






import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


snake_case_   :	int							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		random.seed(lowerCAmelCase__	)
		np.random.seed(lowerCAmelCase__	)
		torch.manual_seed(lowerCAmelCase__	)
		torch.cuda.manual_seed_all(lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		model.eval()
		__A    =							next(model.parameters()	).device
		__A    =							1
		__A    =							torch.zeros((eval_batch_size, context_len)					,     dtype=torch.long					,     device=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							torch.zeros(1					,     device=lowerCAmelCase__	)
		__A    =							0
		for batch in eval_dataloader:
				batch.to(lowerCAmelCase__	)
				# pad
				context.zero_()
				for i in range(lowerCAmelCase__	):
						__A    =							batch[i]
				__A    =							model(lowerCAmelCase__					,     labels=lowerCAmelCase__	)
				eval_loss += outputs[0].sum().item()
				nb_eval_examples += batch.size(0	)
		__A    =							eval_loss / nb_eval_examples
		__A    =							torch.exp(lowerCAmelCase__	)
		model.train()
		return perplexity



def      UpperCAmelCase						( lowerCAmelCase__="gpt2"	):


		'''simple docstring'''

		__A    =							GPTaLMHeadModel.from_pretrained(lowerCAmelCase__					,     output_hidden_states=lowerCAmelCase__	)
		torch.save(model.state_dict()					,     model_name + "local.pt"	)
		return model



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							copy.deepcopy(lowerCAmelCase__	)
		model.to(lowerCAmelCase__	)

		__A    =							["bias", "LayerNorm.weight"]
		__A    =							[
		    {
		        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
		        "weight_decay": 0.0,
		    },
		    {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)], "weight_decay": 0.0},
		]
		__A    =							AdamW(lowerCAmelCase__					,     lr=5E-5					,     eps=1E-8	)
		__A    =							get_linear_schedule_with_warmup(lowerCAmelCase__					,     0					,     lowerCAmelCase__	)
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							np.array(lowerCAmelCase__	).mean()
		__A    =							np.array(lowerCAmelCase__	).std()
		__A    =							(real_perps - avg) / std
		__A    =							list(zip(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)	)
		joblib.dump(lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__="dev.jbl"					,     lowerCAmelCase__=recopy_gpta					,     ):


		'''simple docstring'''

		__A    =							[]
		__A    =							[]
		__A    =							[]

		# Initialize the transformer model
		__A    =							copy.deepcopy(lowerCAmelCase__	)
		orig_model.to(device="cpu"	)
		torch.cuda.empty_cache()

		# Compute perplexity of initial transformer model for comparison
		model.train()
		__A ,   __A ,   __A    =							recopy_model(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

		for step in tqdm(range(lowerCAmelCase__	)	):
				__A    =							torch.zeros((1, context_len)					,     dtype=torch.long					,     device=lowerCAmelCase__	)
				__A    =							random.choice(lowerCAmelCase__	)
				__A    =							random.randint(0					,     len(story[0]	) - context_len - 1	)
				__A    =							story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				__A    =							model(lowerCAmelCase__					,     labels=lowerCAmelCase__	)
				__A    =							outputs[0]
				__A    =							compute_perplexity(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)
				model.train()
				lm_loss.backward()
				# Do LM backprop
				torch.nn.utils.clip_grad_norm_(model.parameters()					,     3.0	)
				lm_optimizer.step()
				lm_scheduler.step()  # Update learning rate schedule

				# Compute perplexity after back-propagating on the selected context
				__A    =							compute_perplexity(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

				# Periodically save the stored (X, IG(X)) pairs
				if step % 1000 == 0 and step > 1:
						intermittent_save(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
				__A ,   __A ,   __A    =							recopy_model(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

				past_perps.append(past_perp.item()	)
				real_perps.append(orig_perp - real_perp.item()	)
				contexts.append(np.array(context.cpu()	)	)

		intermittent_save(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"					,     lowerCAmelCase__=100					,     lowerCAmelCase__=1026					,     lowerCAmelCase__=True	):


		'''simple docstring'''

		__A    =							joblib.load(lowerCAmelCase__	)
		print("data loaded"	)
		__A    =							[]
		if trim:
				for i, example in enumerate(lowerCAmelCase__	):
						if len(example[0]	) > min_len:
								__A    =							random.randint(0					,     len(example[0]	) - context_len - 1	)
								objective_set.append(example[0, start : start + context_len]	)
						if len(lowerCAmelCase__	) >= number:
								break
				__A    =							[]
				for j in range(i + 1					,     len(lowerCAmelCase__	)	):
						if len(data[j][0]	) > min_len:
								train_data.append(data[j]	)
		else:
				__A    =							data[0:number]
				__A    =							data[number:]

		joblib.dump(lowerCAmelCase__					,     "objective_set.jbl"	)
		print("objective set saved"	)
		return train_data, objective_set



