





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase_  							=				logging.getLogger(__name__)
UpperCAmelCase_  							=				list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase_  							=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
           )
       }    ,       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowerCamelCase       )}    ,       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "Override some existing default config settings when a model is trained from scratch. Example: "
               "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
           )
       }    ,       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,       )
   a		:							str     	=	field(
       default="main"    ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
           raise ValueError(
               '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'''							)






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The name of the dataset to use (via the datasets library)."}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
   a		:							Optional[str]     	=	field(default=__lowerCamelCase    ,       metadata={"help": "The input training data file (a text file)."}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Overwrite the cached training and evaluation sets"}       )
   a		:							Optional[int]     	=	field(
       default=5    ,       metadata={
           "help": "The percentage of the train set used as validation set in case there's no validation split"
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated. Default to the max input length of the model."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The number of processes to use for the preprocessing."}    ,       )
   a		:							float     	=	field(
       default=0.1_5    ,       metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "Whether to pad all samples to `max_seq_length`. "
               "If False, will pad the samples dynamically when batching to the maximum length in the batch."
           )
       }    ,       )




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






       """simple docstring"""
       if self.train_file is not None:
           UpperCamelCase__							:       List[str]      =   self.train_file.split('''.'''							)[-1]
           assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
       if self.validation_file is not None:
           UpperCamelCase__							:       str      =   self.validation_file.split('''.'''							)[-1]
           assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Dict:
    with open(__UpperCAmelCase	,     '''r'''	,     encoding='''utf-8'''   ) as f:
        UpperCamelCase__							:       str      =   [json.loads(__UpperCAmelCase   ) for line in f.read().splitlines() if (len(__UpperCAmelCase   ) > 0 and not line.isspace())]
    assert len(__UpperCAmelCase   ) == len(__UpperCAmelCase   )

    UpperCamelCase__							:       Any      =   {c: dataset[c] for c in dataset.column_names}
    UpperCamelCase__							:       List[Any]      =   refs
    return Dataset.from_dict(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						)   ->			List[Any]:
    # 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.

    UpperCamelCase__							:       Optional[Any]      =   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.
        UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
    else:
        UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Any      =   parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    UpperCamelCase__							:       Tuple      =   None
    if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
        UpperCamelCase__							:       Dict      =   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()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('''Training/evaluation parameters %s'''	,     __UpperCAmelCase   )

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

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        UpperCamelCase__							:       Tuple      =   load_dataset(data_args.dataset_name	,     data_args.dataset_config_name   )
        if "validation" not in datasets.keys():
            UpperCamelCase__							:       Any      =   load_dataset(
                data_args.dataset_name	,     data_args.dataset_config_name	,     split=f"train[:{data_args.validation_split_percentage}%]"	,     )
            UpperCamelCase__							:       Optional[Any]      =   load_dataset(
                data_args.dataset_name	,     data_args.dataset_config_name	,     split=f"train[{data_args.validation_split_percentage}%:]"	,     )
    else:
        UpperCamelCase__							:       Dict      =   {}
        if data_args.train_file is not None:
            UpperCamelCase__							:       List[Any]      =   data_args.train_file
        if data_args.validation_file is not None:
            UpperCamelCase__							:       Any      =   data_args.validation_file
        UpperCamelCase__							:       List[str]      =   data_args.train_file.split('''.'''   )[-1]
        if extension == "txt":
            UpperCamelCase__							:       List[Any]      =   '''text'''
        UpperCamelCase__							:       List[Any]      =   load_dataset(__UpperCAmelCase	,     data_files=__UpperCAmelCase   )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    UpperCamelCase__							:       int      =   {
        '''cache_dir''': model_args.cache_dir,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        UpperCamelCase__							:       List[str]      =   AutoConfig.from_pretrained(model_args.config_name	,     **__UpperCAmelCase   )
    elif model_args.model_name_or_path:
        UpperCamelCase__							:       Dict      =   AutoConfig.from_pretrained(model_args.model_name_or_path	,     **__UpperCAmelCase   )
    else:
        UpperCamelCase__							:       Dict      =   CONFIG_MAPPING[model_args.model_type]()
        logger.warning('''You are instantiating a new config instance from scratch.'''   )
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}"   )
            config.update_from_string(model_args.config_overrides   )
            logger.info(f"New config: {config}"   )

