


import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE       :		List[Any]         =				logging.getLogger(__name__)


SCREAMING_SNAKE_CASE       :		Optional[int]         =				list(MODEL_WITH_LM_HEAD_MAPPING.keys())
SCREAMING_SNAKE_CASE       :		List[str]         =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class      UpperCamelCase					:


   '''simple docstring'''







   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={
           """help""": (
               """The model checkpoint for weights initialization. Leave None if you want to train a model from"""
               """ scratch."""
           )
       }						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowercase__     )}						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,      )






@dataclass
class      UpperCamelCase					:


   '''simple docstring'''







   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """The input training data file (a text file)."""}     )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={
           """help""": (
               """The input training data files (multiple files in glob format). """
               """Very often splitting large files to smaller files can prevent tokenizer going out of memory"""
           )
       }						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """An optional input train ref data file for whole word mask in Chinese."""}						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """An optional input eval ref data file for whole word mask in Chinese."""}						,      )
   lowercase					:	bool									=field(
       default=lowercase__						,      metadata={"""help""": """Whether distinct lines of text in the dataset are to be handled as distinct sequences."""}						,      )

   lowercase					:	bool									=field(
       default=lowercase__						,      metadata={"""help""": """Train with masked-language modeling loss instead of language modeling."""}     )
   lowercase					:	bool									=field(default=lowercase__						,      metadata={"""help""": """Whether ot not to use whole word mask."""}     )
   lowercase					:	float									=field(
       default=0.15						,      metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}     )
   lowercase					:	float									=field(
       default=1 / 6						,      metadata={
           """help""": (
               """Ratio of length of a span of masked tokens to surrounding context length for permutation language"""
               """ modeling."""
           )
       }						,      )
   lowercase					:	int									=field(
       default=5						,      metadata={"""help""": """Maximum length of a span of masked tokens for permutation language modeling."""}     )

   lowercase					:	int									=field(
       default=-1						,      metadata={
           """help""": (
               """Optional input sequence length after tokenization."""
               """The training dataset will be truncated in block of this size for training."""
               """Default to the model max input length for single sentence inputs (take into account special tokens)."""
           )
       }						,      )
   lowercase					:	bool									=field(
       default=lowercase__						,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )




def     UpperCamelCase    (      _a       ,		_a       ,		_a = False       ,		_a = None       ,		)					->							Optional[Any]:
     '''simple docstring'''





     def _dataset(_a       ,		_a=None		):
          if args.line_by_line:
               if ref_path is not None:
                    if not args.whole_word_mask or not args.mlm:
                         raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''		)
                    return LineByLineWithRefDataset(
                        tokenizer=_a       ,		file_path=_a       ,		block_size=args.block_size       ,		ref_path=_a       ,		)

               return LineByLineTextDataset(tokenizer=_a       ,		file_path=_a       ,		block_size=args.block_size		)
          else:
               return TextDataset(
                   tokenizer=_a       ,		file_path=_a       ,		block_size=args.block_size       ,		overwrite_cache=args.overwrite_cache       ,		cache_dir=_a       ,		)

     if evaluate:
          return _dataset(args.eval_data_file       ,		args.eval_ref_file		)
     elif args.train_data_files:
          return ConcatDataset([_dataset(_a		) for f in glob(args.train_data_files		)]		)
     else:
          return _dataset(args.train_data_file       ,		args.train_ref_file		)




def     UpperCamelCase    (      )					->							List[str]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
     lowercase_       :Tuple     =       parser.parse_args_into_dataclasses()

     if data_args.eval_data_file is None and training_args.do_eval:
          raise ValueError(
              '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
              '''or remove the --do_eval argument.'''		)
     if (
         os.path.exists(training_args.output_dir		)
         and os.listdir(training_args.output_dir		)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
              ''' --overwrite_output_dir to overcome.'''		)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,		datefmt='''%m/%d/%Y %H:%M:%S'''       ,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,		)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,		training_args.local_rank       ,		training_args.device       ,		training_args.n_gpu       ,		bool(training_args.local_rank != -1		)       ,		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'''       ,		_a		)