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=50					,     lowerCAmelCase__="secondary_learner.pt"	):


		'''simple docstring'''

		__A    =							torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
		# We will use the first 512 pairs from our dataset as a test set for
		# our secondary learner and the rest to train
		__A    =							train_dataset[:512]
		__A    =							train_dataset[512:]
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)

		# secondary learner model set up
		__A    =							nn.MSELoss()
		__A    =							nn.MSELoss(reduction="sum"	)
		secondary_learner.to(lowerCAmelCase__	)
		__A    =							torch.optim.Adam(secondary_learner.parameters()					,     lr=0.00_001	)
		secondary_learner.train()

		# TODO in original code this is written as number of actual batches seen
		# not number of items seen but other places it is number of items instead.
		# improve consistency! changed this to epochs for clarity
		__A    =							float("inf"	)
		# Iterate through batches until we've used max_steps batches
		for epoch in range(int(lowerCAmelCase__	)	):
				__A    =							0.0
				secondary_learner.train()
				for step, batch in enumerate(lowerCAmelCase__	):
						__A    =							batch[0].to(lowerCAmelCase__	)
						__A    =							batch[1].to(lowerCAmelCase__	)
						__A    =							secondary_learner(lowerCAmelCase__	)
						q_optimizer.zero_grad()
						__A    =							loss(lowerCAmelCase__					,     real_q.float()	)
						q_loss.backward()
						q_optimizer.step()
						tr_q_loss += q_loss.item()

						# model trains fairly quickly so we won't wait for a full epoch
						# eval is triggered at eval_freq and end of epochs
						if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCAmelCase__	)):
								__A    =							tr_q_loss / (step + 1)

								secondary_learner.eval()
								__A    =							0.0
								__A    =							0.0
								__A    =							[]
								__A    =							[]
								# Compute performance of the secondary learner after this batch
								for stepa, batcha in enumerate(lowerCAmelCase__	):
										__A    =							batcha[0].to(lowerCAmelCase__	)
										__A    =							batcha[1].to(lowerCAmelCase__	)
										__A    =							secondary_learner(lowerCAmelCase__	)
										q_lossa += test_loss(lowerCAmelCase__					,     lowerCAmelCase__	).item()
										sum_qa += torch.sum(lowerCAmelCase__	).item()
										for ei, i in enumerate(predicted_qa.cpu().detach().numpy()	):
												predicted.append(i.item()	)
										for ei, i in enumerate(real_qa.cpu().detach().numpy()	):
												actual.append(i.item()	)

								q_lossa /= len(lowerCAmelCase__	)
								print(
								    "Epoch: "					,     lowerCAmelCase__					,     "step: "					,     lowerCAmelCase__					,     "Avg. q:"					,     sum_qa / len(lowerCAmelCase__	)					,     "Train Loss: "					,     lowerCAmelCase__					,     "Test Loss: "					,     lowerCAmelCase__					,     )
								if q_lossa < best_test_loss:
										joblib.dump((predicted, actual)					,     "pred_vs_actual.jbl"	)
										torch.save(secondary_learner.state_dict()					,     lowerCAmelCase__	)
										__A    =							q_lossa

						secondary_learner.train()
		return secondary_learner





class        a__      (	nn.Module		):






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				super(lowercase__							, self						).__init__()
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				__A    =							nn.Convad(self.embeddings.weight.size(1						)							, 256							, 3							, padding=1						)
				__A    =							nn.Sequential(nn.Linear(256							, 32						)							, nn.Dropout(p=0.1						)							, nn.Linear(32							, 32						)							, nn.Linear(32							, 1						)						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							torch.max(self.conv(self.embeddings(lowercase__						).squeeze(1						).transpose(1							, 2						)						)							, 2						)[0]
				__A    =							self.fc(lowercase__						)
				return qs.squeeze(1						)






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__						)				->				List[str]:
				__A    =							cls(lowercase__						)  # this calls __init__
				__A    =							torch.load(lowercase__						)
				secondary_learner.load_state_dict(lowercase__						)
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				return secondary_learner






"""simple docstring"""







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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

							elif alpha_transform_type == "exp":

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

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

							A    =       []
							for i in range(lowerCamelCase__      ):
														A    =       i / num_diffusion_timesteps
														A    =       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase__      ) / alpha_bar_fn(lowerCamelCase__      )					,   lowerCamelCase__      )      )
							return torch.tensor(lowerCamelCase__					,   dtype=torch.floataa      )






class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase  ):
		lowerCAmelCase_							:						int							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						Union[str, Any]							=  2



		@register_to_config
		def __init__(			self	:     Any   ,				snake_case	:     int = 1_000   ,				snake_case	:     float = 0.00085   ,				snake_case	:     float = 0.012   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[Union[np.ndarray, List[float]]] = None   ,				snake_case	:     str = "epsilon"   ,				snake_case	:     str = "linspace"   ,				snake_case	:     int = 0   ,				)      ->    Dict:






