



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


UpperCAmelCase :				Tuple							=16
UpperCAmelCase :				str							=32



def  _lowerCAmelCase     (_lowerCAmelCase):
					return int(x / 2**20)


class    _lowercase :
				'''simple docstring'''







				def __enter__(			self     ):

									'''simple docstring'''


									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									UpperCamelCase_			= torch.cuda.memory_allocated()
									UpperCamelCase_			= psutil.Process()

									UpperCamelCase_			= self.cpu_mem_used()
									UpperCamelCase_			= True
									UpperCamelCase_			= threading.Thread(target=self.peak_monitor_func     )
									UpperCamelCase_			= True
									peak_monitor_thread.start()
									return self

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self.process.memory_info().rss

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= -1

									while True:
														UpperCamelCase_			= max(self.cpu_mem_used()  ,			self.cpu_peak     )

														# can't sleep or will not catch the peak right (this comment is here on purpose)
														# time.sleep(0.001) # 1msec

														if not self.peak_monitoring:
																			break

				def __exit__(			self  ,			*snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= False

									gc.collect()
									torch.cuda.empty_cache()
									UpperCamelCase_			= torch.cuda.memory_allocated()
									UpperCamelCase_			= torch.cuda.max_memory_allocated()
									UpperCamelCase_			= bamb(self.end - self.begin     )
									UpperCamelCase_			= bamb(self.peak - self.begin     )

									UpperCamelCase_			= self.cpu_mem_used()
									UpperCamelCase_			= bamb(self.cpu_end - self.cpu_begin     )
									UpperCamelCase_			= bamb(self.cpu_peak - self.cpu_begin     )
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

	UpperCAmelCase :				Union[str, Any]							=mocked_dataloaders  # noqa: F811



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						_lowerCAmelCase) == "1":
										UpperCamelCase_			= 2

					# New Code #
					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
					UpperCamelCase_			= FullyShardedDataParallelPlugin(
					    state_dict_config=FullStateDictConfig(offload_to_cpu=_lowerCAmelCase ,						ranka_only=_lowerCAmelCase) ,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_lowerCAmelCase ,						ranka_only=_lowerCAmelCase) ,						)

					# Initialize accelerator
					if args.with_tracking:
										UpperCamelCase_			= Accelerator(
										    cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with="wandb" ,						project_dir=args.logging_dir ,						fsdp_plugin=_lowerCAmelCase ,						)
					else:
										UpperCamelCase_			= Accelerator(fsdp_plugin=_lowerCAmelCase)
					accelerator.print(accelerator.distributed_type)

					if hasattr(args.checkpointing_steps ,						"isdigit"):
										if args.checkpointing_steps == "epoch":
															UpperCamelCase_			= args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															UpperCamelCase_			= int(args.checkpointing_steps)
										else:
															raise ValueError(
															    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""")
					else:
										UpperCamelCase_			= None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase_			= config["lr"]
					UpperCamelCase_			= int(config["num_epochs"])
					UpperCamelCase_			= int(config["seed"])
					UpperCamelCase_			= int(config["batch_size"])

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										UpperCamelCase_			= vars(_lowerCAmelCase)
										accelerator.init_trackers("fsdp_glue_no_trainer" ,						_lowerCAmelCase)

					UpperCamelCase_			= AutoTokenizer.from_pretrained(args.model_name_or_path)
					UpperCamelCase_			= load_dataset("glue" ,						"mrpc")
					UpperCamelCase_			= evaluate.load("glue" ,						"mrpc")

					def tokenize_function(_lowerCAmelCase):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase_			= tokenizer(examples["sentence1"] ,						examples["sentence2"] ,						truncation=_lowerCAmelCase ,						max_length=_lowerCAmelCase)
										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_			= datasets.map(
										    _lowerCAmelCase ,						batched=_lowerCAmelCase ,						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_			= tokenized_datasets.rename_column("label" ,						"labels")