     # Set seed
     set_seed(training_args.seed		)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     if model_args.config_name:
          lowercase_       :Any     =       AutoConfig.from_pretrained(model_args.config_name       ,		cache_dir=model_args.cache_dir		)
     elif model_args.model_name_or_path:
          lowercase_       :Union[str, Any]     =       AutoConfig.from_pretrained(model_args.model_name_or_path       ,		cache_dir=model_args.cache_dir		)
     else:
          lowercase_       :int     =       CONFIG_MAPPING[model_args.model_type]()
          logger.warning('''You are instantiating a new config instance from scratch.'''		)

     if model_args.tokenizer_name:
          lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(model_args.tokenizer_name       ,		cache_dir=model_args.cache_dir		)
     elif model_args.model_name_or_path:
          lowercase_       :Any     =       AutoTokenizer.from_pretrained(model_args.model_name_or_path       ,		cache_dir=model_args.cache_dir		)
     else:
          raise ValueError(
              '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
              ''' script, save it,and load it from here, using --tokenizer_name'''		)

     if model_args.model_name_or_path:
          lowercase_       :Optional[int]     =       AutoModelWithLMHead.from_pretrained(
              model_args.model_name_or_path       ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)       ,		config=_a       ,		cache_dir=model_args.cache_dir       ,		)
     else:
          logger.info('''Training new model from scratch'''		)
          lowercase_       :int     =       AutoModelWithLMHead.from_config(_a		)

     model.resize_token_embeddings(len(_a		)		)

     if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
          raise ValueError(
              '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
              '''--mlm flag (masked language modeling).'''		)

     if data_args.block_size <= 0:
          lowercase_       :Optional[int]     =       tokenizer.max_len
          # Our input block size will be the max possible for the model
     else:
          lowercase_       :Tuple     =       min(data_args.block_size       ,		tokenizer.max_len		)

     # Get datasets

     lowercase_       :Tuple     =       (
         get_dataset(_a       ,		tokenizer=_a       ,		cache_dir=model_args.cache_dir		) if training_args.do_train else None
     )
     lowercase_       :Optional[int]     =       (
         get_dataset(_a       ,		tokenizer=_a       ,		evaluate=_a       ,		cache_dir=model_args.cache_dir		)
         if training_args.do_eval
         else None
     )
     if config.model_type == "xlnet":
          lowercase_       :Any     =       DataCollatorForPermutationLanguageModeling(
              tokenizer=_a       ,		plm_probability=data_args.plm_probability       ,		max_span_length=data_args.max_span_length       ,		)
     else:
          if data_args.mlm and data_args.whole_word_mask:
               lowercase_       :List[Any]     =       DataCollatorForWholeWordMask(
                   tokenizer=_a       ,		mlm_probability=data_args.mlm_probability		)
          else:
               lowercase_       :Dict     =       DataCollatorForLanguageModeling(
                   tokenizer=_a       ,		mlm=data_args.mlm       ,		mlm_probability=data_args.mlm_probability		)

    # Initialize our Trainer
     lowercase_       :Union[str, Any]     =       Trainer(
         model=_a       ,		args=_a       ,		data_collator=_a       ,		train_dataset=_a       ,		eval_dataset=_a       ,		prediction_loss_only=_a       ,		)

     # Training
     if training_args.do_train:
          lowercase_       :Tuple     =       (
              model_args.model_name_or_path
              if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		)
              else None
          )
          trainer.train(model_path=_a		)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir		)

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

          lowercase_       :int     =       trainer.evaluate()

          lowercase_       :Union[str, Any]     =       math.exp(eval_output['''eval_loss''']		)
          lowercase_       :str     =       {'''perplexity''': perplexity}

          lowercase_       :Tuple     =       os.path.join(training_args.output_dir       ,		'''eval_results_lm.txt'''		)
          if trainer.is_world_master():
               with open(_a       ,		'''w'''		) as writer:
                    logger.info('''***** Eval results *****'''		)
                    for key in sorted(result.keys()		):
                         logger.info('''  %s = %s'''       ,		_a       ,		str(result[key]		)		)
                         writer.write('''%s = %s\n''' % (key, str(result[key]		))		)

          results.update(_a		)

     return results




def     UpperCamelCase    (      _a		)					->							Any:
     '''simple docstring'''





     main()


if __name__ == "__main__":
     main()





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def     UpperCamelCase    (      _a       ,		_a=0.999       ,		_a="cosine"       ,		)					->							Dict:
     '''simple docstring'''





     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_a		):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_a		):
               return math.exp(t * -12.0		)

     else:
          raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

     lowercase_       :Any     =       []
     for i in range(_a		):
          lowercase_       :List[str]     =       i / num_diffusion_timesteps
          lowercase_       :str     =       (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(_a		) / alpha_bar_fn(_a		)       ,		_a		)		)
     return torch.tensor(_a       ,		dtype=torch.floataa		)






class      UpperCamelCase					(		lowercase__						,      lowercase__     ):


