



'''simple docstring'''

import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
	from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
	snake_case_			=     True
	from torch.cuda.amp import autocast

snake_case_			=     logging.getLogger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]=None    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=None       ):
				return field(default_factory=lambda: default    ,	metadata=_SCREAMING_SNAKE_CASE       )


@dataclass
class 	a__     :
	__magic_name__		:    str	    =     field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},			)
	__magic_name__		:    Optional[bool]	    =     field(
	    default=_lowercase,			metadata={"help": "Whether to freeze the feature extractor layers of the model."}	)
	__magic_name__		:    Optional[float]	    =     field(
	    default=0.1,			metadata={"help": "The dropout ratio for the attention probabilities."}	)
	__magic_name__		:    Optional[float]	    =     field(
	    default=0.1,			metadata={"help": "The dropout ratio for activations inside the fully connected layer."}	)
	__magic_name__		:    Optional[float]	    =     field(
	    default=0.1,			metadata={
	        "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
	    },			)
	__magic_name__		:    Optional[float]	    =     field(
	    default=0.1,			metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."},			)
	__magic_name__		:    Optional[float]	    =     field(
	    default=0.05,			metadata={
	        "help": (
	            "Propability of each feature vector along the time axis to be chosen as the start of the vector"
	            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
	            "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
	        )
	    },			)
	__magic_name__		:    Optional[float]	    =     field(default=0.0,			metadata={"help": "The LayerDrop probability."}	)



@dataclass
class 	a__     :
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
	__magic_name__		:    Optional[str]	    =     field(
	    default="train+validation",			metadata={
	        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
	    },			)
	__magic_name__		:    bool	    =     field(
	    default=_lowercase,			metadata={"help": "Overwrite the cached preprocessed datasets or not."}	)
	__magic_name__		:    Optional[int]	    =     field(
	    default=_lowercase,			metadata={"help": "The number of processes to use for the preprocessing."},			)
	__magic_name__		:    Optional[int]	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    },			)
	__magic_name__		:    Optional[int]	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of validation examples to this "
	            "value if set."
	        )
	    },			)
	__magic_name__		:    List[str]	    =     list_field(
	    default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"],			metadata={"help": "A list of characters to remove from the transcripts."},			)



@dataclass
class 	a__     :
	__magic_name__		:    WavaVecaProcessor
	__magic_name__		:    Union[bool, str]	    =     True
	__magic_name__		:    Optional[int]	    =     None
	__magic_name__		:    Optional[int]	    =     None
	__magic_name__		:    Optional[int]	    =     None
	__magic_name__		:    Optional[int]	    =     None


	def __call__(self : Optional[Any],					__UpperCAmelCase : List[Dict[str, Union[List[int], torch.Tensor]]]      )     ->			Dict[str, torch.Tensor]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= [{'''input_values''': feature['''input_values''']} for feature in features]
					SCREAMING_SNAKE_CASE  :						str      					= [{'''input_ids''': feature['''labels''']} for feature in features]

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.processor.pad(
					    __UpperCAmelCase,					padding=self.padding,					max_length=self.max_length,					pad_to_multiple_of=self.pad_to_multiple_of,					return_tensors='''pt''',					)
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.processor.pad(
					    labels=__UpperCAmelCase,					padding=self.padding,					max_length=self.max_length_labels,					pad_to_multiple_of=self.pad_to_multiple_of_labels,					return_tensors='''pt''',					)

					# replace padding with -100 to ignore loss correctly
					SCREAMING_SNAKE_CASE  :						int      					= labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1      ),					-100      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= labels

					return batch



class 	a__     (				_lowercase	):


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : nn.Module,					__UpperCAmelCase : Dict[str, Union[torch.Tensor, Any]]      )     ->			torch.Tensor:

