
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


__lowerCAmelCase					   =       logging.getLogger(__name__)





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					List[str]    		=	'summarization'
	UpperCamelCase_  :					Optional[Any]    		=	['loss']
	UpperCamelCase_  :					List[str]    		=	ROUGE_KEYS
	UpperCamelCase_  :					Optional[int]    		=	'rouge2'







	def __init__(     self      :str   ,     lowercase      :Optional[int]   ,     **lowercase      :Union[str, Any]			)		->      Any:







				"""simple docstring"""






				if hparams.sortish_sampler and hparams.gpus > 1:
							SCREAMING_SNAKE_CASE     						= 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__(lowercase   ,     num_labels=lowercase   ,     mode=self.mode   ,     **lowercase			)
				use_task_specific_params(self.model   ,     '''summarization'''			)
				save_git_info(self.hparams.output_dir			)
				SCREAMING_SNAKE_CASE     						= Path(self.output_dir			) / '''metrics.json'''
				SCREAMING_SNAKE_CASE     						= Path(self.output_dir			) / '''hparams.pkl'''
				pickle_save(self.hparams   ,     self.hparams_save_path			)
				SCREAMING_SNAKE_CASE     						= 0
				SCREAMING_SNAKE_CASE     						= defaultdict(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.config.model_type
				SCREAMING_SNAKE_CASE     						= self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

				SCREAMING_SNAKE_CASE     						= {
				    '''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()			)

				SCREAMING_SNAKE_CASE     						= get_git_info()['''repo_sha''']
				SCREAMING_SNAKE_CASE     						= hparams.num_workers
				SCREAMING_SNAKE_CASE     						= None  # default to config
				if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,     lowercase			):
							SCREAMING_SNAKE_CASE     						= self.tokenizer.lang_code_to_id[hparams.tgt_lang]
							SCREAMING_SNAKE_CASE     						= self.decoder_start_token_id
				SCREAMING_SNAKE_CASE     						= (
				    SeqaSeqDataset if hasattr(self.tokenizer   ,     '''prepare_seq2seq_batch'''			) else LegacySeqaSeqDataset
				)
				SCREAMING_SNAKE_CASE     						= False
				SCREAMING_SNAKE_CASE     						= 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:
							SCREAMING_SNAKE_CASE     						= self.hparams.eval_max_gen_length
				else:
							SCREAMING_SNAKE_CASE     						= self.model.config.max_length
				SCREAMING_SNAKE_CASE     						= self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







	def  snake_case__     (     self      :int   ,     lowercase      :Dict[str, torch.Tensor]			)		->      Dict[str, List[str]]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    k: self.tokenizer.batch_decode(v.tolist()			) if '''mask''' not in k else v.shape for k, v in batch.items()
				}
				save_json(lowercase   ,     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'''			)

				SCREAMING_SNAKE_CASE     						= True
				return readable_batch







	def  snake_case__     (     self      :List[Any]   ,     lowercase      :List[str]   ,     **lowercase      :int			)		->      Tuple:







				"""simple docstring"""






				return self.model(lowercase   ,     **lowercase			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :List[int]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.tokenizer.batch_decode(
				    lowercase   ,     skip_special_tokens=lowercase   ,     clean_up_tokenization_spaces=lowercase			)
				return lmap(str.strip   ,     lowercase			)







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :dict			)		->      Tuple:







				"""simple docstring"""






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

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

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







	@property
	def  snake_case__     (     self      :List[Any]			)		->      int:







				"""simple docstring"""






				return self.tokenizer.pad_token_id







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :Dict   ,     lowercase      :List[Any]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self._step(lowercase			)

				SCREAMING_SNAKE_CASE     						= dict(zip(self.loss_names   ,     lowercase			)			)
				# tokens per batch
				SCREAMING_SNAKE_CASE     						= batch['''input_ids'''].ne(self.pad			).sum() + batch['''labels'''].ne(self.pad			).sum()
				SCREAMING_SNAKE_CASE     						= batch['''input_ids'''].shape[0]
				SCREAMING_SNAKE_CASE     						= batch['''input_ids'''].eq(self.pad			).sum()
				SCREAMING_SNAKE_CASE     						= 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  snake_case__     (     self      :List[str]   ,     lowercase      :List[str]   ,     lowercase      :Tuple			)		->      Dict:







				"""simple docstring"""






				return self._generative_step(lowercase			)







	def  snake_case__     (     self      :Tuple   ,     lowercase      :List[str]   ,     lowercase      :Optional[Any]="val"			)		->      Dict:







				"""simple docstring"""






				self.step_count += 1
				SCREAMING_SNAKE_CASE     						= {k: torch.stack([x[k] for x in outputs]			).mean() for k in self.loss_names}
				SCREAMING_SNAKE_CASE     						= losses['''loss''']
				SCREAMING_SNAKE_CASE     						= {
				    k: np.array([x[k] for x in outputs]			).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
				}
				SCREAMING_SNAKE_CASE     						= (
				    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
				)
				SCREAMING_SNAKE_CASE     						= torch.tensor(lowercase			).type_as(lowercase			)
				generative_metrics.update({k: v.item() for k, v in losses.items()}			)
				losses.update(lowercase			)
				SCREAMING_SNAKE_CASE     						= {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
				SCREAMING_SNAKE_CASE     						= self.step_count
				self.metrics[prefix].append(lowercase			)  # callback writes this to self.metrics_save_path
				SCREAMING_SNAKE_CASE     						= 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  snake_case__     (     self      :Optional[int]   ,     lowercase      :Tuple   ,     lowercase      :Optional[int]			)		->      Dict:







				"""simple docstring"""






				return calculate_rouge(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :dict			)		->      dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= time.time()

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







	def  snake_case__     (     self      :List[str]   ,     lowercase      :Dict   ,     lowercase      :Any			)		->      Union[str, Any]:







				"""simple docstring"""






				return self._generative_step(lowercase			)







	def  snake_case__     (     self      :Tuple   ,     lowercase      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				return self.validation_epoch_end(lowercase   ,     prefix='''test'''			)







	def  snake_case__     (     self      :Tuple   ,     lowercase      :List[str]			)		->      SeqaSeqDataset:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.n_obs[type_path]
				SCREAMING_SNAKE_CASE     						= self.target_lens[type_path]
				SCREAMING_SNAKE_CASE     						= self.dataset_class(
				    self.tokenizer   ,     type_path=lowercase   ,     n_obs=lowercase   ,     max_target_length=lowercase   ,     **self.dataset_kwargs   ,     )
				return dataset







	def  snake_case__     (     self      :str   ,     lowercase      :str   ,     lowercase      :int   ,     lowercase      :bool = False			)		->      DataLoader:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_dataset(lowercase			)

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

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







	def  snake_case__     (     self      :Tuple			)		->      DataLoader:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_dataloader('''train'''   ,     batch_size=self.hparams.train_batch_size   ,     shuffle=lowercase			)
				return dataloader







	def  snake_case__     (     self      :Optional[Any]			)		->      DataLoader:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Tuple			)		->      DataLoader:







				"""simple docstring"""






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







	@staticmethod
	def  snake_case__     (     lowercase      :Union[str, Any]   ,     lowercase      :Any			)		->      Optional[int]:







				"""simple docstring"""






				BaseTransformer.add_model_specific_args(lowercase   ,     lowercase			)
				add_generic_args(lowercase   ,     lowercase			)
				parser.add_argument(
				    '''--max_source_length'''   ,     default=1_0_2_4   ,     type=lowercase   ,     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=lowercase   ,     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=lowercase   ,     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=lowercase   ,     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=lowercase			)
				parser.add_argument('''--overwrite_output_dir'''   ,     action='''store_true'''   ,     default=lowercase			)
				parser.add_argument('''--max_tokens_per_batch'''   ,     type=lowercase   ,     default=lowercase			)
				parser.add_argument('''--logger_name'''   ,     type=lowercase   ,     choices=['''default''', '''wandb''', '''wandb_shared''']   ,     default='''default'''			)
				parser.add_argument('''--n_train'''   ,     type=lowercase   ,     default=-1   ,     required=lowercase   ,     help='''# examples. -1 means use all.'''			)
				parser.add_argument('''--n_val'''   ,     type=lowercase   ,     default=5_0_0   ,     required=lowercase   ,     help='''# examples. -1 means use all.'''			)
				parser.add_argument('''--n_test'''   ,     type=lowercase   ,     default=-1   ,     required=lowercase   ,     help='''# examples. -1 means use all.'''			)
				parser.add_argument(
				    '''--task'''   ,     type=lowercase   ,     default='''summarization'''   ,     required=lowercase   ,     help='''# examples. -1 means use all.'''			)
				parser.add_argument('''--label_smoothing'''   ,     type=lowercase   ,     default=0.0   ,     required=lowercase			)
				parser.add_argument('''--src_lang'''   ,     type=lowercase   ,     default=''''''   ,     required=lowercase			)
				parser.add_argument('''--tgt_lang'''   ,     type=lowercase   ,     default=''''''   ,     required=lowercase			)
				parser.add_argument('''--eval_beams'''   ,     type=lowercase   ,     default=lowercase   ,     required=lowercase			)
				parser.add_argument(
				    '''--val_metric'''   ,     type=lowercase   ,     default=lowercase   ,     required=lowercase   ,     choices=['''bleu''', '''rouge2''', '''loss''', None]			)
				parser.add_argument('''--eval_max_gen_length'''   ,     type=lowercase   ,     default=lowercase   ,     help='''never generate more than n tokens'''			)
				parser.add_argument('''--save_top_k'''   ,     type=lowercase   ,     default=1   ,     required=lowercase   ,     help='''How many checkpoints to save'''			)
				parser.add_argument(
				    '''--early_stopping_patience'''   ,     type=lowercase   ,     default=-1   ,     required=lowercase   ,     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     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'translation'
	UpperCamelCase_  :					Tuple    		=	['loss']
	UpperCamelCase_  :					Any    		=	['bleu']
	UpperCamelCase_  :					Any    		=	'bleu'







