
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_a   :      Dict       =				logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_a   :      Dict       =				{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_a   :      List[str]       =				{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_a   :      Optional[int]       =				sorted(arg_to_scheduler.keys())
_a   :      Tuple       =				"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class   _UpperCAmelCase					(       pl.LightningModule):
    def __init__(     self    ,     snake_case_    ,     snake_case_=None    ,     snake_case_="base"    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_    ,     ):
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(snake_case_  )
      _snake_case  :    str            = 0
      _snake_case  :    Tuple            = Path(self.hparams.output_dir  )
      _snake_case  :    Optional[int]            = self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        _snake_case  :    List[str]            = AutoConfig.from_pretrained(
            self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,     **({"num_labels": num_labels} if num_labels is not None else {})    ,     cache_dir=snake_case_    ,     **snake_case_    ,     )
      else:
        _snake_case  :    PretrainedConfig            = config

      _snake_case  :    List[Any]            = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
      for p in extra_model_params:
        if getattr(self.hparams    ,     snake_case_    ,     snake_case_  ):
          assert hasattr(self.config    ,     snake_case_  ), F'model config doesn\'t have a `{p}` attribute'
          setattr(self.config    ,     snake_case_    ,     getattr(self.hparams    ,     snake_case_  )  )

      if tokenizer is None:
        _snake_case  :    Tuple            = AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,     cache_dir=snake_case_    ,     )
      else:
        _snake_case  :    PreTrainedTokenizer            = tokenizer
      _snake_case  :    List[Any]            = MODEL_MODES[mode]
      if model is None:
        _snake_case  :    int            = self.model_type.from_pretrained(
            self.hparams.model_name_or_path    ,     from_tf=bool(".ckpt" in self.hparams.model_name_or_path  )    ,     config=self.config    ,     cache_dir=snake_case_    ,     )
      else:
        _snake_case  :    Optional[int]            = model
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    List[Any]            = self.model_type.from_pretrained(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = arg_to_scheduler[self.hparams.lr_scheduler]
      _snake_case  :    Union[str, Any]            = get_schedule_func(
          self.opt    ,     num_warmup_steps=self.hparams.warmup_steps    ,     num_training_steps=self.total_steps()  )
      _snake_case  :    Union[str, Any]            = {"scheduler": scheduler, "interval": "step", "frequency": 1}
      return scheduler
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.model
      _snake_case  :    List[Any]            = ["bias", "LayerNorm.weight"]
      _snake_case  :    Optional[Any]            = [
          {
              "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )],
              "weight_decay": self.hparams.weight_decay,
          },
          {
              "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
              "weight_decay": 0.0,
          },
      ]
      if self.hparams.adafactor:
        _snake_case  :    int            = Adafactor(
            snake_case_    ,     lr=self.hparams.learning_rate    ,     scale_parameter=snake_case_    ,     relative_step=snake_case_  )

      else:
        _snake_case  :    Tuple            = AdamW(
            snake_case_    ,     lr=self.hparams.learning_rate    ,     eps=self.hparams.adam_epsilon  )
      _snake_case  :    Any            = optimizer

      _snake_case  :    List[str]            = self.get_lr_scheduler()

      return [optimizer], [scheduler]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      return self.validation_step(snake_case_    ,     snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return self.validation_end(snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = max(1    ,     self.hparams.gpus  )  # TODO: consider num_tpu_cores
      _snake_case  :    List[str]            = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      if stage == "test":
        _snake_case  :    int            = len(self.test_dataloader().dataset  )
      else:
        _snake_case  :    Tuple            = self.get_dataloader("train"    ,     self.hparams.train_batch_size    ,     shuffle=snake_case_  )
        _snake_case  :    Union[str, Any]            = len(self.train_dataloader().dataset  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = False  ):
      raise NotImplementedError("You must implement this for your task"  )
    def        lowerCamelCase__						(     self  ):
      return self.train_loader
    def        lowerCamelCase__						(     self  ):
      return self.get_dataloader("dev"    ,     self.hparams.eval_batch_size    ,     shuffle=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      return self.get_dataloader("test"    ,     self.hparams.eval_batch_size    ,     shuffle=snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return os.path.join(
          self.hparams.data_dir    ,     "cached_{}_{}_{}".format(
              snake_case_    ,     list(filter(snake_case_    ,     self.hparams.model_name_or_path.split("/"  )  )  ).pop()    ,     str(self.hparams.max_seq_length  )    ,     )    ,     )
    @pl.utilities.rank_zero_only
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = self.output_dir.joinpath("best_tfmr"  )
      _snake_case  :    int            = self.step_count
      self.model.save_pretrained(snake_case_  )
      self.tokenizer.save_pretrained(snake_case_  )


    @staticmethod
    def        lowerCamelCase__						(     snake_case_    ,     snake_case_  ):
      parser.add_argument(
          "--model_name_or_path"    ,     default=snake_case_    ,     type=snake_case_    ,     required=snake_case_    ,     help="Path to pretrained model or model identifier from huggingface.co/models"    ,     )
      parser.add_argument(
          "--config_name"    ,     default=""    ,     type=snake_case_    ,     help="Pretrained config name or path if not the same as model_name"  )
      parser.add_argument(
          "--tokenizer_name"    ,     default=snake_case_    ,     type=snake_case_    ,     help="Pretrained tokenizer name or path if not the same as model_name"    ,     )
      parser.add_argument(
          "--cache_dir"    ,     default=""    ,     type=snake_case_    ,     help="Where do you want to store the pre-trained models downloaded from huggingface.co"    ,     )
      parser.add_argument(
          "--encoder_layerdrop"    ,     type=snake_case_    ,     help="Encoder layer dropout probability (Optional). Goes into model.config"    ,     )
      parser.add_argument(
          "--decoder_layerdrop"    ,     type=snake_case_    ,     help="Decoder layer dropout probability (Optional). Goes into model.config"    ,     )
      parser.add_argument(
          "--dropout"    ,     type=snake_case_    ,     help="Dropout probability (Optional). Goes into model.config"    ,     )
      parser.add_argument(
          "--attention_dropout"    ,     type=snake_case_    ,     help="Attention dropout probability (Optional). Goes into model.config"    ,     )
      parser.add_argument("--learning_rate"    ,     default=5E-5    ,     type=snake_case_    ,     help="The initial learning rate for Adam."  )
      parser.add_argument(
          "--lr_scheduler"    ,     default="linear"    ,     choices=snake_case_    ,     metavar=snake_case_    ,     type=snake_case_    ,     help="Learning rate scheduler"    ,     )
      parser.add_argument("--weight_decay"    ,     default=0.0    ,     type=snake_case_    ,     help="Weight decay if we apply some."  )
      parser.add_argument("--adam_epsilon"    ,     default=1E-8    ,     type=snake_case_    ,     help="Epsilon for Adam optimizer."  )
      parser.add_argument("--warmup_steps"    ,     default=0    ,     type=snake_case_    ,     help="Linear warmup over warmup_steps."  )
      parser.add_argument("--num_workers"    ,     default=4    ,     type=snake_case_    ,     help="kwarg passed to DataLoader"  )
      parser.add_argument("--num_train_epochs"    ,     dest="max_epochs"    ,     default=3    ,     type=snake_case_  )
      parser.add_argument("--train_batch_size"    ,     default=32    ,     type=snake_case_  )
      parser.add_argument("--eval_batch_size"    ,     default=32    ,     type=snake_case_  )
      parser.add_argument("--adafactor"    ,     action="store_true"  )






class   _UpperCAmelCase					(       pl.Callback):
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
        pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class   _UpperCAmelCase					(       pl.Callback):
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[int]            = trainer.lr_schedulers[0]["scheduler"]
      _snake_case  :    int            = {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
      pl_module.logger.log_metrics(snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      rank_zero_info("***** Validation results *****"  )
      _snake_case  :    Union[str, Any]            = trainer.callback_metrics
      # Log results
      for key in sorted(snake_case_  ):
        if key not in ["log", "progress_bar"]:
          rank_zero_info("{} = {}\n".format(snake_case_    ,     str(metrics[key]  )  )  )


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      rank_zero_info("***** Test results *****"  )
      _snake_case  :    List[Any]            = trainer.callback_metrics
      # Log and save results to file
      _snake_case  :    Optional[int]            = os.path.join(pl_module.hparams.output_dir    ,     "test_results.txt"  )
      with open(snake_case_    ,     "w"  ) as writer:
        for key in sorted(snake_case_  ):
          if key not in ["log", "progress_bar"]:
            rank_zero_info("{} = {}\n".format(snake_case_    ,     str(metrics[key]  )  )  )
            writer.write("{} = {}\n".format(snake_case_    ,     str(metrics[key]  )  )  )


def 					a__ ( a :					Optional[Any]							,  a :					Tuple		):

  """simple docstring"""







  parser.add_argument(
      "--output_dir"							,  default=a							,  type=a							,  required=a							,  help="The output directory where the model predictions and checkpoints will be written."							,  )
  parser.add_argument(
      "--fp16"							,  action="store_true"							,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"							,  )

  parser.add_argument(
      "--fp16_opt_level"							,  type=a							,  default="O2"							,  help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )							,  )
  parser.add_argument("--n_tpu_cores"							,  dest="tpu_cores"							,  type=a		)
  parser.add_argument("--max_grad_norm"							,  dest="gradient_clip_val"							,  default=1.0							,  type=a							,  help="Max gradient norm"		)
  parser.add_argument("--do_train"							,  action="store_true"							,  help="Whether to run training."		)
  parser.add_argument("--do_predict"							,  action="store_true"							,  help="Whether to run predictions on the test set."		)
  parser.add_argument(
      "--gradient_accumulation_steps"							,  dest="accumulate_grad_batches"							,  type=a							,  default=1							,  help="Number of updates steps to accumulate before performing a backward/update pass."							,  )
  parser.add_argument("--seed"							,  type=a							,  default=42							,  help="random seed for initialization"		)
  parser.add_argument(
      "--data_dir"							,  default=a							,  type=a							,  required=a							,  help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."							,  )



def 					a__ ( a :					BaseTransformer							,  a :					argparse.Namespace							,  a :					str=None							,  a :					Tuple=True							,  a :					Optional[Any]=None							,  a :					Dict=[]							,  a :					str=None							,  a :					int=None							,  **a :					Optional[int]							,  ):

  """simple docstring"""







  pl.seed_everything(args.seed		)

  # init model
  _snake_case  :    str            = Path(model.hparams.output_dir		)
  odir.mkdir(exist_ok=a		)

  # add custom checkpoints
  if checkpoint_callback is None:
    _snake_case  :    str            = pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir							,  prefix="checkpoint"							,  monitor="val_loss"							,  mode="min"							,  save_top_k=1		)
  if early_stopping_callback:
    extra_callbacks.append(a		)
  if logging_callback is None:
    _snake_case  :    Optional[int]            = LoggingCallback()

  _snake_case  :    List[Any]            = {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    _snake_case  :    Union[str, Any]            = 16
    # train_params["amp_level"] = args.fp16_opt_level

  if args.gpus > 1:
    _snake_case  :    Dict            = "auto"  # "ddp"
    _snake_case  :    str            = "ddp"

  _snake_case  :    Optional[int]            = args.accumulate_grad_batches
  _snake_case  :    Tuple            = None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  _snake_case  :    Union[str, Any]            = "auto"

  _snake_case  :    Dict            = pl.Trainer.from_argparse_args(
      a							,  weights_summary=a							,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]							,  logger=a							,  **a							,  )

  if args.do_train:
    trainer.fit(a		)

  return trainer








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

import numpy as np
import torch

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


lowercase     :			Optional[int]     						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class 			__A(    __UpperCAmelCase			):
     __A							=							42






class 			__A(    __UpperCAmelCase    ,      __UpperCAmelCase			):
     __A							=							1

