


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE       :		List[str]         =				16
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				32




def     UpperCamelCase    (      _a       ,		_a = 1_6       ,		_a = "bert-base-cased"		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       AutoTokenizer.from_pretrained(_a		)
     lowercase_       :Tuple     =       load_dataset('''glue'''       ,		'''mrpc'''		)

     def tokenize_function(_a		):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_       :Union[str, Any]     =       tokenizer(examples['''sentence1''']       ,		examples['''sentence2''']       ,		truncation=_a       ,		max_length=_a		)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     lowercase_       :str     =       datasets.map(
         _a       ,		batched=_a       ,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,		load_from_cache_file=_a		)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowercase_       :List[Any]     =       tokenized_datasets.rename_column('''label'''       ,		'''labels'''		)

     def collate_fn(_a		):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_a       ,		padding='''max_length'''       ,		max_length=1_2_8       ,		return_tensors='''pt'''		)
          return tokenizer.pad(_a       ,		padding='''longest'''       ,		return_tensors='''pt'''		)

     # Instantiate dataloaders.
     lowercase_       :List[str]     =       DataLoader(
         tokenized_datasets['''train''']       ,		shuffle=_a       ,		collate_fn=_a       ,		batch_size=_a		)
     lowercase_       :Dict     =       DataLoader(
         tokenized_datasets['''validation''']       ,		shuffle=_a       ,		collate_fn=_a       ,		batch_size=_a		)

     return train_dataloader, eval_dataloader




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





     model.eval()
     lowercase_       :Union[str, Any]     =       0
     for step, batch in enumerate(_a		):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device		)
          with torch.no_grad():
               lowercase_       :Union[str, Any]     =       model(**_a		)
          lowercase_       :Optional[int]     =       outputs.logits.argmax(dim=-1		)
          # It is slightly faster to call this once, than multiple times
          lowercase_       :Tuple     =       accelerator.gather(
              (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(_a		) - 1:
                    lowercase_       :int     =       predictions[: len(eval_dataloader.dataset		) - samples_seen]
                    lowercase_       :List[str]     =       references[: len(eval_dataloader.dataset		) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=_a       ,		references=_a       ,		)

     lowercase_       :str     =       metric.compute()
     return eval_metric["accuracy"]




def     UpperCamelCase    (      _a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_       :List[str]     =       config["""lr"""]
     lowercase_       :Optional[int]     =       int(config['''num_epochs''']		)
     lowercase_       :Optional[Any]     =       int(config['''seed''']		)
     lowercase_       :str     =       int(config['''batch_size''']		)
     lowercase_       :Any     =       args.model_name_or_path

     set_seed(_a		)
     lowercase_       :Optional[Any]     =       get_dataloaders(_a       ,		_a       ,		_a		)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_       :str     =       AutoModelForSequenceClassification.from_pretrained(_a       ,		return_dict=_a		)

     # Instantiate optimizer
     lowercase_       :Union[str, Any]     =       (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase_       :Any     =       optimizer_cls(params=model.parameters()       ,		lr=_a		)

     if accelerator.state.deepspeed_plugin is not None:
          lowercase_       :List[str]     =       accelerator.state.deepspeed_plugin.deepspeed_config[
              """gradient_accumulation_steps"""
          ]
     else:
          lowercase_       :Dict     =       1
     lowercase_       :Optional[int]     =       (len(_a		) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          lowercase_       :Any     =       get_linear_schedule_with_warmup(
              optimizer=_a       ,		num_warmup_steps=0       ,		num_training_steps=_a       ,		)
     else:
          lowercase_       :Optional[Any]     =       DummyScheduler(_a       ,		total_num_steps=_a       ,		warmup_num_steps=0		)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     lowercase_       :Optional[Any]     =       accelerator.prepare(
         _a       ,		_a       ,		_a       ,		_a       ,		_a		)

     # We need to keep track of how many total steps we have iterated over
     lowercase_       :List[str]     =       0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_       :Dict     =       0
     lowercase_       :Optional[Any]     =       evaluate.load('''glue'''       ,		'''mrpc'''		)
     lowercase_       :str     =       num_epochs

     if args.partial_train_epoch is not None:
          lowercase_       :Optional[Any]     =       args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint		)
          lowercase_       :List[str]     =       args.resume_from_checkpoint.split('''epoch_'''		)[1]
          lowercase_       :Optional[Any]     =       """"""
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          lowercase_       :Optional[Any]     =       int(_a		) + 1
          lowercase_       :Dict     =       evaluation_loop(_a       ,		_a       ,		_a       ,		_a		)
          accelerator.print('''resumed checkpoint performance:'''       ,		_a		)
          accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''       ,		lr_scheduler.get_lr()[0]		)
          accelerator.print('''resumed optimizers\'s lr:'''       ,		optimizer.param_groups[0]['''lr''']		)
          with open(os.path.join(args.output_dir       ,		f"state_{starting_epoch-1}.json"		)       ,		'''r'''		) as f:
               lowercase_       :Union[str, Any]     =       json.load(_a		)
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     lowercase_       :Tuple     =       {}
     for epoch in range(_a       ,		_a		):
          model.train()
          for step, batch in enumerate(_a		):
               lowercase_       :Dict     =       model(**_a		)
               lowercase_       :str     =       outputs.loss
               lowercase_       :List[str]     =       loss / gradient_accumulation_steps
               accelerator.backward(_a		)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          lowercase_       :List[str]     =       f"epoch_{epoch}"
          lowercase_       :Optional[Any]     =       os.path.join(args.output_dir       ,		_a		)
          accelerator.save_state(_a		)
          lowercase_       :List[Any]     =       evaluation_loop(_a       ,		_a       ,		_a       ,		_a		)
          lowercase_       :str     =       accuracy
          lowercase_       :List[Any]     =       lr_scheduler.get_lr()[0]
          lowercase_       :int     =       optimizer.param_groups[0]["""lr"""]
          lowercase_       :Any     =       epoch
          lowercase_       :Optional[Any]     =       overall_step
          accelerator.print(f"epoch {epoch}:"       ,		_a		)

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir       ,		f"state_{epoch}.json"		)       ,		'''w'''		) as f:
                    json.dump(_a       ,		_a		)




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





     lowercase_       :List[Any]     =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
     parser.add_argument(
         '''--model_name_or_path'''       ,		type=_a       ,		default='''bert-base-cased'''       ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,		required=_a       ,		)
     parser.add_argument(
         '''--output_dir'''       ,		type=_a       ,		default='''.'''       ,		help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,		)
     parser.add_argument(
         '''--resume_from_checkpoint'''       ,		type=_a       ,		default=_a       ,		help='''If the training should continue from a checkpoint folder.'''       ,		)
     parser.add_argument(
         '''--partial_train_epoch'''       ,		type=_a       ,		default=_a       ,		help='''If passed, the training will stop after this number of epochs.'''       ,		)
     parser.add_argument(
         '''--num_epochs'''       ,		type=_a       ,		default=2       ,		help='''Number of train epochs.'''       ,		)
     lowercase_       :Tuple     =       parser.parse_args()
     lowercase_       :Union[str, Any]     =       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

     training_function(_a       ,		_a		)


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