	def __init__(     self      :str   ,     lowercase      :List[Any]   ,     **lowercase      :Union[str, Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				super().__init__(lowercase   ,     **lowercase			)
				SCREAMING_SNAKE_CASE     						= hparams.src_lang
				SCREAMING_SNAKE_CASE     						= hparams.tgt_lang







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :Dict   ,     lowercase      :Tuple			)		->      dict:







				"""simple docstring"""






				return calculate_bleu(lowercase   ,     lowercase			)



def    a      ( a		,						a=None	)     ->SummarizationModule:




			'''simple docstring'''


			Path(args.output_dir	).mkdir(exist_ok=a	)
			check_output_dir(a		,						expected_items=3	)

			if model is None:
						if "summarization" in args.task:
									SCREAMING_SNAKE_CASE     						= SummarizationModule(a	)
						else:
									SCREAMING_SNAKE_CASE     						= TranslationModule(a	)
			SCREAMING_SNAKE_CASE     						= 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'''	)
			):
						SCREAMING_SNAKE_CASE     						= True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						SCREAMING_SNAKE_CASE     						= os.environ.get('''WANDB_PROJECT'''		,						a	)
						SCREAMING_SNAKE_CASE     						= WandbLogger(name=model.output_dir.name		,						project=a	)

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

						SCREAMING_SNAKE_CASE     						= WandbLogger(name=model.output_dir.name		,						project=F"""hf_{dataset}"""	)

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

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

			SCREAMING_SNAKE_CASE     						= ''''''
			SCREAMING_SNAKE_CASE     						= sorted(glob.glob(os.path.join(args.output_dir		,						'''*.ckpt'''	)		,						recursive=a	)	)
			if checkpoints:
						SCREAMING_SNAKE_CASE     						= checkpoints[-1]
						SCREAMING_SNAKE_CASE     						= 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__":
							__lowerCAmelCase					   =       argparse.ArgumentParser()
							__lowerCAmelCase					   =       pl.Trainer.add_argparse_args(parser)
							__lowerCAmelCase					   =       SummarizationModule.add_model_specific_args(parser, os.getcwd())

							__lowerCAmelCase					   =       parser.parse_args()

							main(args)

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''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(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				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=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






				self.check_over_configs(thresholding=lowercase			)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=lowercase   ,     prediction_type=lowercase   ,     sample_max_value=lowercase   ,     )







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






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

				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  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter + 0.1
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter - 0.1

				SCREAMING_SNAKE_CASE     						= samplea.shape[0]
				SCREAMING_SNAKE_CASE     						= torch.stack([samplea, samplea, samplea]   ,     dim=0			)
				SCREAMING_SNAKE_CASE     						= torch.arange(lowercase			)[0:3, None].repeat(1   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

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







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

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







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

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







	def  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

				for i, timestep in enumerate(lowercase			):
							if i == len(lowercase			) - 1:
										SCREAMING_SNAKE_CASE     						= -1
							else:
										SCREAMING_SNAKE_CASE     						= timesteps[i + 1]

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

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







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

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