



import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__SCREAMING_SNAKE_CASE           =  """bert-base-cased"""
__SCREAMING_SNAKE_CASE           =  """google/pegasus-xsum"""
__SCREAMING_SNAKE_CASE           =  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__SCREAMING_SNAKE_CASE           =  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__SCREAMING_SNAKE_CASE           =  """patrickvonplaten/t5-tiny-random"""
__SCREAMING_SNAKE_CASE           =  """sshleifer/bart-tiny-random"""
__SCREAMING_SNAKE_CASE           =  """sshleifer/tiny-mbart"""
__SCREAMING_SNAKE_CASE           =  """sshleifer/tiny-marian-en-de"""




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						"\n".join(_lowerCamelCase    )
			Path(_lowerCamelCase    ).open("w"    ).writelines(_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(_lowerCamelCase ,    f"""{split}.source"""    ) ,    _lowerCamelCase    )
						_dump_articles(os.path.join(_lowerCamelCase ,    f"""{split}.target"""    ) ,    _lowerCamelCase    )
			return tmp_dir






class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]	,	)
						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int] )      ->				Tuple:
									A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									A							:					Tuple              =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
									A							:					Optional[Any]              =						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in ARTICLES )
									A							:					Optional[Any]              =						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in SUMMARIES )
									A							:					Tuple              =						4
									A							:					int              =						8
									assert max_len_target > max_src_len  # Will be truncated
									assert max_len_source > max_src_len  # Will be truncated
									A							:					Optional[Any]              =						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
									A							:					Any              =						SeqaSeqDataset(
									    __lowerCamelCase	,	data_dir=__lowerCamelCase	,	type_path="train"	,	max_source_length=__lowerCamelCase	,	max_target_length=__lowerCamelCase	,	src_lang=__lowerCamelCase	,	tgt_lang=__lowerCamelCase	,	)
									A							:					List[Any]              =						DataLoader(__lowerCamelCase	,	batch_size=2	,	collate_fn=train_dataset.collate_fn )
									for batch in dataloader:
												assert isinstance(__lowerCamelCase	,	__lowerCamelCase )
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_src_len
												# show that targets are the same len
												assert batch["labels"].shape[1] == max_tgt_len
												if tok_name != MBART_TINY:
															continue
												# check language codes in correct place
												A							:					str              =						shift_tokens_right(batch["labels"]	,	tokenizer.pad_token_id )
												assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
												assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

												break  # No need to test every batch







						@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			List[str] )      ->				List[str]:
									A							:					Union[str, Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									A							:					Any              =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
									A							:					List[Any]              =						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in ARTICLES )
									A							:					int              =						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in SUMMARIES )
									A							:					List[str]              =						4
									A							:					str              =						LegacySeqaSeqDataset(
									    __lowerCamelCase	,	data_dir=__lowerCamelCase	,	type_path="train"	,	max_source_length=20	,	max_target_length=__lowerCamelCase	,	)
									A							:					List[str]              =						DataLoader(__lowerCamelCase	,	batch_size=2	,	collate_fn=train_dataset.collate_fn )
									for batch in dataloader:
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_len_source
												assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
												# show that targets were truncated
												assert batch["labels"].shape[1] == trunc_target  # Truncated
												assert max_len_target > trunc_target  # Truncated
												break  # No need to test every batch







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[str]:
									A							:					Tuple              =						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )

									A							:					Optional[Any]              =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
									A							:					Optional[int]              =						tmp_dir.joinpath("train.source" ).open().readlines()
									A							:					Union[str, Any]              =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
									pack_data_dir(__lowerCamelCase	,	__lowerCamelCase	,	1_28	,	__lowerCamelCase )
									A							:					List[str]              =						{x.name for x in tmp_dir.iterdir()}
									A							:					Tuple              =						{x.name for x in save_dir.iterdir()}
									A							:					List[str]              =						save_dir.joinpath("train.source" ).open().readlines()
									# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
									# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
									assert len(__lowerCamelCase ) < len(__lowerCamelCase )
									assert len(__lowerCamelCase ) == 1
									assert len(packed_examples[0] ) == sum(len(__lowerCamelCase ) for x in orig_examples )
									assert orig_paths == new_paths







