

'''simple docstring'''


import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCAmelCase__      				=			16
lowerCAmelCase__      				=			32







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase = 1_6	)     ->     Optional[int]:
	UpperCAmelCase_		:   str													=       AutoTokenizer.from_pretrained('bert-base-cased'	)
	UpperCAmelCase_		:   int													=       load_dataset('glue' ,'mrpc'	)

	def tokenize_function(UpperCamelCase	):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_		:   int													=       tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=UpperCamelCase ,max_length=UpperCamelCase	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		UpperCAmelCase_		:   Optional[Any]													=       datasets.map(
		    UpperCamelCase ,batched=UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,)

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

	def collate_fn(UpperCamelCase	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_		:   str													=       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			UpperCAmelCase_		:   Optional[Any]													=       1_6
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_		:   str													=       8
		else:
			UpperCAmelCase_		:   List[Any]													=       None

		return tokenizer.pad(
		    UpperCamelCase ,padding='longest' ,max_length=UpperCamelCase ,pad_to_multiple_of=UpperCamelCase ,return_tensors='pt' ,)

	# Instantiate dataloaders.
	UpperCAmelCase_		:   Union[str, Any]													=       DataLoader(
	    tokenized_datasets['train'] ,shuffle=UpperCamelCase ,collate_fn=UpperCamelCase ,batch_size=UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       DataLoader(
	    tokenized_datasets['validation'] ,shuffle=UpperCamelCase ,collate_fn=UpperCamelCase ,batch_size=UpperCamelCase	)

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						lowerCAmelCase__      				=			mocked_dataloaders  # noqa: F811







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Optional[int]:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS' ,UpperCamelCase	) == "1":
		UpperCAmelCase_		:   Tuple													=       2
	# Initialize accelerator
	UpperCAmelCase_		:   int													=       Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision	)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_		:   Any													=       config['lr']
	UpperCAmelCase_		:   Any													=       int(config['num_epochs']	)
	UpperCAmelCase_		:   Optional[Any]													=       int(config['seed']	)
	UpperCAmelCase_		:   Optional[Any]													=       int(config['batch_size']	)

	# New Code #
	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps ,'isdigit'	):
		if args.checkpointing_steps == "epoch":
			UpperCAmelCase_		:   Dict													=       args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			UpperCAmelCase_		:   Dict													=       int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
	else:
		UpperCAmelCase_		:   int													=       None

	set_seed(UpperCamelCase	)

	UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[int]													=       get_dataloaders(UpperCamelCase ,UpperCamelCase	)
	UpperCAmelCase_		:   str													=       evaluate.load('glue' ,'mrpc'	)

	# If the batch size is too big we use gradient accumulation
	UpperCAmelCase_		:   int													=       1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		UpperCAmelCase_		:   Optional[int]													=       batch_size // MAX_GPU_BATCH_SIZE
		UpperCAmelCase_		:   int													=       MAX_GPU_BATCH_SIZE

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_		:   Dict													=       AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,return_dict=UpperCamelCase	)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	UpperCAmelCase_		:   List[str]													=       model.to(accelerator.device	)

	# Instantiate optimizer
	UpperCAmelCase_		:   List[str]													=       AdamW(params=model.parameters() ,lr=UpperCamelCase	)

	# Instantiate scheduler
	UpperCAmelCase_		:   Dict													=       get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase ,num_warmup_steps=1_0_0 ,num_training_steps=(len(UpperCamelCase	) * num_epochs) // gradient_accumulation_steps ,)

	# 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.
	UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       accelerator.prepare(
	    UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	UpperCAmelCase_		:   List[str]													=       0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCAmelCase_		:   List[str]													=       0

	# We need to load the checkpoint back in before training here with `load_state`
	# The total number of epochs is adjusted based on where the state is being loaded from,
	# as we assume continuation of the same training script
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
			accelerator.load_state(args.resume_from_checkpoint	)
			UpperCAmelCase_		:   Dict													=       os.path.basename(args.resume_from_checkpoint	)
		else:
			# Get the most recent checkpoint
			UpperCAmelCase_		:   Dict													=       [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
			dirs.sort(key=os.path.getctime	)
			UpperCAmelCase_		:   Dict													=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		UpperCAmelCase_		:   str													=       os.path.splitext(UpperCamelCase	)[0]

		if "epoch" in training_difference:
			UpperCAmelCase_		:   List[Any]													=       int(training_difference.replace('epoch_' ,''	)	) + 1
			UpperCAmelCase_		:   Union[str, Any]													=       None
		else:
			UpperCAmelCase_		:   str													=       int(training_difference.replace('step_' ,''	)	)
			UpperCAmelCase_		:   Optional[int]													=       resume_step // len(UpperCamelCase	)
			resume_step -= starting_epoch * len(UpperCamelCase	)

    # Now we train the model
	for epoch in range(UpperCamelCase ,UpperCamelCase	):
		model.train()
		# New Code #
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			UpperCAmelCase_		:   Union[str, Any]													=       accelerator.skip_first_batches(UpperCamelCase ,UpperCamelCase	)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			UpperCAmelCase_		:   str													=       train_dataloader
		for step, batch in enumerate(UpperCamelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			UpperCAmelCase_		:   List[str]													=       model(**UpperCamelCase	)
			UpperCAmelCase_		:   Dict													=       outputs.loss
			UpperCAmelCase_		:   List[Any]													=       loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
			# New Code #
			overall_step += 1