   '''simple docstring'''







   lowercase					:	Tuple									=[e.name for e in KarrasDiffusionSchedulers]
   lowercase					:	Tuple									=2
   @register_to_config
   def __init__(     self   ,							UpperCamelCase_ = 1000   ,							UpperCamelCase_ = 0.0_0085   ,							UpperCamelCase_ = 0.012   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = "epsilon"   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = 1.0   ,							UpperCamelCase_ = "linspace"   ,							UpperCamelCase_ = 0   ,							):
        if trained_betas is not None:
             lowercase_       :int     =       torch.tensor(UpperCamelCase_   ,							dtype=torch.floataa		)
        elif beta_schedule == "linear":
             lowercase_       :List[str]     =       torch.linspace(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							dtype=torch.floataa		)
        elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             lowercase_       :int     =       (
                 torch.linspace(beta_start**0.5   ,							beta_end**0.5   ,							UpperCamelCase_   ,							dtype=torch.floataa		) ** 2
             )
        elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             lowercase_       :Optional[int]     =       betas_for_alpha_bar(UpperCamelCase_   ,							alpha_transform_type='''cosine'''		)
        elif beta_schedule == "exp":
             lowercase_       :Dict     =       betas_for_alpha_bar(UpperCamelCase_   ,							alpha_transform_type='''exp'''		)
        else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

        lowercase_       :str     =       1.0 - self.betas
        lowercase_       :Optional[int]     =       torch.cumprod(self.alphas   ,							dim=0		)

        #  set all values
        self.set_timesteps(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
        lowercase_       :str     =       use_karras_sigmas
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None		):
        if schedule_timesteps is None:
             lowercase_       :List[str]     =       self.timesteps

        lowercase_       :int     =       (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter		) == 0:
             lowercase_       :Dict     =       1 if len(UpperCamelCase_		) > 1 else 0
        else:
             lowercase_       :Any     =       timestep.cpu().item() if torch.is_tensor(UpperCamelCase_		) else timestep
             lowercase_       :Union[str, Any]     =       self._index_counter[timestep_int]

        return indices[pos].item()
   @property
   def 		UpperCamelCase  (     self		):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							):
        lowercase_       :List[str]     =       self.index_for_timestep(UpperCamelCase_		)

        lowercase_       :Optional[Any]     =       self.sigmas[step_index]
        lowercase_       :Union[str, Any]     =       sample / ((sigma**2 + 1) ** 0.5)
        return sample
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							):
        lowercase_       :Optional[Any]     =       num_inference_steps

        lowercase_       :Dict     =       num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
             lowercase_       :Union[str, Any]     =       np.linspace(0   ,							num_train_timesteps - 1   ,							UpperCamelCase_   ,							dtype=UpperCamelCase_		)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
             lowercase_       :Any     =       num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             lowercase_       :List[Any]     =       (np.arange(0   ,							UpperCamelCase_		) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_		)
             timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
             lowercase_       :Dict     =       num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             lowercase_       :Dict     =       (np.arange(UpperCamelCase_   ,							0   ,							-step_ratio		)).round().copy().astype(UpperCamelCase_		)
             timesteps -= 1
        else:
             raise ValueError(
                 f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

        lowercase_       :Optional[int]     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
        lowercase_       :Tuple     =       np.log(UpperCamelCase_		)
        lowercase_       :Dict     =       np.interp(UpperCamelCase_   ,							np.arange(0   ,							len(UpperCamelCase_		)		)   ,							UpperCamelCase_		)

        if self.config.use_karras_sigmas:
             lowercase_       :int     =       self._convert_to_karras(in_sigmas=UpperCamelCase_   ,							num_inference_steps=self.num_inference_steps		)
             lowercase_       :Optional[int]     =       np.array([self._sigma_to_t(UpperCamelCase_   ,							UpperCamelCase_		) for sigma in sigmas]		)

        lowercase_       :Tuple     =       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
        lowercase_       :Optional[int]     =       torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_		)
        lowercase_       :Tuple     =       torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

        lowercase_       :Any     =       torch.from_numpy(UpperCamelCase_		)
        lowercase_       :List[str]     =       torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

        if str(UpperCamelCase_		).startswith('''mps'''		):
             # mps does not support float64
             lowercase_       :int     =       timesteps.to(UpperCamelCase_   ,							dtype=torch.floataa		)
        else:
             lowercase_       :Optional[Any]     =       timesteps.to(device=UpperCamelCase_		)