    UpperCamelCase__							:       List[str]      =   {
        '''cache_dir''': model_args.cache_dir,
        '''use_fast''': model_args.use_fast_tokenizer,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.tokenizer_name:
        UpperCamelCase__							:       Union[str, Any]      =   AutoTokenizer.from_pretrained(model_args.tokenizer_name	,     **__UpperCAmelCase   )
    elif model_args.model_name_or_path:
        UpperCamelCase__							:       List[Any]      =   AutoTokenizer.from_pretrained(model_args.model_name_or_path	,     **__UpperCAmelCase   )
    else:
        raise ValueError(
            '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
            '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''   )

    if model_args.model_name_or_path:
        UpperCamelCase__							:       Optional[Any]      =   AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path	,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path   )	,     config=__UpperCAmelCase	,     cache_dir=model_args.cache_dir	,     revision=model_args.model_revision	,     use_auth_token=True if model_args.use_auth_token else None	,     )
    else:
        logger.info('''Training new model from scratch'''   )
        UpperCamelCase__							:       Dict      =   AutoModelForMaskedLM.from_config(__UpperCAmelCase   )

    model.resize_token_embeddings(len(__UpperCAmelCase   )   )

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        UpperCamelCase__							:       int      =   datasets['''train'''].column_names
    else:
        UpperCamelCase__							:       Optional[int]      =   datasets['''validation'''].column_names
    UpperCamelCase__							:       List[Any]      =   '''text''' if '''text''' in column_names else column_names[0]

    UpperCamelCase__							:       str      =   '''max_length''' if data_args.pad_to_max_length else False

    def tokenize_function(__UpperCAmelCase:    Dict   ):
        # Remove empty lines
        UpperCamelCase__							:       List[str]      =   [line for line in examples['''text'''] if len(__UpperCAmelCase   ) > 0 and not line.isspace()]
        return tokenizer(examples['''text''']	,     padding=__UpperCAmelCase	,     truncation=__UpperCAmelCase	,     max_length=data_args.max_seq_length   )

    UpperCamelCase__							:       Union[str, Any]      =   datasets.map(
        __UpperCAmelCase	,     batched=__UpperCAmelCase	,     num_proc=data_args.preprocessing_num_workers	,     remove_columns=[text_column_name]	,     load_from_cache_file=not data_args.overwrite_cache	,     )

    # Add the chinese references if provided
    if data_args.train_ref_file is not None:
        UpperCamelCase__							:       Union[str, Any]      =   add_chinese_references(tokenized_datasets['''train''']	,     data_args.train_ref_file   )
    if data_args.validation_ref_file is not None:
        UpperCamelCase__							:       Optional[Any]      =   add_chinese_references(
            tokenized_datasets['''validation''']	,     data_args.validation_ref_file   )
    # If we have ref files, need to avoid it removed by trainer
    UpperCamelCase__							:       Any      =   data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        UpperCamelCase__							:       Any      =   False

    # Data collator
    # This one will take care of randomly masking the tokens.
    UpperCamelCase__							:       List[str]      =   DataCollatorForWholeWordMask(tokenizer=__UpperCAmelCase	,     mlm_probability=data_args.mlm_probability   )

    # Initialize our Trainer
    UpperCamelCase__							:       List[Any]      =   Trainer(
        model=__UpperCAmelCase	,     args=__UpperCAmelCase	,     train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None	,     eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None	,     tokenizer=__UpperCAmelCase	,     data_collator=__UpperCAmelCase	,     )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            UpperCamelCase__							:       Optional[int]      =   last_checkpoint
        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path   ):
            UpperCamelCase__							:       Optional[int]      =   model_args.model_name_or_path
        else:
            UpperCamelCase__							:       Any      =   None
        UpperCamelCase__							:       Dict      =   trainer.train(resume_from_checkpoint=__UpperCAmelCase   )
        trainer.save_model()  # Saves the tokenizer too for easy upload

        UpperCamelCase__							:       Optional[int]      =   os.path.join(training_args.output_dir	,     '''train_results.txt'''   )
        if trainer.is_world_process_zero():
            with open(__UpperCAmelCase	,     '''w'''   ) as writer:
                logger.info('''***** Train results *****'''   )
                for key, value in sorted(train_result.metrics.items()   ):
                    logger.info(f"  {key} = {value}"   )
                    writer.write(f"{key} = {value}\n"   )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
            trainer.state.save_to_json(os.path.join(training_args.output_dir	,     '''trainer_state.json'''   )   )

    # Evaluation
    UpperCamelCase__							:       Tuple      =   {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''   )

        UpperCamelCase__							:       List[Any]      =   trainer.evaluate()

        UpperCamelCase__							:       List[Any]      =   math.exp(eval_output['''eval_loss''']   )
        UpperCamelCase__							:       Any      =   perplexity

        UpperCamelCase__							:       str      =   os.path.join(training_args.output_dir	,     '''eval_results_mlm_wwm.txt'''   )
        if trainer.is_world_process_zero():
            with open(__UpperCAmelCase	,     '''w'''   ) as writer:
                logger.info('''***** Eval results *****'''   )
                for key, value in sorted(results.items()   ):
                    logger.info(f"  {key} = {value}"   )
                    writer.write(f"{key} = {value}\n"   )

    return results




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]   )   ->			Optional[int]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




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






       """simple docstring"""
       return self.config.num_train_timesteps