					"""simple docstring"""

					model.train()
					SCREAMING_SNAKE_CASE  :						str      					= self._prepare_inputs(__UpperCAmelCase      )

					if self.use_amp:
									with autocast():
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.compute_loss(__UpperCAmelCase,					__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						int      					= self.compute_loss(__UpperCAmelCase,					__UpperCAmelCase      )

					if self.args.n_gpu > 1:
									if model.module.config.ctc_loss_reduction == "mean":
													SCREAMING_SNAKE_CASE  :						Tuple      					= loss.mean()
									elif model.module.config.ctc_loss_reduction == "sum":
													SCREAMING_SNAKE_CASE  :						str      					= loss.sum() / (inputs['''labels'''] >= 0).sum()
									else:
													raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''      )

					if self.args.gradient_accumulation_steps > 1:
									SCREAMING_SNAKE_CASE  :						Dict      					= loss / self.args.gradient_accumulation_steps

					if self.use_amp:
									self.scaler.scale(__UpperCAmelCase      ).backward()
					elif self.use_apex:
									with amp.scale_loss(__UpperCAmelCase,					self.optimizer      ) as scaled_loss:
													scaled_loss.backward()
					elif self.deepspeed:
									self.deepspeed.backward(__UpperCAmelCase      )
					else:
									loss.backward()

					return loss.detach()






def       __lowercase  ():
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				SCREAMING_SNAKE_CASE  :						str      					= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
				if len(sys.argv       ) == 2 and sys.argv[1].endswith('''.json'''       ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= parser.parse_args_into_dataclasses()

				# Detecting last checkpoint.
				SCREAMING_SNAKE_CASE  :						Tuple      					= None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= get_last_checkpoint(training_args.output_dir       )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    '''Use --overwrite_output_dir to overcome.'''       )
								elif last_checkpoint is not None:
												logger.info(
												    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''       )

    # Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,	datefmt='''%m/%d/%Y %H:%M:%S'''    ,	handlers=[logging.StreamHandler(sys.stdout       )]    ,	)
				logger.setLevel(logging.INFO if is_main_process(training_args.local_rank       ) else logging.WARN       )

				# Log on each process the small summary:
				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + F'''distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'''       )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank       ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''    ,	_SCREAMING_SNAKE_CASE       )

				# Set seed before initializing model.
				set_seed(training_args.seed       )

				# Get the datasets:
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= datasets.load_dataset(
				    '''common_voice'''    ,	data_args.dataset_config_name    ,	split=data_args.train_split_name       )
				SCREAMING_SNAKE_CASE  :						str      					= datasets.load_dataset('''common_voice'''    ,	data_args.dataset_config_name    ,	split='''test'''       )

				# Create and save tokenizer
				SCREAMING_SNAKE_CASE  :						Tuple      					= F'''[{"".join(data_args.chars_to_ignore       )}]'''

				def remove_special_characters(_SCREAMING_SNAKE_CASE      :Tuple       ):
								SCREAMING_SNAKE_CASE  :						int      					= re.sub(_SCREAMING_SNAKE_CASE    ,	''''''    ,	batch['''sentence''']       ).lower() + ''' '''
								return batch

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= train_dataset.map(_SCREAMING_SNAKE_CASE    ,	remove_columns=['''sentence''']       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= eval_dataset.map(_SCREAMING_SNAKE_CASE    ,	remove_columns=['''sentence''']       )

				def extract_all_chars(_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
								SCREAMING_SNAKE_CASE  :						str      					= ''' '''.join(batch['''text''']       )
								SCREAMING_SNAKE_CASE  :						str      					= list(set(_SCREAMING_SNAKE_CASE       )       )
								return {"vocab": [vocab], "all_text": [all_text]}

				SCREAMING_SNAKE_CASE  :						List[Any]      					= train_dataset.map(
				    _SCREAMING_SNAKE_CASE    ,	batched=_SCREAMING_SNAKE_CASE    ,	batch_size=-1    ,	keep_in_memory=_SCREAMING_SNAKE_CASE    ,	remove_columns=train_dataset.column_names    ,	)
				SCREAMING_SNAKE_CASE  :						Tuple      					= train_dataset.map(
				    _SCREAMING_SNAKE_CASE    ,	batched=_SCREAMING_SNAKE_CASE    ,	batch_size=-1    ,	keep_in_memory=_SCREAMING_SNAKE_CASE    ,	remove_columns=eval_dataset.column_names    ,	)