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

					def collate_fn(_lowerCAmelCase):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase_			= 1_28 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_			= 16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase_			= 8
										else:
															UpperCamelCase_			= None

										return tokenizer.pad(
										    _lowerCAmelCase ,						padding="longest" ,						max_length=_lowerCAmelCase ,						pad_to_multiple_of=_lowerCAmelCase ,						return_tensors="pt" ,						)

					# Instantiate dataloaders.
					UpperCamelCase_			= DataLoader(
					    tokenized_datasets["train"] ,						shuffle=_lowerCAmelCase ,						collate_fn=_lowerCAmelCase ,						batch_size=_lowerCAmelCase)
					UpperCamelCase_			= DataLoader(
					    tokenized_datasets["validation"] ,						shuffle=_lowerCAmelCase ,						collate_fn=_lowerCAmelCase ,						batch_size=_lowerCAmelCase)

					set_seed(_lowerCAmelCase)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase_			= AutoModelForSequenceClassification.from_pretrained(
					    args.model_name_or_path ,						return_dict=_lowerCAmelCase ,						low_cpu_mem_usage=_lowerCAmelCase)

					# New Code #
					# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
					UpperCamelCase_			= accelerator.prepare(_lowerCAmelCase)
					accelerator.print(_lowerCAmelCase)

					# Instantiate optimizer
					# New Code #
					# For FSDP feature, at present it doesn't support multiple parameter groups,
					# so we need to create a single parameter group for the whole model
					UpperCamelCase_			= torch.optim.AdamW(params=model.parameters() ,						lr=_lowerCAmelCase ,						weight_decay=2e-4)

					# Instantiate scheduler
					UpperCamelCase_			= get_linear_schedule_with_warmup(
					    optimizer=_lowerCAmelCase ,						num_warmup_steps=10 ,						num_training_steps=(len(_lowerCAmelCase) * num_epochs) // gradient_accumulation_steps ,						)

					# New Code #
					# For FSDP feature, prepare everything except the model as we have already prepared the model
					# before creating the optimizer
					# 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_			= accelerator.prepare(
					    _lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					UpperCamelCase_			= 0

					# Potentially load in the weights and states from a previous save
					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_			= os.path.basename(args.resume_from_checkpoint)
										else:
															# Get the most recent checkpoint
															UpperCamelCase_			= [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
															dirs.sort(key=os.path.getctime)
															UpperCamelCase_			= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										UpperCamelCase_			= os.path.splitext(_lowerCAmelCase)[0]

										if "epoch" in training_difference:
															num_epochs -= int(training_difference.replace("epoch_" ,						""))
															UpperCamelCase_			= None
										else:
															UpperCamelCase_			= int(training_difference.replace("step_" ,						""))
															num_epochs -= resume_step // len(_lowerCAmelCase)
															# If resuming by step, we also need to know exactly how far into the DataLoader we went
															UpperCamelCase_			= (num_epochs * len(_lowerCAmelCase)) - resume_step

    # Now we train the model
					for epoch in range(_lowerCAmelCase):
										# New Code #
										# context manager to track the peak memory usage during the training epoch
										with TorchTracemalloc() as tracemalloc:
															model.train()
															if args.with_tracking:
																				UpperCamelCase_			= 0
															for step, batch in enumerate(_lowerCAmelCase):
																				# We need to skip steps until we reach the resumed step
																				if args.resume_from_checkpoint and epoch == 0:
																									if resume_step is not None and step < resume_step:
																														pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device)
																				UpperCamelCase_			= model(**_lowerCAmelCase)
																				UpperCamelCase_			= outputs.loss
																				UpperCamelCase_			= loss / gradient_accumulation_steps
																				# We keep track of the loss at each epoch
																				if args.with_tracking:
																									total_loss += loss.detach().float()
																				accelerator.backward(_lowerCAmelCase)
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# accelerator.print(lr_scheduler.get_lr())

																				overall_step += 1

																				if isinstance(_lowerCAmelCase ,						_lowerCAmelCase):
																									UpperCamelCase_			= f"""step_{overall_step}"""
																									if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																			UpperCamelCase_			= os.path.join(args.output_dir ,						_lowerCAmelCase)
																														accelerator.save_state(_lowerCAmelCase)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin)))
										accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
										accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
										accelerator.print(
										    "Total Peak Memory consumed during the train (max): {}".format(
										        tracemalloc.peaked + bamb(tracemalloc.begin)))
										# Logging the peak memory usage of the GPU to the tracker
										if args.with_tracking:
															accelerator.log(
															    {
															        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin),
															    } ,						step=_lowerCAmelCase ,						)