			# New Code #
			# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
			# These are saved to folders named `step_{overall_step}`
			# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
			# If mixed precision was used, will also save a "scalar.bin" file
			if isinstance(UpperCamelCase ,UpperCamelCase	):
				UpperCAmelCase_		:   Tuple													=       f"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						UpperCAmelCase_		:   Optional[int]													=       os.path.join(args.output_dir ,UpperCamelCase	)
					accelerator.save_state(UpperCamelCase	)

		model.eval()
		for step, batch in enumerate(UpperCamelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
			batch.to(accelerator.device	)
			with torch.no_grad():
				UpperCAmelCase_		:   Dict													=       model(**UpperCamelCase	)
			UpperCAmelCase_		:   str													=       outputs.logits.argmax(dim=-1	)
			UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       accelerator.gather_for_metrics((predictions, batch['labels'])	)
			metric.add_batch(
			    predictions=UpperCamelCase ,references=UpperCamelCase ,)

		UpperCAmelCase_		:   List[str]													=       metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:""" ,UpperCamelCase	)

		# New Code #
		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
		# These are saved to folders named `epoch_{epoch}`
		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
		# If mixed precision was used, will also save a "scalar.bin" file
		if checkpointing_steps == "epoch":
			UpperCAmelCase_		:   Tuple													=       f"""epoch_{epoch}"""
			if args.output_dir is not None:
				UpperCAmelCase_		:   int													=       os.path.join(args.output_dir ,UpperCamelCase	)
			accelerator.save_state(UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	)     ->     Union[str, Any]:
	UpperCAmelCase_		:   Any													=       argparse.ArgumentParser(description='Simple example of training script.'	)
	parser.add_argument(
	    '--mixed_precision' ,type=UpperCamelCase ,default=UpperCamelCase ,choices=['no', 'fp16', 'bf16', 'fp8'] ,help='Whether to use mixed precision. Choose'
	    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
	    'and an Nvidia Ampere GPU.' ,)
	parser.add_argument('--cpu' ,action='store_true' ,help='If passed, will train on the CPU.'	)
	parser.add_argument(
	    '--checkpointing_steps' ,type=UpperCamelCase ,default=UpperCamelCase ,help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' ,)
	parser.add_argument(
	    '--output_dir' ,type=UpperCamelCase ,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=UpperCamelCase ,default=UpperCamelCase ,help='If the training should continue from a checkpoint folder.' ,)
	UpperCAmelCase_		:   Optional[Any]													=       parser.parse_args()
	UpperCAmelCase_		:   Union[str, Any]													=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(UpperCamelCase ,UpperCamelCase	)


if __name__ == "__main__":
						main()









'''simple docstring'''


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase__      				=			logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	UpperCAmelCase_		:   Optional[int]													=       []
	for i in range(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[Any]													=       i / num_diffusion_timesteps
		UpperCAmelCase_		:   Any													=       (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase	) / alpha_bar_fn(UpperCamelCase	) ,UpperCamelCase	)	)
	return torch.tensor(UpperCamelCase ,dtype=torch.floataa	)







class        lowercase    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       torch.linspace(_snake_case     ,      _snake_case     ,      _snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase_		:   List[Any]													=       (
									    torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      _snake_case     ,      dtype=torch.floataa) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

								UpperCAmelCase_		:   List[Any]													=       1.0 - self.betas
								UpperCAmelCase_		:   Union[str, Any]													=       torch.cumprod(self.alphas     ,      dim=0)

								UpperCAmelCase_		:   Union[str, Any]													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   int													=       np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case)

								# setable values
								UpperCAmelCase_		:   List[str]													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       np.linspace(0     ,      num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[::-1].copy()
								UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       False


							@property
							def     _snake_case				(      self)				->					Optional[Any]:
								# 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     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
									UpperCAmelCase_		:   List[Any]													=       np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									UpperCAmelCase_		:   Any													=       self.config.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
									UpperCAmelCase_		:   List[Any]													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									UpperCAmelCase_		:   List[Any]													=       self.config.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
									UpperCAmelCase_		:   Any													=       (np.arange(self.config.num_train_timesteps     ,      0     ,      -step_ratio)).round().copy().astype(_snake_case)
									timesteps -= 1
								else:
									raise ValueError(
									    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

								UpperCAmelCase_		:   Any													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   Dict													=       np.interp(_snake_case     ,      np.arange(0     ,      len(_snake_case))     ,      _snake_case)
								UpperCAmelCase_		:   Optional[int]													=       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case).to(device=_snake_case)
								if str(_snake_case).startswith('mps'):
									# mps does not support float64
									UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case).to(_snake_case     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       torch.from_numpy(_snake_case).to(device=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(_snake_case     ,      _snake_case)
								    or isinstance(_snake_case     ,      torch.IntTensor)
								    or isinstance(_snake_case     ,      torch.LongTensor)
								):
									raise ValueError(
									    (
									        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
									        ' `EulerDiscreteScheduler.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(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   List[Any]													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   int													=       self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									UpperCAmelCase_		:   Tuple													=       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCAmelCase_		:   str													=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample')
								else:
									raise ValueError(
									    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								UpperCAmelCase_		:   Dict													=       (sample - pred_original_sample) / sigma

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

								UpperCAmelCase_		:   Any													=       model_output.device
								UpperCAmelCase_		:   List[Any]													=       randn_tensor(model_output.shape     ,      dtype=model_output.dtype     ,      device=_snake_case     ,      generator=_snake_case)

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   List[str]													=       self.sigmas.to(device=original_samples.device     ,      dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
									# mps does not support float64
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[Any]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   str													=       timesteps.to(original_samples.device)

								UpperCAmelCase_		:   Optional[Any]													=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								UpperCAmelCase_		:   Any													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   Union[str, Any]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





							def __len__(      self)				->					Optional[Any]:
								return self.config.num_train_timesteps