     @register_to_config
     def __init__(   self,							A = 40,							A = 0.002,							A = 80.0,							A = 0.5,							A = 1.0,							A = 7.0,							A = True,							):





            """simple docstring"""

            _UpperCamelCase														=    sigma_max

            _UpperCamelCase														=    np.linspace(0,							1,							A   )
            _UpperCamelCase														=    self._convert_to_karras(A   )
            _UpperCamelCase														=    self.sigma_to_t(A   )

            # setable values
            _UpperCamelCase														=    None
            _UpperCamelCase														=    torch.from_numpy(A   )
            _UpperCamelCase														=    torch.from_numpy(A   )
            _UpperCamelCase														=    False
            _UpperCamelCase														=    False

     def 						_UpperCamelCase  (   self,							A,							A=None   ):





            """simple docstring"""

            if schedule_timesteps is None:
                   _UpperCamelCase														=    self.timesteps

            _UpperCamelCase														=    (schedule_timesteps == timestep).nonzero()
            return indices.item()

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            if isinstance(A,							torch.Tensor   ):
                   _UpperCamelCase														=    timestep.to(self.timesteps.device   )
            _UpperCamelCase														=    self.index_for_timestep(A   )
            _UpperCamelCase														=    self.sigmas[step_idx]

            _UpperCamelCase														=    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

            _UpperCamelCase														=    True
            return sample

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            if not isinstance(A,							np.ndarray   ):
                   _UpperCamelCase														=    np.array(A,							dtype=np.floataa   )

            _UpperCamelCase														=    1000 * 0.25 * np.log(sigmas + 1E-4_4   )

            return timesteps

     def 						_UpperCamelCase  (   self,							A = None,							A = None,							A = None,							):





            """simple docstring"""

            if num_inference_steps is None and timesteps is None:
                   raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''   )

            if num_inference_steps is not None and timesteps is not None:
                   raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''   )

            # Follow DDPMScheduler custom timesteps logic
            if timesteps is not None:
                   for i in range(1,							len(A   )   ):
                          if timesteps[i] >= timesteps[i - 1]:
                                 raise ValueError('''`timesteps` must be in descending order.'''   )

                   if timesteps[0] >= self.config.num_train_timesteps:
                          raise ValueError(
                              F'''`timesteps` must start before `self.config.train_timesteps`:'''
                              F''' {self.config.num_train_timesteps}.'''   )

                   _UpperCamelCase														=    np.array(A,							dtype=np.intaa   )
                   _UpperCamelCase														=    True
            else:
                   if num_inference_steps > self.config.num_train_timesteps:
                          raise ValueError(
                              F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                              F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                              F''' maximal {self.config.num_train_timesteps} timesteps.'''   )

                   _UpperCamelCase														=    num_inference_steps

                   _UpperCamelCase														=    self.config.num_train_timesteps // self.num_inference_steps
                   _UpperCamelCase														=    (np.arange(0,							A   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
                   _UpperCamelCase														=    False

            # Map timesteps to Karras sigmas directly for multistep sampling
            # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
            _UpperCamelCase														=    self.config.num_train_timesteps
            _UpperCamelCase														=    timesteps[::-1].copy()
            _UpperCamelCase														=    ramp / (num_train_timesteps - 1)
            _UpperCamelCase														=    self._convert_to_karras(A   )
            _UpperCamelCase														=    self.sigma_to_t(A   )

            _UpperCamelCase														=    np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
            _UpperCamelCase														=    torch.from_numpy(A   ).to(device=A   )

            if str(A   ).startswith('''mps'''   ):
                   # mps does not support float64
                   _UpperCamelCase														=    torch.from_numpy(A   ).to(A,							dtype=torch.floataa   )
            else:
                   _UpperCamelCase														=    torch.from_numpy(A   ).to(device=A   )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config.sigma_min
            _UpperCamelCase														=    self.config.sigma_max