										# New Code #
										# context manager to track the peak memory usage during the evaluation
										with TorchTracemalloc() as tracemalloc:
															model.eval()
															for step, batch in enumerate(_lowerCAmelCase):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device)
																				with torch.no_grad():
																									UpperCamelCase_			= model(**_lowerCAmelCase)
																				UpperCamelCase_			= outputs.logits.argmax(dim=-1)
																				UpperCamelCase_	,     UpperCamelCase_			= accelerator.gather_for_metrics((predictions, batch["labels"]))
																				metric.add_batch(
																				    predictions=_lowerCAmelCase ,						references=_lowerCAmelCase ,						)

															UpperCamelCase_			= metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f"""epoch {epoch}:""" ,						_lowerCAmelCase)
															if args.with_tracking:
																				accelerator.log(
																				    {
																				        "accuracy": eval_metric["accuracy"],
																				        "f1": eval_metric["f1"],
																				        "train_loss": total_loss.item() / len(_lowerCAmelCase),
																				    } ,						step=_lowerCAmelCase ,						)

															if checkpointing_steps == "epoch":
																				UpperCamelCase_			= f"""epoch_{epoch}"""
																				if args.output_dir is not None:
																									UpperCamelCase_			= os.path.join(args.output_dir ,						_lowerCAmelCase)
																				accelerator.save_state(_lowerCAmelCase)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin)))
										accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
										accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
										accelerator.print(
										    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin)))
										# Logging the peak memory usage of the GPU to the tracker
										if args.with_tracking:
															accelerator.log(
															    {
															        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin),
															    } ,						step=_lowerCAmelCase ,						)

					if args.with_tracking:
										accelerator.end_training()



def  _lowerCAmelCase     ():
					UpperCamelCase_			= argparse.ArgumentParser(description="Simple example of training script.")
					parser.add_argument(
					    "--mixed_precision" ,						type=_lowerCAmelCase ,						default=_lowerCAmelCase ,						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=_lowerCAmelCase ,						default=_lowerCAmelCase ,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,						)
					parser.add_argument(
					    "--resume_from_checkpoint" ,						type=_lowerCAmelCase ,						default=_lowerCAmelCase ,						help="If the training should continue from a checkpoint folder." ,						)
					parser.add_argument(
					    "--with_tracking" ,						action="store_true" ,						help="Whether to load in all available experiment trackers from the environment and use them for logging." ,						)
					parser.add_argument(
					    "--output_dir" ,						type=_lowerCAmelCase ,						default="." ,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,						)
					parser.add_argument(
					    "--logging_dir" ,						type=_lowerCAmelCase ,						default="logs" ,						help="Location on where to store experiment tracking logs`" ,						)
					parser.add_argument(
					    "--model_name_or_path" ,						type=_lowerCAmelCase ,						help="Path to pretrained model or model identifier from huggingface.co/models." ,						required=_lowerCAmelCase ,						)
					UpperCamelCase_			= parser.parse_args()
					UpperCamelCase_			= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_lowerCAmelCase ,						_lowerCAmelCase)


if __name__ == "__main__":
	main()






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


__A						:		Union[str, Any]          =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     Optional[int]:







				'''simple docstring'''


				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}'''   )