									'''simple docstring'''

									if trained_betas is not None:
																A    =       torch.tensor(snake_case   ,				dtype=torch.floataa					)
									elif beta_schedule == "linear":
																A    =       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.
																A    =       (
																    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
																A    =       betas_for_alpha_bar(snake_case					)
									else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

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

									#  set all values
									self.set_timesteps(snake_case   ,				snake_case   ,				snake_case					)



		def        A_		(			self	:     str   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Optional[Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A    =       self.timesteps

									A    =       (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:
																A    =       1 if len(snake_case					) > 1 else 0
									else:
																A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
																A    =       self._index_counter[timestep_int]

									return indices[pos].item()



		@property
		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

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

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



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
									else:
																A    =       self.sigmas_interpol[step_index - 1]

									A    =       sample / ((sigma**2 + 1) ** 0.5)
									return sample



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     int   ,				snake_case	:     Union[str, torch.device] = None   ,				snake_case	:     Optional[int] = None   ,				)      ->    Any:






									'''simple docstring'''

									A    =       num_inference_steps

									A    =       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":
																A    =       np.linspace(0   ,				num_train_timesteps - 1   ,				snake_case   ,				dtype=snake_case					)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																A    =       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
																A    =       (np.arange(0   ,				snake_case					) * step_ratio).round()[::-1].copy().astype(snake_case					)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A    =       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
																A    =       (np.arange(snake_case   ,				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'."""					)

									A    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
									A    =       torch.from_numpy(np.log(snake_case					)					).to(snake_case					)

									A    =       np.interp(snake_case   ,				np.arange(0   ,				len(snake_case					)					)   ,				snake_case					)
									A    =       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(snake_case					).to(device=snake_case					)

									# compute up and down sigmas
									A    =       sigmas.roll(-1					)
									A    =       0.0
									A    =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									A    =       (sigmas_next**2 - sigmas_up**2) ** 0.5
									A    =       0.0

									# compute interpolated sigmas
									A    =       sigmas.log().lerp(sigmas_down.log()   ,				0.5					).exp()
									A    =       0.0

									# set sigmas
									A    =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
									A    =       torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
									A    =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
									A    =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

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

									A    =       self.sigma_to_t(snake_case					).to(snake_case   ,				dtype=timesteps.dtype					)
									A    =       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,				dim=-1					).flatten()

									A    =       torch.cat([timesteps[:1], interleaved_timesteps]					)

									A    =       None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									A    =       defaultdict(snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict					)      ->    Tuple:






									'''simple docstring'''

									A    =       sigma.log()

									# get distribution
									A    =       log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									A    =       dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
									A    =       low_idx + 1

									A    =       self.log_sigmas[low_idx]
									A    =       self.log_sigmas[high_idx]

									# interpolate sigmas
									A    =       (low - log_sigma) / (low - high)
									A    =       w.clamp(0   ,				1					)

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



		@property
		def        A_		(			self	:     Any					)      ->    int:






									'''simple docstring'''

									return self.sample is None



		def        A_		(			self	:     Tuple   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Optional[torch.Generator] = None   ,				snake_case	:     bool = True   ,				)      ->    Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									# advance index counter by 1
									A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
																A    =       self.sigmas_interpol[step_index]
																A    =       self.sigmas_up[step_index]
																A    =       self.sigmas_down[step_index - 1]
									else:
																# 2nd order / KPDM2's method
																A    =       self.sigmas[step_index - 1]
																A    =       self.sigmas_interpol[step_index - 1]
																A    =       self.sigmas_up[step_index - 1]
																A    =       self.sigmas_down[step_index - 1]

									# 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
									A    =       0
									A    =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									A    =       model_output.device
									A    =       randn_tensor(model_output.shape   ,				dtype=model_output.dtype   ,				device=snake_case   ,				generator=snake_case					)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**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`"""					)

									if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																A    =       (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																A    =       sigma_interpol - sigma_hat

																# store for 2nd order step
																A    =       sample
																A    =       dt
																A    =       sample + derivative * dt
									else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																A    =       (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																A    =       sigma_down - sigma_hat

																A    =       self.sample
																A    =       None

																A    =       sample + derivative * dt
																A    =       prev_sample + noise * sigma_up

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       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
																A    =       self.timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
																A    =       timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
									else:
																A    =       self.timesteps.to(original_samples.device					)
																A    =       timesteps.to(original_samples.device					)

									A    =       [self.index_for_timestep(snake_case   ,				snake_case					) for t in timesteps]

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

									A    =       original_samples + noise * sigma
									return noisy_samples



		def __len__(			self	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return self.config.num_train_timesteps



