






import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
				import torch

				from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=False    ,		_UpperCamelCase=True    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=5    ,		_UpperCamelCase=4    ,		_UpperCamelCase=3_7    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=3    ,		_UpperCamelCase=4    ,		_UpperCamelCase=None    ,		)     ->							Dict:
							UpperCAmelCase_   :				Tuple						=						parent
							UpperCAmelCase_   :				Optional[Any]						=						batch_size
							UpperCAmelCase_   :				Optional[Any]						=						seq_length
							UpperCAmelCase_   :				Any						=						is_training
							UpperCAmelCase_   :				Any						=						use_input_mask
							UpperCAmelCase_   :				Dict						=						use_token_type_ids
							UpperCAmelCase_   :				Dict						=						use_labels
							UpperCAmelCase_   :				Tuple						=						vocab_size
							UpperCAmelCase_   :				Union[str, Any]						=						hidden_size
							UpperCAmelCase_   :				Dict						=						num_hidden_layers
							UpperCAmelCase_   :				Dict						=						num_attention_heads
							UpperCAmelCase_   :				Optional[int]						=						intermediate_size
							UpperCAmelCase_   :				Optional[int]						=						hidden_act
							UpperCAmelCase_   :				Any						=						hidden_dropout_prob
							UpperCAmelCase_   :				Any						=						attention_probs_dropout_prob
							UpperCAmelCase_   :				int						=						max_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						type_vocab_size
							UpperCAmelCase_   :				int						=						type_sequence_label_size
							UpperCAmelCase_   :				List[Any]						=						initializer_range
							UpperCAmelCase_   :				Union[str, Any]						=						num_labels
							UpperCAmelCase_   :				Tuple						=						num_choices
							UpperCAmelCase_   :				str						=						scope



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				List[str]						=						ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size       )

							UpperCAmelCase_   :				Tuple						=						None
							if self.use_input_mask:
												UpperCAmelCase_   :				int						=						random_attention_mask([self.batch_size, self.seq_length]       )

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

							UpperCAmelCase_   :				Tuple						=						None
							UpperCAmelCase_   :				int						=						None
							UpperCAmelCase_   :				List[Any]						=						None
							if self.use_labels:
												UpperCAmelCase_   :				Tuple						=						ids_tensor([self.batch_size]    ,		self.type_sequence_label_size       )
												UpperCAmelCase_   :				Union[str, Any]						=						ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels       )
												UpperCAmelCase_   :				Dict						=						ids_tensor([self.batch_size]    ,		self.num_choices       )

							UpperCAmelCase_   :				Union[str, Any]						=						self.get_config()

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



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return OpenLlamaConfig(
							    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    ,		is_decoder=__lowerCamelCase    ,		initializer_range=self.initializer_range    ,		use_stable_embedding=__lowerCamelCase    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Optional[Any]						=						OpenLlamaModel(config=__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							UpperCAmelCase_   :				List[Any]						=						model(__lowerCamelCase    ,		attention_mask=__lowerCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						model(__lowerCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							Any:
							UpperCAmelCase_   :				Union[str, Any]						=						True
							UpperCAmelCase_   :				Optional[int]						=						OpenLlamaModel(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Any						=						model(
							    __lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		encoder_hidden_states=__lowerCamelCase    ,		encoder_attention_mask=__lowerCamelCase    ,		)
							UpperCAmelCase_   :				Any						=						model(
							    __lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		encoder_hidden_states=__lowerCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						model(__lowerCamelCase    ,		attention_mask=__lowerCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							Any:
							UpperCAmelCase_   :				Any						=						OpenLlamaForCausalLM(config=__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							UpperCAmelCase_   :				List[Any]						=						model(__lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		labels=__lowerCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				Optional[int]						=						True
							UpperCAmelCase_   :				Optional[Any]						=						True
							UpperCAmelCase_   :				Dict						=						OpenLlamaForCausalLM(config=__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							# first forward pass
							UpperCAmelCase_   :				Tuple						=						model(
							    __lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		encoder_hidden_states=__lowerCamelCase    ,		encoder_attention_mask=__lowerCamelCase    ,		use_cache=__lowerCamelCase    ,		)
							UpperCAmelCase_   :				List[Any]						=						outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							UpperCAmelCase_   :				List[str]						=						ids_tensor((self.batch_size, 3)    ,		config.vocab_size       )
							UpperCAmelCase_   :				List[Any]						=						ids_tensor((self.batch_size, 3)    ,		vocab_size=2       )

							# append to next input_ids and
							UpperCAmelCase_   :				Optional[Any]						=						torch.cat([input_ids, next_tokens]    ,		dim=-1       )
							UpperCAmelCase_   :				List[str]						=						torch.cat([input_mask, next_mask]    ,		dim=-1       )

							UpperCAmelCase_   :				Dict						=						model(
							    __lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		encoder_hidden_states=__lowerCamelCase    ,		encoder_attention_mask=__lowerCamelCase    ,		output_hidden_states=__lowerCamelCase    ,		)["""hidden_states"""][0]
							UpperCAmelCase_   :				Optional[int]						=						model(
							    __lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		encoder_hidden_states=__lowerCamelCase    ,		encoder_attention_mask=__lowerCamelCase    ,		past_key_values=__lowerCamelCase    ,		output_hidden_states=__lowerCamelCase    ,		)["""hidden_states"""][0]

							# select random slice
							UpperCAmelCase_   :				Optional[int]						=						ids_tensor((1,)    ,		output_from_past.shape[-1]       ).item()
							UpperCAmelCase_   :				List[Any]						=						output_from_no_past[:, -3:, random_slice_idx].detach()
							UpperCAmelCase_   :				Any						=						output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(__lowerCamelCase    ,		__lowerCamelCase    ,		atol=1E-3       )       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Optional[Any]						=						self.prepare_config_and_inputs()
							(
							    UpperCAmelCase_				
							)   :				Union[str, Any]						=						config_and_inputs
							UpperCAmelCase_   :				Any						=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict





@require_torch
class   lowerCamelCase				(_a			,  _a			,  _a			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Optional[int]							=  (
		    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
		)
		_snake_case   :   List[str]							=  (OpenLlamaForCausalLM,) if is_torch_available() else ()
		_snake_case   :   str							=  (
		    {
		        """feature-extraction""": OpenLlamaModel,
		        """text-classification""": OpenLlamaForSequenceClassification,
		        """text-generation""": OpenLlamaForCausalLM,
		        """zero-shot""": OpenLlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_snake_case   :   Optional[Any]							=  False
		_snake_case   :   Optional[int]							=  False



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Union[str, Any]						=						OpenLlamaModelTester(self       )
							UpperCAmelCase_   :				Union[str, Any]						=						ConfigTester(self    ,		config_class=__lowerCamelCase    ,		hidden_size=3_7       )



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



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				int						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				List[str]						=						self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
												UpperCAmelCase_   :				Any						=						type
												self.model_tester.create_and_check_model(*__lowerCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_   :				Dict						=						3
							UpperCAmelCase_   :				str						=						input_dict["""input_ids"""]
							UpperCAmelCase_   :				Dict						=						input_ids.ne(1       ).to(__lowerCamelCase       )
							UpperCAmelCase_   :				Tuple						=						ids_tensor([self.model_tester.batch_size]    ,		self.model_tester.type_sequence_label_size       )
							UpperCAmelCase_   :				int						=						OpenLlamaForSequenceClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							UpperCAmelCase_   :				int						=						model(__lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		labels=__lowerCamelCase       )
							self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels)       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Union[str, Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_   :				Dict						=						3
							UpperCAmelCase_   :				int						=						"""single_label_classification"""
							UpperCAmelCase_   :				List[Any]						=						input_dict["""input_ids"""]
							UpperCAmelCase_   :				Optional[Any]						=						input_ids.ne(1       ).to(__lowerCamelCase       )
							UpperCAmelCase_   :				List[str]						=						ids_tensor([self.model_tester.batch_size]    ,		self.model_tester.type_sequence_label_size       )
							UpperCAmelCase_   :				Dict						=						OpenLlamaForSequenceClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Any						=						model(__lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		labels=__lowerCamelCase       )
							self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels)       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Any						=						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_   :				int						=						3
							UpperCAmelCase_   :				Union[str, Any]						=						"""multi_label_classification"""
							UpperCAmelCase_   :				Optional[Any]						=						input_dict["""input_ids"""]
							UpperCAmelCase_   :				List[Any]						=						input_ids.ne(1       ).to(__lowerCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]    ,		self.model_tester.type_sequence_label_size       ).to(torch.float       )
							UpperCAmelCase_   :				int						=						OpenLlamaForSequenceClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							UpperCAmelCase_   :				List[Any]						=						model(__lowerCamelCase    ,		attention_mask=__lowerCamelCase    ,		labels=__lowerCamelCase       )
							self.assertEqual(result.logits.shape    ,		(self.model_tester.batch_size, self.model_tester.num_labels)       )



		@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'       )
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass



		@parameterized.expand([('linear',), ('dynamic',)]       )
		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							int:
							UpperCAmelCase_   :				List[str]						=						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_   :				List[Any]						=						ids_tensor([1, 1_0]    ,		config.vocab_size       )
							UpperCAmelCase_   :				Union[str, Any]						=						ids_tensor([1, int(config.max_position_embeddings * 1.5       )]    ,		config.vocab_size       )

							set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
							UpperCAmelCase_   :				Optional[Any]						=						OpenLlamaModel(__lowerCamelCase       )
							original_model.to(__lowerCamelCase       )
							original_model.eval()
							UpperCAmelCase_   :				Tuple						=						original_model(__lowerCamelCase       ).last_hidden_state
							UpperCAmelCase_   :				Optional[Any]						=						original_model(__lowerCamelCase       ).last_hidden_state

							set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
							UpperCAmelCase_   :				Optional[int]						=						{"""type""": scaling_type, """factor""": 10.0}
							UpperCAmelCase_   :				Tuple						=						OpenLlamaModel(__lowerCamelCase       )
							scaled_model.to(__lowerCamelCase       )
							scaled_model.eval()
							UpperCAmelCase_   :				Optional[Any]						=						scaled_model(__lowerCamelCase       ).last_hidden_state
							UpperCAmelCase_   :				Any						=						scaled_model(__lowerCamelCase       ).last_hidden_state

							# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
							# maximum sequence length, so the outputs for the short input should match.
							if scaling_type == "dynamic":
												self.assertTrue(torch.allclose(__lowerCamelCase    ,		__lowerCamelCase    ,		atol=1E-5       )       )
							else:
												self.assertFalse(torch.allclose(__lowerCamelCase    ,		__lowerCamelCase    ,		atol=1E-5       )       )

							# The output should be different for long inputs
							self.assertFalse(torch.allclose(__lowerCamelCase    ,		__lowerCamelCase    ,		atol=1E-5       )       )














import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Any							=  (DDPMParallelScheduler,)



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						{
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_UpperCamelCase       )
							return config



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,		[0.0_02, 0.02, 0.2, 2]       ):
												self.check_over_configs(beta_start=_UpperCamelCase    ,		beta_end=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							self.check_over_configs(thresholding=_UpperCamelCase       )
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_UpperCamelCase    ,		prediction_type=_UpperCamelCase    ,		sample_max_value=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				int						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1E-5



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						self.dummy_model()
							UpperCAmelCase_   :				List[Any]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						self.dummy_sample_deter + 0.1
							UpperCAmelCase_   :				str						=						self.dummy_sample_deter - 0.1

							UpperCAmelCase_   :				Tuple						=						samplea.shape[0]
							UpperCAmelCase_   :				Tuple						=						torch.stack([samplea, samplea, samplea]    ,		dim=0       )
							UpperCAmelCase_   :				str						=						torch.arange(_UpperCamelCase       )[0:3, None].repeat(1    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						model(samples.flatten(0    ,		1       )    ,		timesteps.flatten(0    ,		1       )       )
							UpperCAmelCase_   :				Dict						=						scheduler.batch_step_no_noise(_UpperCamelCase    ,		timesteps.flatten(0    ,		1       )    ,		samples.flatten(0    ,		1       )       )

							UpperCAmelCase_   :				List[Any]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 11_53.18_33       ) < 1E-2
							assert abs(result_mean.item() - 0.50_05       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Any						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						self.dummy_model()
							UpperCAmelCase_   :				Optional[int]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				str						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				str						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Optional[int]						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_58.96_06       ) < 1E-2
							assert abs(result_mean.item() - 0.33_72       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[int]						=						self.get_scheduler_config(prediction_type='v_prediction'       )
							UpperCAmelCase_   :				Tuple						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )

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

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

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				Any						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				List[str]						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_02.02_96       ) < 1E-2
							assert abs(result_mean.item() - 0.26_31       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				str						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 1, 0]

							scheduler.set_timesteps(timesteps=_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						scheduler.timesteps

							for i, timestep in enumerate(_UpperCamelCase       ):
												if i == len(_UpperCamelCase       ) - 1:
																	UpperCAmelCase_   :				List[str]						=						-1
												else:
																	UpperCAmelCase_   :				int						=						timesteps[i + 1]

												UpperCAmelCase_   :				int						=						scheduler.previous_timestep(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						prev_t.item()

												self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Tuple						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 5_1, 0]

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



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[str]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				List[str]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						[1_0_0, 8_7, 5_0, 1, 0]
							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

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



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Optional[Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						[scheduler.config.num_train_timesteps]

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