				__lowerCAmelCase					=  []
				for i in range(UpperCamelCase__   ):
								__lowerCAmelCase					=  i / num_diffusion_timesteps
								__lowerCAmelCase					=  (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_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Any,     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		str = "epsilon",     _lowercase:		str = "linear",     _lowercase:		Optional[bool] = False,     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


									if trained_betas is not None:
													__lowerCAmelCase					=  torch.tensor(_lowercase,     dtype=torch.floataa)
									elif beta_schedule == "linear":
													__lowerCAmelCase					=  torch.linspace(_lowercase,     _lowercase,     _lowercase,     dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCAmelCase					=  (
													    torch.linspace(beta_start**0.5,     beta_end**0.5,     _lowercase,     dtype=torch.floataa) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCAmelCase					=  betas_for_alpha_bar(_lowercase)
									else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

									__lowerCAmelCase					=  1.0 - self.betas
									__lowerCAmelCase					=  torch.cumprod(self.alphas,     dim=0)

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase)

									# setable values
									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  np.linspace(0,     num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[::-1].copy()
									__lowerCAmelCase					=  torch.from_numpy(_lowercase)
									__lowerCAmelCase					=  False
									__lowerCAmelCase					=  use_karras_sigmas




					@property
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)

									__lowerCAmelCase					=  True
									return sample




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		int,     _lowercase:		Union[str, torch.device] = None):


									'''simple docstring'''


									__lowerCAmelCase					=  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":
													__lowerCAmelCase					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)

									if self.config.interpolation_type == "linear":
													__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)
									elif self.config.interpolation_type == "log_linear":
													__lowerCAmelCase					=  torch.linspace(np.log(sigmas[-1]),     np.log(sigmas[0]),     num_inference_steps + 1).exp()
									else:
													raise ValueError(
													    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
													    """ 'linear' or 'log_linear'""")

									if self.use_karras_sigmas:
													__lowerCAmelCase					=  self._convert_to_karras(in_sigmas=_lowercase,     num_inference_steps=self.num_inference_steps)
													__lowerCAmelCase					=  np.array([self._sigma_to_t(_lowercase,     _lowercase) for sigma in sigmas])

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		Optional[int],     _lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

									# get distribution
									__lowerCAmelCase					=  log_sigma - log_sigmas[:, np.newaxis]

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

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

									# interpolate sigmas
									__lowerCAmelCase					=  (low - log_sigma) / (low - high)
									__lowerCAmelCase					=  np.clip(_lowercase,     0,     1)

									# transform interpolation to time range
									__lowerCAmelCase					=  (1 - w) * low_idx + w * high_idx
									__lowerCAmelCase					=  t.reshape(sigma.shape)
									return t




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  in_sigmas[-1].item()
									__lowerCAmelCase					=  in_sigmas[0].item()

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     _lowercase)
									__lowerCAmelCase					=  sigma_min ** (1 / rho)
									__lowerCAmelCase					=  sigma_max ** (1 / rho)
									__lowerCAmelCase					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas




					def     _lowercase						(   self:		Optional[int],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		float = 0.0,     _lowercase:		float = 0.0,     _lowercase:		float = float("""inf"""),     _lowercase:		float = 1.0,     _lowercase:		Optional[torch.Generator] = None,     _lowercase:		bool = True,     ):


									'''simple docstring'''


									if (
									    isinstance(_lowercase,     _lowercase)
									    or isinstance(_lowercase,     torch.IntTensor)
									    or isinstance(_lowercase,     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(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)

									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  min(s_churn / (len(self.sigmas) - 1),     2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

									__lowerCAmelCase					=  randn_tensor(
									    model_output.shape,     dtype=model_output.dtype,     device=model_output.device,     generator=_lowercase)

									__lowerCAmelCase					=  noise * s_noise
									__lowerCAmelCase					=  sigma * (gamma + 1)

									if gamma > 0:
													__lowerCAmelCase					=  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									elif self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

									# 2. Convert to an ODE derivative
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma_hat

									__lowerCAmelCase					=  self.sigmas[step_index + 1] - sigma_hat

									__lowerCAmelCase					=  sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''simple docstring'''


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

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

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

									__lowerCAmelCase					=  original_samples + noise * sigma
									return noisy_samples







					def __len__(   self:		str):


									'''simple docstring'''


									return self.config.num_train_timesteps





