
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


lowercase__	:Tuple										=					logging.getLogger(__name__)







class        lowercase	(      __lowercase	):
							lowercase_ : int       ='''summarization'''
							lowercase_ : Tuple       =['''loss''']
							lowercase_ : List[Any]       =ROUGE_KEYS
							lowercase_ : Optional[int]       ='''rouge2'''




							def __init__(       self     ,A__     ,**A__):
								if hparams.sortish_sampler and hparams.gpus > 1:
									lowercase					=		False
								elif hparams.max_tokens_per_batch is not None:
									if hparams.gpus > 1:
										raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training''')
									if hparams.sortish_sampler:
										raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously''')

								super().__init__(_A     ,num_labels=_A     ,mode=self.mode     ,**_A)
								use_task_specific_params(self.model     ,'''summarization''')
								save_git_info(self.hparams.output_dir)
								lowercase					=		Path(self.output_dir) / '''metrics.json'''
								lowercase					=		Path(self.output_dir) / '''hparams.pkl'''
								pickle_save(self.hparams     ,self.hparams_save_path)
								lowercase					=		0
								lowercase					=		defaultdict(_A)
								lowercase					=		self.config.model_type
								lowercase					=		self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

								lowercase					=		{
								    '''data_dir''': self.hparams.data_dir,
								    '''max_source_length''': self.hparams.max_source_length,
								    '''prefix''': self.model.config.prefix or '''''',
								}
								lowercase					=		{
								    '''train''': self.hparams.n_train,
								    '''val''': self.hparams.n_val,
								    '''test''': self.hparams.n_test,
								}
								lowercase					=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

								lowercase					=		{
								    '''train''': self.hparams.max_target_length,
								    '''val''': self.hparams.val_max_target_length,
								    '''test''': self.hparams.test_max_target_length,
								}
								assert self.target_lens["train"] <= self.target_lens["val"], f'target_lens: {self.target_lens}'
								assert self.target_lens["train"] <= self.target_lens["test"], f'target_lens: {self.target_lens}'
								if self.hparams.freeze_embeds:
									freeze_embeds(self.model)
								if self.hparams.freeze_encoder:
									freeze_params(self.model.get_encoder())
									assert_all_frozen(self.model.get_encoder())

								lowercase					=		get_git_info()['''repo_sha''']
								lowercase					=		hparams.num_workers
								lowercase					=		None  # default to config
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer     ,_A):
									lowercase					=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
									lowercase					=		self.decoder_start_token_id
								lowercase					=		(
								    SeqaSeqDataset if hasattr(self.tokenizer     ,'''prepare_seq2seq_batch''') else LegacySeqaSeqDataset
								)
								lowercase					=		False
								lowercase					=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
								if self.hparams.eval_max_gen_length is not None:
									lowercase					=		self.hparams.eval_max_gen_length
								else:
									lowercase					=		self.model.config.max_length
								lowercase					=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




							def 				A__   (       self     ,A__):
								lowercase					=		{
								    k: self.tokenizer.batch_decode(v.tolist()) if '''mask''' not in k else v.shape for k, v in batch.items()
								}
								save_json(_A     ,Path(self.output_dir) / '''text_batch.json''')
								save_json({k: v.tolist() for k, v in batch.items()}     ,Path(self.output_dir) / '''tok_batch.json''')

								lowercase					=		True
								return readable_batch




							def 				A__   (       self     ,A__     ,**A__):
								return self.model(_A     ,**_A)




							def 				A__   (       self     ,A__):
								lowercase					=		self.tokenizer.batch_decode(
								    _A     ,skip_special_tokens=_A     ,clean_up_tokenization_spaces=_A)
								return lmap(str.strip     ,_A)




							def 				A__   (       self     ,A__):
								lowercase					=		self.tokenizer.pad_token_id
								lowercase			,		lowercase					=		batch['''input_ids'''], batch['''attention_mask''']
								lowercase					=		batch['''labels''']
								if isinstance(self.model     ,_A):
									lowercase					=		self.model._shift_right(_A)
								else:
									lowercase					=		shift_tokens_right(_A     ,_A)
								if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
									lowercase					=		decoder_input_ids
									self.save_readable_batch(_A)

								lowercase					=		self(_A     ,attention_mask=_A     ,decoder_input_ids=_A     ,use_cache=_A)
								lowercase					=		outputs['''logits''']
								if self.hparams.label_smoothing == 0:
									# Same behavior as modeling_bart.py, besides ignoring pad_token_id
									lowercase					=		nn.CrossEntropyLoss(ignore_index=_A)

									assert lm_logits.shape[-1] == self.vocab_size
									lowercase					=		ce_loss_fct(lm_logits.view(-1     ,lm_logits.shape[-1])     ,tgt_ids.view(-1))
								else:
									lowercase					=		nn.functional.log_softmax(_A     ,dim=-1)
									lowercase			,		lowercase					=		label_smoothed_nll_loss(
									    _A     ,_A     ,self.hparams.label_smoothing     ,ignore_index=_A)
								return (loss,)




							@property
							def 				A__   (       self):
								return self.tokenizer.pad_token_id




							def 				A__   (       self     ,A__     ,A__):
								lowercase					=		self._step(_A)

								lowercase					=		dict(zip(self.loss_names     ,_A))
								# tokens per batch
								lowercase					=		batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
								lowercase					=		batch['''input_ids'''].shape[0]
								lowercase					=		batch['''input_ids'''].eq(self.pad).sum()
								lowercase					=		batch['''input_ids'''].eq(self.pad).float().mean()
								# TODO(SS): make a wandb summary metric for this
								return {"loss": loss_tensors[0], "log": logs}




							def 				A__   (       self     ,A__     ,A__):
								return self._generative_step(_A)




							def 				A__   (       self     ,A__     ,A__="val"):
								self.step_count += 1
								lowercase					=		{k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
								lowercase					=		losses['''loss''']
								lowercase					=		{
								    k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
								}
								lowercase					=		(
								    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
								)
								lowercase					=		torch.tensor(_A).type_as(_A)
								generative_metrics.update({k: v.item() for k, v in losses.items()})
								losses.update(_A)
								lowercase					=		{f'{prefix}_avg_{k}': x for k, x in losses.items()}
								lowercase					=		self.step_count
								self.metrics[prefix].append(_A)  # callback writes this to self.metrics_save_path
								lowercase					=		flatten_list([x['''preds'''] for x in outputs])
								return {
								    "log": all_metrics,
								    "preds": preds,
								    f'{prefix}_loss': loss,
								    f'{prefix}_{self.val_metric}': metric_tensor,
								}




							def 				A__   (       self     ,A__     ,A__):
								return calculate_rouge(_A     ,_A)




							def 				A__   (       self     ,A__):
								lowercase					=		time.time()

								# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
								lowercase					=		self.model.generate(
								    batch['''input_ids''']     ,attention_mask=batch['''attention_mask''']     ,use_cache=_A     ,decoder_start_token_id=self.decoder_start_token_id     ,num_beams=self.eval_beams     ,max_length=self.eval_max_length     ,)
								lowercase					=		(time.time() - ta) / batch['''input_ids'''].shape[0]
								lowercase					=		self.ids_to_clean_text(_A)
								lowercase					=		self.ids_to_clean_text(batch['''labels'''])
								lowercase					=		self._step(_A)
								lowercase					=		dict(zip(self.loss_names     ,_A))
								lowercase					=		self.calc_generative_metrics(_A     ,_A)
								lowercase					=		np.mean(lmap(_A     ,_A))
								base_metrics.update(gen_time=_A     ,gen_len=_A     ,preds=_A     ,target=_A     ,**_A)
								return base_metrics




							def 				A__   (       self     ,A__     ,A__):
								return self._generative_step(_A)




							def 				A__   (       self     ,A__):
								return self.validation_epoch_end(_A     ,prefix='''test''')




							def 				A__   (       self     ,A__):
								lowercase					=		self.n_obs[type_path]
								lowercase					=		self.target_lens[type_path]
								lowercase					=		self.dataset_class(
								    self.tokenizer     ,type_path=_A     ,n_obs=_A     ,max_target_length=_A     ,**self.dataset_kwargs     ,)
								return dataset




							def 				A__   (       self     ,A__     ,A__     ,A__ = False):
								lowercase					=		self.get_dataset(_A)

								if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
									lowercase					=		dataset.make_sortish_sampler(_A     ,distributed=self.hparams.gpus > 1)
									return DataLoader(
									    _A     ,batch_size=_A     ,collate_fn=dataset.collate_fn     ,shuffle=_A     ,num_workers=self.num_workers     ,sampler=_A     ,)

								elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
									lowercase					=		dataset.make_dynamic_sampler(
									    self.hparams.max_tokens_per_batch     ,distributed=self.hparams.gpus > 1)
									return DataLoader(
									    _A     ,batch_sampler=_A     ,collate_fn=dataset.collate_fn     ,num_workers=self.num_workers     ,)
								else:
									return DataLoader(
									    _A     ,batch_size=_A     ,collate_fn=dataset.collate_fn     ,shuffle=_A     ,num_workers=self.num_workers     ,sampler=_A     ,)




							def 				A__   (       self):
								lowercase					=		self.get_dataloader('''train'''     ,batch_size=self.hparams.train_batch_size     ,shuffle=_A)
								return dataloader




							def 				A__   (       self):
								return self.get_dataloader('''val'''     ,batch_size=self.hparams.eval_batch_size)




							def 				A__   (       self):
								return self.get_dataloader('''test'''     ,batch_size=self.hparams.eval_batch_size)





							@staticmethod
							def 				A__   (       A__     ,A__):
								BaseTransformer.add_model_specific_args(_A     ,_A)
								add_generic_args(_A     ,_A)
								parser.add_argument(
								    '''--max_source_length'''     ,default=1_0_2_4     ,type=_A     ,help=(
								        '''The maximum total input sequence length after tokenization. Sequences longer '''
								        '''than this will be truncated, sequences shorter will be padded.'''
								    )     ,)
								parser.add_argument(
								    '''--max_target_length'''     ,default=5_6     ,type=_A     ,help=(
								        '''The maximum total input sequence length after tokenization. Sequences longer '''
								        '''than this will be truncated, sequences shorter will be padded.'''
								    )     ,)
								parser.add_argument(
								    '''--val_max_target_length'''     ,default=1_4_2     ,type=_A     ,help=(
								        '''The maximum total input sequence length after tokenization. Sequences longer '''
								        '''than this will be truncated, sequences shorter will be padded.'''
								    )     ,)
								parser.add_argument(
								    '''--test_max_target_length'''     ,default=1_4_2     ,type=_A     ,help=(
								        '''The maximum total input sequence length after tokenization. Sequences longer '''
								        '''than this will be truncated, sequences shorter will be padded.'''
								    )     ,)
								parser.add_argument('''--freeze_encoder'''     ,action='''store_true''')
								parser.add_argument('''--freeze_embeds'''     ,action='''store_true''')
								parser.add_argument('''--sortish_sampler'''     ,action='''store_true'''     ,default=_A)
								parser.add_argument('''--overwrite_output_dir'''     ,action='''store_true'''     ,default=_A)
								parser.add_argument('''--max_tokens_per_batch'''     ,type=_A     ,default=_A)
								parser.add_argument('''--logger_name'''     ,type=_A     ,choices=['''default''', '''wandb''', '''wandb_shared''']     ,default='''default''')
								parser.add_argument('''--n_train'''     ,type=_A     ,default=-1     ,required=_A     ,help='''# examples. -1 means use all.''')
								parser.add_argument('''--n_val'''     ,type=_A     ,default=5_0_0     ,required=_A     ,help='''# examples. -1 means use all.''')
								parser.add_argument('''--n_test'''     ,type=_A     ,default=-1     ,required=_A     ,help='''# examples. -1 means use all.''')
								parser.add_argument(
								    '''--task'''     ,type=_A     ,default='''summarization'''     ,required=_A     ,help='''# examples. -1 means use all.''')
								parser.add_argument('''--label_smoothing'''     ,type=_A     ,default=0.0     ,required=_A)
								parser.add_argument('''--src_lang'''     ,type=_A     ,default=''''''     ,required=_A)
								parser.add_argument('''--tgt_lang'''     ,type=_A     ,default=''''''     ,required=_A)
								parser.add_argument('''--eval_beams'''     ,type=_A     ,default=_A     ,required=_A)
								parser.add_argument(
								    '''--val_metric'''     ,type=_A     ,default=_A     ,required=_A     ,choices=['''bleu''', '''rouge2''', '''loss''', None])
								parser.add_argument('''--eval_max_gen_length'''     ,type=_A     ,default=_A     ,help='''never generate more than n tokens''')
								parser.add_argument('''--save_top_k'''     ,type=_A     ,default=1     ,required=_A     ,help='''How many checkpoints to save''')
								parser.add_argument(
								    '''--early_stopping_patience'''     ,type=_A     ,default=-1     ,required=_A     ,help=(
								        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
								        ''' val_check_interval will effect it.'''
								    )     ,)
								return parser







class        lowercase	(      __lowercase	):
							lowercase_ : Dict       ='''translation'''
							lowercase_ : Union[str, Any]       =['''loss''']
							lowercase_ : List[str]       =['''bleu''']
							lowercase_ : Tuple       ='''bleu'''




							def __init__(       self     ,A__     ,**A__):
								super().__init__(_A     ,**_A)
								lowercase					=		hparams.src_lang
								lowercase					=		hparams.tgt_lang





							def 				A__   (       self     ,A__     ,A__):
								return calculate_bleu(_A     ,_A)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None	):
	'''simple docstring'''
	Path(args.output_dir	).mkdir(exist_ok=__snake_case	)
	check_output_dir(__snake_case				,    expected_items=3	)

	if model is None:
		if "summarization" in args.task:
			lowercase					=		SummarizationModule(__snake_case	)
		else:
			lowercase					=		TranslationModule(__snake_case	)
	lowercase					=		Path(args.data_dir	).name
	if (
	    args.logger_name == "default"
	    or args.fast_dev_run
	    or str(args.output_dir	).startswith('''/tmp'''	)
	    or str(args.output_dir	).startswith('''/var'''	)
	):
		lowercase					=		True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		lowercase					=		os.environ.get('''WANDB_PROJECT'''				,    __snake_case	)
		lowercase					=		WandbLogger(name=model.output_dir.name				,    project=__snake_case	)

	elif args.logger_name == "wandb_shared":
		from pytorch_lightning.loggers import WandbLogger

		lowercase					=		WandbLogger(name=model.output_dir.name				,    project=f'hf_{dataset}'	)

	if args.early_stopping_patience >= 0:
		lowercase					=		get_early_stopping_callback(model.val_metric				,    args.early_stopping_patience	)
	else:
		lowercase					=		False

	lowercase					=		args.val_metric == '''loss'''
	lowercase					=		generic_train(
	    __snake_case				,    __snake_case				,    logging_callback=SeqaSeqLoggingCallback()				,    checkpoint_callback=get_checkpoint_callback(
	        args.output_dir				,    model.val_metric				,    args.save_top_k				,    __snake_case	)				,    early_stopping_callback=__snake_case				,    logger=__snake_case				,    )
	pickle_save(model.hparams				,    model.output_dir / '''hparams.pkl'''	)
	if not args.do_predict:
		return model

	lowercase					=		''''''
	lowercase					=		sorted(glob.glob(os.path.join(args.output_dir				,    '''*.ckpt'''	)				,    recursive=__snake_case	)	)
	if checkpoints:
		lowercase					=		checkpoints[-1]
		lowercase					=		checkpoints[-1]
	trainer.logger.log_hyperparams(model.hparams	)

	# test() without a model tests using the best checkpoint automatically
	trainer.test()
	return model


if __name__ == "__main__":
							lowercase__	:str										=					argparse.ArgumentParser()
							lowercase__	:Any										=					pl.Trainer.add_argparse_args(parser)
							lowercase__	:Optional[int]										=					SummarizationModule.add_model_specific_args(parser, os.getcwd())

							lowercase__	:List[Any]										=					parser.parse_args()

							main(args)






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(UniPCMultistepScheduler,)
							lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)
																	# copy over dummy past residuals
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

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

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

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




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(A__)

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

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self     ,A__=None     ,**A__):
												if scheduler is None:
																	lowercase					=		self.scheduler_classes[0]
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

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

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

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

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

																	if num_inference_steps is not None and hasattr(A__     ,'''set_timesteps'''):
																						scheduler.set_timesteps(A__)
																	elif num_inference_steps is not None and not hasattr(A__     ,'''set_timesteps'''):
																						lowercase					=		num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	lowercase					=		scheduler.timesteps[5]
																	lowercase					=		scheduler.timesteps[6]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

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




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

												assert abs(result_mean.item() - 0.2464) < 1E-3

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

												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												self.check_over_configs(thresholding=A__)
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,solver_order=A__     ,solver_type=A__     ,)




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




							def 				A__   (       self):
												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											lowercase					=		self.full_loop(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											assert not torch.isnan(A__).any(), "Samples have nan numbers"




							def 				A__   (       self):
												self.check_over_configs(lower_order_final=A__)
												self.check_over_configs(lower_order_final=A__)




							def 				A__   (       self):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=A__     ,time_step=0)




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.1014) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(thresholding=A__     ,dynamic_thresholding_ratio=0)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter.half()
												scheduler.set_timesteps(A__)

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

												assert sample.dtype == torch.floataa





							def 				A__   (       self     ,**A__):
												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

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




