase_ 							=	PoolFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




def 			lowerCAmelCase_			(				)  ->		Optional[Any]:
       UpperCamelCase_ 							=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       return image







@require_torch
class 				_UpperCamelCase       (						unittest.TestCase  ):

       @slow
       def lowercase			(		self:			str		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	PoolFormerImageProcessor()
              UpperCamelCase_ 							=	PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12"		).to(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	image_processor(images=prepare_img()       , return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase_ 							=	model(**_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_ 							=	torch.tensor([-0.61_13, 0.16_85, -0.04_92]		).to(_SCREAMING_SNAKE_CASE		)
              self.assertTrue(torch.allclose(outputs.logits[0, :3]       , _SCREAMING_SNAKE_CASE       , atol=1e-4		)		)



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			(DPMSolverMultistepScheduler,)
						__a     :    Dict												=			(("""num_inference_steps""", 2_5),)


						def    A__       (  self  , **A	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    {
									    'num_train_timesteps': 10_00,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'	),
									    'variance_type': None,
									}

									config.update(**A	)
									return config


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:int 		=    self.dummy_sample
									UpperCAmelCase__		:Optional[int] 		=    0.1 * sample
									UpperCAmelCase__		:Any 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:List[Any] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)
												# copy over dummy past residuals
												UpperCAmelCase__		:Optional[Any] 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:List[Any] 		=    scheduler_class.from_pretrained(A	)
															new_scheduler.set_timesteps(A	)
															# copy over dummy past residuals
															UpperCAmelCase__		:Union[str, Any] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    sample, sample
												for t in range(A  , time_step + scheduler.config.solver_order + 1	):
															UpperCAmelCase__		:Dict 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
															UpperCAmelCase__		:Optional[Any] 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->List[Any]:
									pass


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:str 		=    self.dummy_sample
									UpperCAmelCase__		:Union[str, Any] 		=    0.1 * sample
									UpperCAmelCase__		:Union[str, Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:List[Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:List[str] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)

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

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

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

												UpperCAmelCase__		:Any 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:Dict 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self  , A=None  , **A	)  ->List[str]:
									if scheduler is None:
												UpperCAmelCase__		:Optional[Any] 		=    self.scheduler_classes[0]
												UpperCAmelCase__		:Optional[Any] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler_class(**A	)

									UpperCAmelCase__		:List[Any] 		=    10
									UpperCAmelCase__		:int 		=    self.dummy_model()
									UpperCAmelCase__		:int 		=    self.dummy_sample_deter
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:Union[str, Any] 		=    model(A  , A	)
												UpperCAmelCase__		:str 		=    scheduler.step(A  , A  , A	).prev_sample

									return sample


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    dict(self.forward_default_kwargs	)

									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('num_inference_steps'  , A	)

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:Optional[Any] 		=    scheduler_class(**A	)

												UpperCAmelCase__		:Dict 		=    self.dummy_sample
												UpperCAmelCase__		:Dict 		=    0.1 * sample

												if num_inference_steps is not None and hasattr(A  , 'set_timesteps'	):
															scheduler.set_timesteps(A	)
												elif num_inference_steps is not None and not hasattr(A  , 'set_timesteps'	):
															UpperCAmelCase__		:List[Any] 		=    num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												UpperCAmelCase__		:List[Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]
												UpperCAmelCase__		:Any 		=    dummy_past_residuals[: scheduler.config.solver_order]

												UpperCAmelCase__		:str 		=    scheduler.timesteps[5]
												UpperCAmelCase__		:str 		=    scheduler.timesteps[6]

												UpperCAmelCase__		:List[str] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:List[Any] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->Optional[Any]:
									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
												self.check_over_configs(num_train_timesteps=A	)


						def    A__       (  self	)  ->str:
									self.check_over_configs(thresholding=A	)
									for order in [1, 2, 3]:
												for solver_type in ["midpoint", "heun"]:
															for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=A  , prediction_type=A  , sample_max_value=A  , algorithm_type='dpmsolver++'  , solver_order=A  , solver_type=A  , )


						def    A__       (  self	)  ->Optional[int]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=A	)


						def    A__       (  self	)  ->List[str]:
									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
															for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																								if order == 3:
																											continue
																					else:
																								self.check_over_configs(
																								    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					UpperCAmelCase__		:Optional[int] 		=    self.full_loop(
																					    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					assert not torch.isnan(A	).any(), "Samples have nan numbers"


						def    A__       (  self	)  ->List[Any]:
									self.check_over_configs(lower_order_final=A	)
									self.check_over_configs(lower_order_final=A	)


						def    A__       (  self	)  ->Optional[Any]:
									self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
									self.check_over_configs(lambda_min_clipped=-5.1	)


						def    A__       (  self	)  ->Union[str, Any]:
									self.check_over_configs(variance_type=A	)
									self.check_over_configs(variance_type='learned_range'	)


						def    A__       (  self	)  ->List[str]:
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
												self.check_over_forward(num_inference_steps=A  , time_step=0	)


						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    self.full_loop()
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(thresholding=A  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5	)
									UpperCAmelCase__		:Any 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 1.1364	) < 1e-3


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.full_loop(prediction_type='v_prediction'	)
									UpperCAmelCase__		:int 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2251	) < 1e-3


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(prediction_type='v_prediction'  , use_karras_sigmas=A	)
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2096	) < 1e-3


						def    A__       (  self	)  ->List[Any]:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									UpperCAmelCase__		:Tuple 		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:List[Any] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3

									UpperCAmelCase__		:Dict 		=    DPMSolverSinglestepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    UniPCMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    DEISMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    DPMSolverMultistepScheduler.from_config(scheduler.config	)

									UpperCAmelCase__		:Dict 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:Dict 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    self.scheduler_classes[0]
									UpperCAmelCase__		:List[str] 		=    self.get_scheduler_config(thresholding=A  , dynamic_thresholding_ratio=0	)
									UpperCAmelCase__		:str 		=    scheduler_class(**A	)

									UpperCAmelCase__		:int 		=    10
									UpperCAmelCase__		:Optional[int] 		=    self.dummy_model()
									UpperCAmelCase__		:List[str] 		=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:str 		=    model(A  , A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler.step(A  , A  , A	).prev_sample

									assert sample.dtype == torch.floataa


						def    A__       (  self  , **A	)  ->Any:
									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:int 		=    scheduler_class(**A	)

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