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= list(set(vocab_train['''vocab'''][0]       ) | set(vocab_test['''vocab'''][0]       )       )
				SCREAMING_SNAKE_CASE  :						int      					= {v: k for k, v in enumerate(_SCREAMING_SNAKE_CASE       )}
				SCREAMING_SNAKE_CASE  :						int      					= vocab_dict[''' ''']
				del vocab_dict[" "]
				SCREAMING_SNAKE_CASE  :						str      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(_SCREAMING_SNAKE_CASE       )

				with open('''vocab.json'''    ,	'''w'''       ) as vocab_file:
								json.dump(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				SCREAMING_SNAKE_CASE  :						Any      					= WavaVecaCTCTokenizer(
				    '''vocab.json'''    ,	unk_token='''[UNK]'''    ,	pad_token='''[PAD]'''    ,	word_delimiter_token='''|'''    ,	)
				SCREAMING_SNAKE_CASE  :						Tuple      					= WavaVecaFeatureExtractor(
				    feature_size=1    ,	sampling_rate=1_60_00    ,	padding_value=0.0    ,	do_normalize=_SCREAMING_SNAKE_CASE    ,	return_attention_mask=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE    ,	tokenizer=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= WavaVecaForCTC.from_pretrained(
				    model_args.model_name_or_path    ,	cache_dir=model_args.cache_dir    ,	activation_dropout=model_args.activation_dropout    ,	attention_dropout=model_args.attention_dropout    ,	hidden_dropout=model_args.hidden_dropout    ,	feat_proj_dropout=model_args.feat_proj_dropout    ,	mask_time_prob=model_args.mask_time_prob    ,	gradient_checkpointing=training_args.gradient_checkpointing    ,	layerdrop=model_args.layerdrop    ,	ctc_loss_reduction='''mean'''    ,	pad_token_id=processor.tokenizer.pad_token_id    ,	vocab_size=len(processor.tokenizer       )    ,	)

				if data_args.max_train_samples is not None:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= min(len(_SCREAMING_SNAKE_CASE       )    ,	data_args.max_train_samples       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= train_dataset.select(range(_SCREAMING_SNAKE_CASE       )       )

				if data_args.max_val_samples is not None:
								SCREAMING_SNAKE_CASE  :						str      					= eval_dataset.select(range(data_args.max_val_samples       )       )

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torchaudio.transforms.Resample(4_80_00    ,	1_60_00       )

				# Preprocessing the datasets.
				# We need to read the aduio files as arrays and tokenize the targets.
				def speech_file_to_array_fn(_SCREAMING_SNAKE_CASE      :Tuple       ):
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Tuple      					= torchaudio.load(batch['''path''']       )
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= resampler(_SCREAMING_SNAKE_CASE       ).squeeze().numpy()
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 1_60_00
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= batch['''text''']
								return batch

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= train_dataset.map(
				    _SCREAMING_SNAKE_CASE    ,	remove_columns=train_dataset.column_names    ,	num_proc=data_args.preprocessing_num_workers    ,	)
				SCREAMING_SNAKE_CASE  :						List[Any]      					= eval_dataset.map(
				    _SCREAMING_SNAKE_CASE    ,	remove_columns=eval_dataset.column_names    ,	num_proc=data_args.preprocessing_num_workers    ,	)

				def prepare_dataset(_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
								# check that all files have the correct sampling rate
								assert (
								    len(set(batch['''sampling_rate''']       )       ) == 1
								), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

								SCREAMING_SNAKE_CASE  :						List[Any]      					= processor(
								    audio=batch['''speech''']    ,	text=batch['''target_text''']    ,	sampling_rate=batch['''sampling_rate'''][0]       )
								batch.update(_SCREAMING_SNAKE_CASE       )
								return batch