            _UpperCamelCase														=    self.config.rho
            _UpperCamelCase														=    sigma_min ** (1 / rho)
            _UpperCamelCase														=    sigma_max ** (1 / rho)
            _UpperCamelCase														=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
            return sigmas

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config.sigma_data

            _UpperCamelCase														=    sigma_data**2 / (sigma**2 + sigma_data**2)
            _UpperCamelCase														=    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
            return c_skip, c_out

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config.sigma_min
            _UpperCamelCase														=    self.config.sigma_data

            _UpperCamelCase														=    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
            _UpperCamelCase														=    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
            return c_skip, c_out

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A = None,							A = True,							):





            """simple docstring"""

            if (
                isinstance(A,							A   )
                or isinstance(A,							torch.IntTensor   )
                or isinstance(A,							torch.LongTensor   )
            ):
                   raise ValueError(
                       (
                           '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                           F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                           ''' one of the `scheduler.timesteps` as a timestep.'''
                       ),							)

            if not self.is_scale_input_called:
                   logger.warning(
                       '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                       '''See `StableDiffusionPipeline` for a usage example.'''   )

            if isinstance(A,							torch.Tensor   ):
                   _UpperCamelCase														=    timestep.to(self.timesteps.device   )

            _UpperCamelCase														=    self.config.sigma_min
            _UpperCamelCase														=    self.config.sigma_max

            _UpperCamelCase														=    self.index_for_timestep(A   )

            # sigma_next corresponds to next_t in original implementation
            _UpperCamelCase														=    self.sigmas[step_index]
            if step_index + 1 < self.config.num_train_timesteps:
                   _UpperCamelCase														=    self.sigmas[step_index + 1]
            else:
                   # Set sigma_next to sigma_min
                   _UpperCamelCase														=    self.sigmas[-1]

            # Get scalings for boundary conditions
            _UpperCamelCase							,		_UpperCamelCase														=    self.get_scalings_for_boundary_condition(A   )

            # 1. Denoise model output using boundary conditions
            _UpperCamelCase														=    c_out * model_output + c_skip * sample
            if self.config.clip_denoised:
                   _UpperCamelCase														=    denoised.clamp(-1,							1   )

            # 2. Sample z ~ N(0, s_noise^2 * I)
            # Noise is not used for onestep sampling.
            if len(self.timesteps   ) > 1:
                   _UpperCamelCase														=    randn_tensor(
                       model_output.shape,							dtype=model_output.dtype,							device=model_output.device,							generator=A   )
            else:
                   _UpperCamelCase														=    torch.zeros_like(A   )
            _UpperCamelCase														=    noise * self.config.s_noise

            _UpperCamelCase														=    sigma_next.clamp(min=A,							max=A   )

            # 3. Return noisy sample
            # tau = sigma_hat, eps = sigma_min
            _UpperCamelCase														=    denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

            if not return_dict:
                   return (prev_sample,)

            return CMStochasticIterativeSchedulerOutput(prev_sample=A   )

     def 						_UpperCamelCase  (   self,							A,							A,							A,							):





            """simple docstring"""

            _UpperCamelCase														=    self.sigmas.to(device=original_samples.device,							dtype=original_samples.dtype   )
            if original_samples.device.type == "mps" and torch.is_floating_point(A   ):
                   # mps does not support float64
                   _UpperCamelCase														=    self.timesteps.to(original_samples.device,							dtype=torch.floataa   )
                   _UpperCamelCase														=    timesteps.to(original_samples.device,							dtype=torch.floataa   )
            else:
                   _UpperCamelCase														=    self.timesteps.to(original_samples.device   )
                   _UpperCamelCase														=    timesteps.to(original_samples.device   )

            _UpperCamelCase														=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

            _UpperCamelCase														=    sigmas[step_indices].flatten()
            while len(sigma.shape   ) < len(original_samples.shape   ):
                   _UpperCamelCase														=    sigma.unsqueeze(-1   )

            _UpperCamelCase														=    original_samples + noise * sigma
            return noisy_samples



     def __len__(   self   ):





            """simple docstring"""

            return self.config.num_train_timesteps






