
import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CpmAntConfig,
							    CpmAntForCausalLM,
							    CpmAntModel,
							    CpmAntTokenizer,
							)







@require_torch
class 							lowercase						:




							def __init__(							self  ,A__  ,A__=2  ,A__=8  ,A__=True  ,A__=False  ,A__=False  ,A__=False  ,A__=False  ,A__=9_9  ,A__=3_2  ,A__=3  ,A__=4  ,A__=3_7  ,A__=3_2  ,A__=1_2_8  ,A__=8  ,A__=8  ,A__=8  ,A__=1.0  ,A__=True  ,):
												lowercase		      =		parent
												lowercase		      =		batch_size
												lowercase		      =		seq_length
												lowercase		      =		is_training
												lowercase		      =		use_token_type_ids
												lowercase		      =		use_input_mask
												lowercase		      =		use_labels
												lowercase		      =		use_mc_token_ids
												lowercase		      =		vocab_size
												lowercase		      =		hidden_size
												lowercase		      =		num_hidden_layers
												lowercase		      =		num_attention_heads
												lowercase		      =		intermediate_size
												lowercase		      =		num_buckets
												lowercase		      =		max_distance
												lowercase		      =		prompt_length
												lowercase		      =		prompt_types
												lowercase		      =		segment_types
												lowercase		      =		init_std
												lowercase		      =		return_dict




							def 					A__ (							self):
												lowercase		      =		{}
												lowercase		      =		ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size).type(torch.intaa)
												lowercase		      =		False

												lowercase		      =		self.get_config()

												return (config, input_ids)




							def 					A__ (							self):
												return CpmAntConfig(
												    vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,dim_ff=self.intermediate_size  ,position_bias_num_buckets=self.num_buckets  ,position_bias_max_distance=self.max_distance  ,prompt_types=self.prompt_types  ,prompt_length=self.prompt_length  ,segment_types=self.segment_types  ,use_cache=_SCREAMING_SNAKE_CASE  ,init_std=self.init_std  ,return_dict=self.return_dict  ,)




							def 					A__ (							self  ,A__  ,A__  ,*A__):
												lowercase		      =		CpmAntModel(config=_SCREAMING_SNAKE_CASE)
												model.to(_SCREAMING_SNAKE_CASE)
												model.eval()

												lowercase		      =		model(**_SCREAMING_SNAKE_CASE).last_hidden_state

												self.parent.assertEqual(hidden_states.shape  ,(self.batch_size, self.seq_length, config.hidden_size))




							def 					A__ (							self  ,A__  ,A__  ,*A__):
												lowercase		      =		CpmAntForCausalLM(_SCREAMING_SNAKE_CASE)
												model.to(_SCREAMING_SNAKE_CASE)
												lowercase		      =		input_ids['''input_ids'''].to(_SCREAMING_SNAKE_CASE)
												model.eval()

												lowercase		      =		model(**_SCREAMING_SNAKE_CASE)
												self.parent.assertEqual(
												    model_output.logits.shape  ,(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)  ,)





							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.prepare_config_and_inputs()
												return config, inputs_dict







@require_torch
class 							lowercase						(   _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    unittest.TestCase						):
							lowercase_			:			int			   =(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
							lowercase_			:			Tuple			   =(
							    {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
							)

							lowercase_			:			Optional[Any]			   =False
							lowercase_			:			Union[str, Any]			   =False
							lowercase_			:			Any			   =False
							lowercase_			:			List[Any]			   =False
							lowercase_			:			Union[str, Any]			   =False




							def 					A__ (							self):
												lowercase		      =		CpmAntModelTester(self)
												lowercase		      =		ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE)




							def 					A__ (							self):
												self.config_tester.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.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()




							def 					A__ (							self):
												unittest.skip('''CPMAnt doesn\'t support input_embeds.''')(self.test_inputs_embeds)




							def 					A__ (							self):
												unittest.skip(
												    '''CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.''')(self.test_retain_grad_hidden_states_attentions)




							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_cpmant_model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)





							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)







@require_torch
class 							lowercase						(   unittest.TestCase						):




							@tooslow
							def 					A__ (							self):
												lowercase		      =		'''今天天气真好！'''
												lowercase		      =		'''openbmb/cpm-ant-10b'''
												lowercase		      =		CpmAntModel.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		CpmAntTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		tokenizer(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt''')
												lowercase		      =		model(**_SCREAMING_SNAKE_CASE).last_hidden_state

												lowercase		      =		torch.tensor(
												    [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -1_1.3_3_2_4], [-1.0107, -0.0576, -5.9577]]]  ,)
												self.assertTrue(torch.allclose(hidden_states[:, :3, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1E-2))







@require_torch
class 							lowercase						(   unittest.TestCase						):




							@tooslow
							def 					A__ (							self):
												lowercase		      =		'''今天天气真好！'''
												lowercase		      =		'''openbmb/cpm-ant-10b'''
												lowercase		      =		CpmAntForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		CpmAntTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		tokenizer(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt''')
												lowercase		      =		model(**_SCREAMING_SNAKE_CASE).logits

												lowercase		      =		torch.tensor(
												    [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]]  ,)
												self.assertTrue(torch.allclose(hidden_states[:, :3, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1E-2))




							@tooslow
							def 					A__ (							self):
												lowercase		      =		'''openbmb/cpm-ant-10b'''
												lowercase		      =		CpmAntForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		CpmAntTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		'''今天天气不错，'''
												lowercase		      =		'''今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'''
												lowercase		      =		tokenizer(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt''')
												lowercase		      =		model.generate(**_SCREAMING_SNAKE_CASE)
												lowercase		      =		tokenizer.batch_decode(_SCREAMING_SNAKE_CASE)
												self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)





							@tooslow
							def 					A__ (							self):
												lowercase		      =		'''openbmb/cpm-ant-10b'''
												lowercase		      =		CpmAntForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		CpmAntTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
												lowercase		      =		['''今天天气不错，''', '''新年快乐，万事如意！''']
												lowercase		      =		[
												    '''今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的''',
												    '''新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的''',
												]
												lowercase		      =		tokenizer(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''  ,padding=_SCREAMING_SNAKE_CASE)
												lowercase		      =		model.generate(**_SCREAMING_SNAKE_CASE)
												lowercase		      =		tokenizer.batch_decode(_SCREAMING_SNAKE_CASE)
												self.assertEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)








import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Optional[int]       =					(DPMSolverMultistepScheduler,)
							_lowerCamelCase				: Optional[Any]       =					(("""num_inference_steps""", 25),)





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """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(**_SCREAMING_SNAKE_CASE    )
								return config





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								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(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									# copy over dummy past residuals
									a_     			=   dummy_past_residuals[: scheduler.config.solver_order]

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

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

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





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								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(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

									# 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(_SCREAMING_SNAKE_CASE    )
										a_     			=   scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   new_scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE    ):
								if scheduler is None:
									a_     			=   self.scheduler_classes[0]
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

								return sample





							def   __magic_name__      (							self    ):
								a_     			=   dict(self.forward_default_kwargs    )

								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

									a_     			=   self.dummy_sample
									a_     			=   0.1 * sample

									if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE			,       """set_timesteps"""    ):
										scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE			,       """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(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





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





							def   __magic_name__      (							self    ):
								self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
								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=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       sample_max_value=_SCREAMING_SNAKE_CASE			,       algorithm_type="""dpmsolver++"""			,       solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       )





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





							def   __magic_name__      (							self    ):
								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=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												a_     			=   self.full_loop(
												    solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												assert not torch.isnan(_SCREAMING_SNAKE_CASE    ).any(), "Samples have nan numbers"





							def   __magic_name__      (							self    ):
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(lambda_min_clipped=-float("""inf"""    )    )
								self.check_over_configs(lambda_min_clipped=-5.1    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(variance_type="""learned_range"""    )





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





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop()
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0.8_7			,       sample_max_value=0.5    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 1.1_3_6_4    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_2_5_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""			,       use_karras_sigmas=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_0_9_6    ) < 1E-3





							def   __magic_name__      (							self    ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a_     			=   DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3

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

								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0    )
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

								assert sample.dtype == torch.floataa





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

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