"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 (
	    GPTNeoXForCausalLM,
	    GPTNeoXForQuestionAnswering,
	    GPTNeoXForSequenceClassification,
	    GPTNeoXForTokenClassification,
	    GPTNeoXModel,
	)






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=13      ,					_lowerCamelCase=7      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=99      ,					_lowerCamelCase=64      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=37      ,					_lowerCamelCase="gelu"      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=512      ,					_lowerCamelCase=16      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=3      ,					_lowerCamelCase=4      ,					_lowerCamelCase=None      ,					):
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =seq_length
										lowerCamelCase__   =is_training
										lowerCamelCase__   =use_input_mask
										lowerCamelCase__   =use_token_type_ids
										lowerCamelCase__   =use_labels
										lowerCamelCase__   =vocab_size
										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =num_hidden_layers
										lowerCamelCase__   =num_attention_heads
										lowerCamelCase__   =intermediate_size
										lowerCamelCase__   =hidden_act
										lowerCamelCase__   =hidden_dropout_prob
										lowerCamelCase__   =attention_probs_dropout_prob
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =type_vocab_size
										lowerCamelCase__   =type_sequence_label_size
										lowerCamelCase__   =initializer_range
										lowerCamelCase__   =num_labels
										lowerCamelCase__   =num_choices
										lowerCamelCase__   =scope
										lowerCamelCase__   =vocab_size - 1

				def 						_a      ( self ):
										lowerCamelCase__   =ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

										lowerCamelCase__   =None
										if self.use_input_mask:
																lowerCamelCase__   =random_attention_mask([self.batch_size, self.seq_length] )

										lowerCamelCase__   =None
										if self.use_labels:
																lowerCamelCase__   =ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels )

										lowerCamelCase__   =self.get_config()

										return config, input_ids, input_mask, token_labels

				def 						_a      ( self ):
										return GPTNeoXConfig(
										    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=SCREAMING_SNAKE_CASE_      ,					initializer_range=self.initializer_range      ,					pad_token_id=self.pad_token_id      ,					)

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self.prepare_config_and_inputs()

										lowerCamelCase__   =True

										return config, input_ids, input_mask, token_labels

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =GPTNeoXModel(config=SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =True
										lowerCamelCase__   =GPTNeoXModel(SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_      ,					labels=SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =self.num_labels
										lowerCamelCase__   =GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_ )
										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 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =self.num_labels
										lowerCamelCase__   =GPTNeoXForSequenceClassification(SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()
										lowerCamelCase__   =ids_tensor([self.batch_size]      ,					self.type_sequence_label_size )
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_      ,					labels=SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =self.num_labels
										lowerCamelCase__   =GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_      ,					labels=SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =True
										lowerCamelCase__   =GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE_ )
										model.to(SCREAMING_SNAKE_CASE_ )
										model.eval()

										# first forward pass
										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_      ,					use_cache=SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										lowerCamelCase__   =ids_tensor((self.batch_size, 3)      ,					config.vocab_size )
										lowerCamelCase__   =ids_tensor((self.batch_size, 3)      ,					vocab_size=2 )

										# append to next input_ids and
										lowerCamelCase__   =torch.cat([input_ids, next_tokens]      ,					dim=-1 )
										lowerCamelCase__   =torch.cat([input_mask, next_mask]      ,					dim=-1 )

										lowerCamelCase__   =model(SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_      ,					output_hidden_states=SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =output_from_no_past["hidden_states"][0]
										lowerCamelCase__   =model(
										    SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_      ,					past_key_values=SCREAMING_SNAKE_CASE_      ,					output_hidden_states=SCREAMING_SNAKE_CASE_      ,					)["hidden_states"][0]

										# select random slice
										lowerCamelCase__   =ids_tensor((1,)      ,					output_from_past.shape[-1] ).item()
										lowerCamelCase__   =output_from_no_past[:, -3:, random_slice_idx].detach()
										lowerCamelCase__   =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(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					atol=1E-3 ) )