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= train_dataset.map(
				    _SCREAMING_SNAKE_CASE    ,	remove_columns=train_dataset.column_names    ,	batch_size=training_args.per_device_train_batch_size    ,	batched=_SCREAMING_SNAKE_CASE    ,	num_proc=data_args.preprocessing_num_workers    ,	)
				SCREAMING_SNAKE_CASE  :						Tuple      					= eval_dataset.map(
				    _SCREAMING_SNAKE_CASE    ,	remove_columns=eval_dataset.column_names    ,	batch_size=training_args.per_device_train_batch_size    ,	batched=_SCREAMING_SNAKE_CASE    ,	num_proc=data_args.preprocessing_num_workers    ,	)

				# Metric
				SCREAMING_SNAKE_CASE  :						List[str]      					= datasets.load_metric('''wer'''       )

				def compute_metrics(_SCREAMING_SNAKE_CASE      :str       ):
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pred.predictions
								SCREAMING_SNAKE_CASE  :						Any      					= np.argmax(_SCREAMING_SNAKE_CASE    ,	axis=-1       )

								SCREAMING_SNAKE_CASE  :						Any      					= processor.tokenizer.pad_token_id

								SCREAMING_SNAKE_CASE  :						Optional[int]      					= processor.batch_decode(_SCREAMING_SNAKE_CASE       )
								# we do not want to group tokens when computing the metrics
								SCREAMING_SNAKE_CASE  :						Any      					= processor.batch_decode(pred.label_ids    ,	group_tokens=_SCREAMING_SNAKE_CASE       )

								SCREAMING_SNAKE_CASE  :						Dict      					= wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE    ,	references=_SCREAMING_SNAKE_CASE       )

								return {"wer": wer}

				if model_args.freeze_feature_extractor:
								model.freeze_feature_extractor()

				# Data collator
				SCREAMING_SNAKE_CASE  :						int      					= DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE    ,	padding=_SCREAMING_SNAKE_CASE       )

				# Initialize our Trainer
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= CTCTrainer(
				    model=_SCREAMING_SNAKE_CASE    ,	data_collator=_SCREAMING_SNAKE_CASE    ,	args=_SCREAMING_SNAKE_CASE    ,	compute_metrics=_SCREAMING_SNAKE_CASE    ,	train_dataset=train_dataset if training_args.do_train else None    ,	eval_dataset=eval_dataset if training_args.do_eval else None    ,	tokenizer=processor.feature_extractor    ,	)

				# Training
				if training_args.do_train:
								if last_checkpoint is not None:
												SCREAMING_SNAKE_CASE  :						Any      					= last_checkpoint
								elif os.path.isdir(model_args.model_name_or_path       ):
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= model_args.model_name_or_path
								else:
												SCREAMING_SNAKE_CASE  :						List[str]      					= None

								# Save the feature_extractor and the tokenizer
								if is_main_process(training_args.local_rank       ):
												processor.save_pretrained(training_args.output_dir       )

								SCREAMING_SNAKE_CASE  :						Tuple      					= trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE       )
								trainer.save_model()

								SCREAMING_SNAKE_CASE  :						Dict      					= train_result.metrics
								SCREAMING_SNAKE_CASE  :						List[str]      					= (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(_SCREAMING_SNAKE_CASE       )
								)
								SCREAMING_SNAKE_CASE  :						int      					= min(_SCREAMING_SNAKE_CASE    ,	len(_SCREAMING_SNAKE_CASE       )       )

								trainer.log_metrics('''train'''    ,	_SCREAMING_SNAKE_CASE       )
								trainer.save_metrics('''train'''    ,	_SCREAMING_SNAKE_CASE       )
								trainer.save_state()

				# Evaluation
				SCREAMING_SNAKE_CASE  :						Any      					= {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= trainer.evaluate()
								SCREAMING_SNAKE_CASE  :						str      					= data_args.max_val_samples if data_args.max_val_samples is not None else len(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						str      					= min(_SCREAMING_SNAKE_CASE    ,	len(_SCREAMING_SNAKE_CASE       )       )

								trainer.log_metrics('''eval'''    ,	_SCREAMING_SNAKE_CASE       )
								trainer.save_metrics('''eval'''    ,	_SCREAMING_SNAKE_CASE       )

				return results


if __name__ == "__main__":
	main()









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