						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE	,	reason="This test requires fairseq" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									if not FAIRSEQ_AVAILABLE:
												return
									A							:					Any              =						self._get_dataset(max_len=64 )
									A							:					List[Any]              =						64
									A							:					str              =						ds.make_dynamic_sampler(__lowerCamelCase	,	required_batch_size_multiple=__lowerCamelCase )
									A							:					Optional[Any]              =						[len(__lowerCamelCase ) for x in batch_sampler]
									assert len(set(__lowerCamelCase ) ) > 1  # it's not dynamic batch size if every batch is the same length
									assert sum(__lowerCamelCase ) == len(__lowerCamelCase )  # no dropped or added examples
									A							:					Any              =						DataLoader(__lowerCamelCase	,	batch_sampler=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2 )
									A							:					List[Any]              =						[]
									A							:					Union[str, Any]              =						[]
									for batch in data_loader:
												A							:					Union[str, Any]              =						batch["input_ids"].shape
												A							:					str              =						src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												A							:					int              =						np.product(batch["input_ids"].shape )
												num_src_per_batch.append(__lowerCamelCase )
												if num_src_tokens > (max_tokens * 1.1):
															failures.append(__lowerCamelCase )
									assert num_src_per_batch[0] == max(__lowerCamelCase )
									if failures:
												raise AssertionError(F"""too many tokens in {len(__lowerCamelCase )} batches""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				str:
									A							:					Dict              =						self._get_dataset(max_len=5_12 )
									A							:					List[str]              =						2
									A							:					List[str]              =						ds.make_sortish_sampler(__lowerCamelCase	,	shuffle=__lowerCamelCase )

									A							:					Any              =						DataLoader(__lowerCamelCase	,	batch_size=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2 )
									A							:					List[str]              =						DataLoader(__lowerCamelCase	,	batch_size=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2	,	sampler=__lowerCamelCase )

									A							:					Optional[Any]              =						tokenizer.pad_token_id

									def count_pad_tokens(__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Dict="input_ids" ):
												return [batch[k].eq(__lowerCamelCase ).sum().item() for batch in data_loader]

									assert sum(count_pad_tokens(__lowerCamelCase	,	k="labels" ) ) < sum(count_pad_tokens(__lowerCamelCase	,	k="labels" ) )
									assert sum(count_pad_tokens(__lowerCamelCase ) ) < sum(count_pad_tokens(__lowerCamelCase ) )
									assert len(__lowerCamelCase ) == len(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any=10_00	,	__lowerCamelCase      :			List[Any]=1_28 )      ->				Any:
									if os.getenv("USE_REAL_DATA"	,	__lowerCamelCase ):
												A							:					List[Any]              =						"examples/seq2seq/wmt_en_ro"
												A							:					Dict              =						max_len * 2 * 64
												if not Path(__lowerCamelCase ).joinpath("train.len" ).exists():
															save_len_file(__lowerCamelCase	,	__lowerCamelCase )
									else:
												A							:					Tuple              =						"examples/seq2seq/test_data/wmt_en_ro"
												A							:					List[str]              =						max_len * 4
												save_len_file(__lowerCamelCase	,	__lowerCamelCase )

									A							:					Any              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									A							:					Tuple              =						SeqaSeqDataset(
									    __lowerCamelCase	,	data_dir=__lowerCamelCase	,	type_path="train"	,	max_source_length=__lowerCamelCase	,	max_target_length=__lowerCamelCase	,	n_obs=__lowerCamelCase	,	)
									return ds, max_tokens, tokenizer







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Any:
									A							:					Any              =						self._get_dataset()
									A							:					Dict              =						set(DistributedSortishSampler(__lowerCamelCase	,	2_56	,	num_replicas=2	,	rank=0	,	add_extra_examples=__lowerCamelCase ) )
									A							:					Union[str, Any]              =						set(DistributedSortishSampler(__lowerCamelCase	,	2_56	,	num_replicas=2	,	rank=1	,	add_extra_examples=__lowerCamelCase ) )
									assert idsa.intersection(__lowerCamelCase ) == set()







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]	,	)
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[str] )      ->				Optional[Any]:
									A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	use_fast=__lowerCamelCase )
									if tok_name == MBART_TINY:
												A							:					str              =						SeqaSeqDataset(
												    __lowerCamelCase	,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )	,	type_path="train"	,	max_source_length=4	,	max_target_length=8	,	src_lang="EN"	,	tgt_lang="FR"	,	)
												A							:					int              =						train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
												A							:					Dict              =						SeqaSeqDataset(
												    __lowerCamelCase	,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )	,	type_path="train"	,	max_source_length=4	,	max_target_length=8	,	)
												A							:					int              =						train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(__lowerCamelCase ) == 1 if tok_name == BART_TINY else len(__lowerCamelCase ) == 0




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