				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =config_and_inputs
										lowerCamelCase__   ={"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_torch
class   __UpperCAmelCase		(      __lowercase , __lowercase , __lowercase , unittest.TestCase      ):
				A__ :  Optional[int]    				=			(
				    (
				        GPTNeoXModel,
				        GPTNeoXForCausalLM,
				        GPTNeoXForQuestionAnswering,
				        GPTNeoXForSequenceClassification,
				        GPTNeoXForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				A__ :  Optional[int]    				=			(GPTNeoXForCausalLM,) if is_torch_available() else ()
				A__ :  Tuple    				=			(
				    {
				        '''feature-extraction''': GPTNeoXModel,
				        '''question-answering''': GPTNeoXForQuestionAnswering,
				        '''text-classification''': GPTNeoXForSequenceClassification,
				        '''text-generation''': GPTNeoXForCausalLM,
				        '''token-classification''': GPTNeoXForTokenClassification,
				        '''zero-shot''': GPTNeoXForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				A__ :  Tuple    				=			False
				A__ :  Union[str, Any]    				=			False
				A__ :  Dict    				=			False
				A__ :  Optional[Any]    				=			False

				def 						_a      ( self ):
										lowerCamelCase__   =GPTNeoXModelTester(self )
										lowerCamelCase__   =ConfigTester(self      ,					config_class=SCREAMING_SNAKE_CASE_      ,					hidden_size=64      ,					num_attention_heads=8 )

				def 						_a      ( self ):
										self.config_tester.run_common_tests()

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_decoder()

										lowerCamelCase__   =None

										self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )

				@unittest.skip(reason="Feed forward chunking is not implemented" )
				def 						_a      ( self ):
										pass






				@parameterized.expand([("linear",), ("dynamic",)] )
				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__   =ids_tensor([1, 10]      ,					config.vocab_size )
										lowerCamelCase__   =ids_tensor([1, int(config.max_position_embeddings * 1.5 )]      ,					config.vocab_size )

										set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
										lowerCamelCase__   =GPTNeoXModel(SCREAMING_SNAKE_CASE_ )
										original_model.to(SCREAMING_SNAKE_CASE_ )
										original_model.eval()
										lowerCamelCase__   =original_model(SCREAMING_SNAKE_CASE_ ).last_hidden_state
										lowerCamelCase__   =original_model(SCREAMING_SNAKE_CASE_ ).last_hidden_state

										set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
										lowerCamelCase__   ={"type": scaling_type, "factor": 1_0.0}
										lowerCamelCase__   =GPTNeoXModel(SCREAMING_SNAKE_CASE_ )
										scaled_model.to(SCREAMING_SNAKE_CASE_ )
										scaled_model.eval()
										lowerCamelCase__   =scaled_model(SCREAMING_SNAKE_CASE_ ).last_hidden_state
										lowerCamelCase__   =scaled_model(SCREAMING_SNAKE_CASE_ ).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(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					atol=1E-5 ) )
										else:
																self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					atol=1E-5 ) )

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






@require_torch
class   __UpperCAmelCase		(      unittest.TestCase      ):

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped" )
										for checkpointing in [True, False]:
																lowerCamelCase__   =GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped" )

																if checkpointing:
																						model.gradient_checkpointing_enable()
																else:
																						model.gradient_checkpointing_disable()
																model.to(SCREAMING_SNAKE_CASE_ )

																lowerCamelCase__   =tokenizer("My favorite food is"      ,					return_tensors="pt" ).to(SCREAMING_SNAKE_CASE_ )
																# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
																# See: https://github.com/huggingface/transformers/pull/24193
																lowerCamelCase__   ="My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure"

																lowerCamelCase__   =model.generate(**SCREAMING_SNAKE_CASE_      ,					do_sample=SCREAMING_SNAKE_CASE_      ,					max_new_tokens=20 )
																lowerCamelCase__   =tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )[0]

																self.assertEqual(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )




"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass