



import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, 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 (
	    DebertaVaForMaskedLM,
	    DebertaVaForMultipleChoice,
	    DebertaVaForQuestionAnswering,
	    DebertaVaForSequenceClassification,
	    DebertaVaForTokenClassification,
	    DebertaVaModel,
	)
	from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST

class 	lowerCamelCase_    ( lowercase_						):






						'''simple docstring'''







						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]=13	,	__lowerCamelCase      :			List[Any]=7	,	__lowerCamelCase      :			int=True	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			int=99	,	__lowerCamelCase      :			int=32	,	__lowerCamelCase      :			Tuple=5	,	__lowerCamelCase      :			Any=4	,	__lowerCamelCase      :			Union[str, Any]=37	,	__lowerCamelCase      :			int="gelu"	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Dict=0.1	,	__lowerCamelCase      :			Tuple=5_12	,	__lowerCamelCase      :			Tuple=16	,	__lowerCamelCase      :			List[Any]=2	,	__lowerCamelCase      :			List[str]=0.02	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			Tuple=True	,	__lowerCamelCase      :			List[str]="None"	,	__lowerCamelCase      :			Any=3	,	__lowerCamelCase      :			List[Any]=4	,	__lowerCamelCase      :			Optional[Any]=None	,	)      ->				List[str]:
									A							:					List[str]              =						parent
									A							:					Tuple              =						batch_size
									A							:					Any              =						seq_length
									A							:					Union[str, Any]              =						is_training
									A							:					Tuple              =						use_input_mask
									A							:					str              =						use_token_type_ids
									A							:					Union[str, Any]              =						use_labels
									A							:					Any              =						vocab_size
									A							:					List[str]              =						hidden_size
									A							:					List[str]              =						num_hidden_layers
									A							:					Optional[int]              =						num_attention_heads
									A							:					List[str]              =						intermediate_size
									A							:					Tuple              =						hidden_act
									A							:					Dict              =						hidden_dropout_prob
									A							:					Dict              =						attention_probs_dropout_prob
									A							:					Union[str, Any]              =						max_position_embeddings
									A							:					Optional[int]              =						type_vocab_size
									A							:					Optional[int]              =						type_sequence_label_size
									A							:					Tuple              =						initializer_range
									A							:					Any              =						num_labels
									A							:					List[Any]              =						num_choices
									A							:					Tuple              =						relative_attention
									A							:					Optional[Any]              =						position_biased_input
									A							:					Any              =						pos_att_type
									A							:					Optional[Any]              =						scope







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				int:
									A							:					Tuple              =						ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size )

									A							:					int              =						None
									if self.use_input_mask:
												A							:					Any              =						ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2 )

									A							:					List[str]              =						None
									if self.use_token_type_ids:
												A							:					Dict              =						ids_tensor([self.batch_size, self.seq_length]	,	self.type_vocab_size )

									A							:					Any              =						None
									A							:					Tuple              =						None
									A							:					int              =						None
									if self.use_labels:
												A							:					Tuple              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )
												A							:					Optional[int]              =						ids_tensor([self.batch_size, self.seq_length]	,	self.num_labels )
												A							:					List[Any]              =						ids_tensor([self.batch_size]	,	self.num_choices )

									A							:					str              =						self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Tuple:
									return DebertaVaConfig(
									    vocab_size=self.vocab_size	,	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	,	max_position_embeddings=self.max_position_embeddings	,	type_vocab_size=self.type_vocab_size	,	initializer_range=self.initializer_range	,	relative_attention=self.relative_attention	,	position_biased_input=self.position_biased_input	,	pos_att_type=self.pos_att_type	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			List[Any] )      ->				Any:
									self.parent.assertListEqual(list(result.loss.size() )	,	[] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[str] )      ->				Dict:
									A							:					Any              =						DebertaVaModel(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Optional[Any]              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase )[0]
									A							:					int              =						model(__lowerCamelCase	,	token_type_ids=__lowerCamelCase )[0]
									A							:					Any              =						model(__lowerCamelCase )[0]

									self.parent.assertListEqual(list(sequence_output.size() )	,	[self.batch_size, self.seq_length, self.hidden_size] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[Any] )      ->				Union[str, Any]:
									A							:					int              =						DebertaVaForMaskedLM(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					str              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase )

									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			str )      ->				List[str]:
									A							:					str              =						self.num_labels
									A							:					int              =						DebertaVaForSequenceClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					int              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertListEqual(list(result.logits.size() )	,	[self.batch_size, self.num_labels] )
									self.check_loss_output(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			str )      ->				Any:
									A							:					List[Any]              =						self.num_labels
									A							:					Optional[Any]              =						DebertaVaForTokenClassification(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					int              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Tuple:
									A							:					Optional[Any]              =						DebertaVaForQuestionAnswering(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Optional[int]              =						model(
									    __lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	start_positions=__lowerCamelCase	,	end_positions=__lowerCamelCase	,	)
									self.parent.assertEqual(result.start_logits.shape	,	(self.batch_size, self.seq_length) )
									self.parent.assertEqual(result.end_logits.shape	,	(self.batch_size, self.seq_length) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Union[str, Any] )      ->				int:
									A							:					Optional[int]              =						DebertaVaForMultipleChoice(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Dict              =						input_ids.unsqueeze(1 ).expand(-1	,	self.num_choices	,	-1 ).contiguous()
									A							:					Any              =						token_type_ids.unsqueeze(1 ).expand(-1	,	self.num_choices	,	-1 ).contiguous()
									A							:					str              =						input_mask.unsqueeze(1 ).expand(-1	,	self.num_choices	,	-1 ).contiguous()
									A							:					Optional[Any]              =						model(
									    __lowerCamelCase	,	attention_mask=__lowerCamelCase	,	token_type_ids=__lowerCamelCase	,	labels=__lowerCamelCase	,	)
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_choices) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									A							:					List[Any]              =						self.prepare_config_and_inputs()
									(
									    (
									    A					
									)       , (
									    A					
									)       , (
									    A					
									)       , (
									    A					
									)       , (
									    A					
									)       , (
									    A					
									)       , (
									    A					
									)       , 					
									)							:					List[str]              =						config_and_inputs
									A							:					Optional[Any]              =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict







@require_torch
class 	lowerCamelCase_    ( lowercase_						,lowercase_						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (
						    (
						        DebertaVaModel,
						        DebertaVaForMaskedLM,
						        DebertaVaForSequenceClassification,
						        DebertaVaForTokenClassification,
						        DebertaVaForQuestionAnswering,
						        DebertaVaForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						a__   =  (
						    {
						        '''feature-extraction''': DebertaVaModel,
						        '''fill-mask''': DebertaVaForMaskedLM,
						        '''question-answering''': DebertaVaForQuestionAnswering,
						        '''text-classification''': DebertaVaForSequenceClassification,
						        '''token-classification''': DebertaVaForTokenClassification,
						        '''zero-shot''': DebertaVaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						a__   =  True
						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				List[str]:
									A							:					List[Any]              =						DebertaVaModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[Any]:
									self.config_tester.run_common_tests()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					Any              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				str:
									A							:					str              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					str              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_masked_lm(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[Any]:
									A							:					Dict              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_question_answering(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Union[str, Any]:
									A							:					Union[str, Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_token_classification(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[int]:
									A							:					Dict              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_deberta_for_multiple_choice(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				int:
									for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Dict              =						DebertaVaModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@unittest.skip(reason="Model not available yet" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									pass







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge" )

									A							:					List[Any]              =						torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
									A							:					Optional[int]              =						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
									with torch.no_grad():
												A							:					Union[str, Any]              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase )[0]
									# compare the actual values for a slice.
									A							:					Dict              =						torch.tensor(
									    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,	__lowerCamelCase	,	atol=1e-4 )	,	F"""{output[:, 1:4, 1:4]}""" )




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (PNDMScheduler,)
						a__   =  (("num_inference_steps", 50),)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			str )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]=0	,	**__lowerCamelCase      :			Any )      ->				Tuple:
									A							:					Dict              =						dict(self.forward_default_kwargs )
									A							:					Dict              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.dummy_sample
									A							:					List[Any]              =						0.1 * sample
									A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Any              =						self.get_scheduler_config(**__lowerCamelCase )
												A							:					int              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )
												# copy over dummy past residuals
												A							:					Tuple              =						dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					Dict              =						scheduler_class.from_pretrained(__lowerCamelCase )
															new_scheduler.set_timesteps(__lowerCamelCase )
															# copy over dummy past residuals
															A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					int              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[str]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]=0	,	**__lowerCamelCase      :			Tuple )      ->				str:
									A							:					List[str]              =						dict(self.forward_default_kwargs )
									A							:					Optional[int]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					List[str]              =						self.dummy_sample
									A							:					Any              =						0.1 * sample
									A							:					Any              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.get_scheduler_config()
												A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )

												# copy over dummy past residuals (must be after setting timesteps)
												A							:					Optional[int]              =						dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					str              =						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							:					Optional[Any]              =						dummy_past_residuals[:]

												A							:					Union[str, Any]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					Dict              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Union[str, Any]              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					List[Any]              =						self.get_scheduler_config(**__lowerCamelCase )
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					List[str]              =						10
									A							:					Union[str, Any]              =						self.dummy_model()
									A							:					int              =						self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCamelCase )

									for i, t in enumerate(scheduler.prk_timesteps ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Optional[int]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps ):
												A							:					Tuple              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Any:
									A							:					Union[str, Any]              =						dict(self.forward_default_kwargs )

									A							:					Union[str, Any]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )

									for scheduler_class in self.scheduler_classes:
												A							:					Dict              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												A							:					List[Any]              =						self.dummy_sample
												A							:					List[Any]              =						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							:					List[str]              =						num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						scheduler.step_prk(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Any              =						scheduler.step_plms(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						scheduler.step_plms(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									for timesteps in [1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__lowerCamelCase )

									A							:					Dict              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config(steps_offset=1 )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
									scheduler.set_timesteps(10 )
									assert torch.equal(
									    scheduler.timesteps	,	torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									for beta_start, beta_end in zip([0.0001, 0.001]	,	[0.002, 0.02] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									for t in [1, 5, 10]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									for t, num_inference_steps in zip([1, 5, 10]	,	[10, 50, 1_00] ):
												self.check_over_forward(num_inference_steps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									A							:					str              =						27

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												A							:					List[Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.set_timesteps(__lowerCamelCase )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2] ):
															A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									with self.assertRaises(__lowerCamelCase ):
												A							:					Union[str, Any]              =						self.scheduler_classes[0]
												A							:					Union[str, Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.step_plms(self.dummy_sample	,	1	,	self.dummy_sample ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									A							:					Optional[Any]              =						self.full_loop()
									A							:					Tuple              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 198.1318 ) < 1e-2
									assert abs(result_mean.item() - 0.2580 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Any              =						self.full_loop(prediction_type="v_prediction" )
									A							:					Union[str, Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					List[str]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 67.3986 ) < 1e-2
									assert abs(result_mean.item() - 0.0878 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				List[str]:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					Dict              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Any              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 230.0399 ) < 1e-2
									assert abs(result_mean.item() - 0.2995 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					List[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 186.9482 ) < 1e-2
									assert abs(result_mean.item() - 0.2434 ) < 1e-3