        # empty dt and derivative
        lowercase_       :List[str]     =       None
        lowercase_       :List[Any]     =       None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        lowercase_       :int     =       defaultdict(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        # get log sigma
        lowercase_       :Union[str, Any]     =       np.log(UpperCamelCase_		)

        # get distribution
        lowercase_       :Optional[Any]     =       log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        lowercase_       :List[Any]     =       np.cumsum((dists >= 0)   ,							axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
        lowercase_       :str     =       low_idx + 1

        lowercase_       :Any     =       log_sigmas[low_idx]
        lowercase_       :int     =       log_sigmas[high_idx]

        # interpolate sigmas
        lowercase_       :Dict     =       (low - log_sigma) / (low - high)
        lowercase_       :str     =       np.clip(UpperCamelCase_   ,							0   ,							1		)

        # transform interpolation to time range
        lowercase_       :Dict     =       (1 - w) * low_idx + w * high_idx
        lowercase_       :int     =       t.reshape(sigma.shape		)
        return t
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :float     =       in_sigmas[-1].item()
        lowercase_       :float     =       in_sigmas[0].item()

        lowercase_       :int     =       7.0  # 7.0 is the value used in the paper
        lowercase_       :Optional[Any]     =       np.linspace(0   ,							1   ,							UpperCamelCase_		)
        lowercase_       :List[str]     =       sigma_min ** (1 / rho)
        lowercase_       :List[Any]     =       sigma_max ** (1 / rho)
        lowercase_       :Tuple     =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas
   @property
   def 		UpperCamelCase  (     self		):
        return self.dt is None
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_ = True   ,							):
        lowercase_       :Any     =       self.index_for_timestep(UpperCamelCase_		)

        # advance index counter by 1
        lowercase_       :Any     =       timestep.cpu().item() if torch.is_tensor(UpperCamelCase_		) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
             lowercase_       :Optional[int]     =       self.sigmas[step_index]
             lowercase_       :List[Any]     =       self.sigmas[step_index + 1]
        else:
             # 2nd order / Heun's method
             lowercase_       :Optional[int]     =       self.sigmas[step_index - 1]
             lowercase_       :Dict     =       self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        lowercase_       :List[Any]     =       0
        lowercase_       :List[str]     =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
             lowercase_       :Union[str, Any]     =       sigma_hat if self.state_in_first_order else sigma_next
             lowercase_       :List[Any]     =       sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
             lowercase_       :Dict     =       sigma_hat if self.state_in_first_order else sigma_next
             lowercase_       :Optional[Any]     =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
        elif self.config.prediction_type == "sample":
             lowercase_       :List[str]     =       model_output
        else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"		)

        if self.config.clip_sample:
             lowercase_       :str     =       pred_original_sample.clamp(
                 -self.config.clip_sample_range   ,							self.config.clip_sample_range		)

        if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             lowercase_       :Optional[int]     =       (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             lowercase_       :Optional[int]     =       sigma_next - sigma_hat

             # store for 2nd order step
             lowercase_       :str     =       derivative
             lowercase_       :Union[str, Any]     =       dt
             lowercase_       :Optional[int]     =       sample
        else:
             # 2. 2nd order / Heun's method
             lowercase_       :str     =       (sample - pred_original_sample) / sigma_next
             lowercase_       :List[str]     =       (self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             lowercase_       :Union[str, Any]     =       self.dt
             lowercase_       :Any     =       self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             lowercase_       :List[Any]     =       None
             lowercase_       :List[str]     =       None
             lowercase_       :Dict     =       None

        lowercase_       :int     =       sample + derivative * dt

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase_       :List[str]     =       self.sigmas.to(device=original_samples.device   ,							dtype=original_samples.dtype		)
        if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_		):
             # mps does not support float64
             lowercase_       :Optional[Any]     =       self.timesteps.to(original_samples.device   ,							dtype=torch.floataa		)
             lowercase_       :Tuple     =       timesteps.to(original_samples.device   ,							dtype=torch.floataa		)
        else:
             lowercase_       :Union[str, Any]     =       self.timesteps.to(original_samples.device		)
             lowercase_       :int     =       timesteps.to(original_samples.device		)

        lowercase_       :int     =       [self.index_for_timestep(UpperCamelCase_   ,							UpperCamelCase_		) for t in timesteps]

        lowercase_       :Tuple     =       sigmas[step_indices].flatten()
        while len(sigma.shape		) < len(original_samples.shape		):
             lowercase_       :List[str]     =       sigma.unsqueeze(-1		)

        lowercase_       :List[str]     =       original_samples + noise * sigma
        return noisy_samples




   def __len__(     self		):
        return self.config.num_train_timesteps

