import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError('''You need to install the version of fairseq from the Mega repo!''')




class      lowercase_						(						nn.Module		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Optional[Any]:
					super().__init__()
					lowerCAmelCase				=	mega_args
					lowerCAmelCase				=	nn.Embedding(__SCREAMING_SNAKE_CASE	,    self.mega_args.encoder_embed_dim       )
					lowerCAmelCase				=	nn.ModuleList([MegaEncoderLayer(self.mega_args       ) for _ in range(__SCREAMING_SNAKE_CASE       )]       )
					lowerCAmelCase				=	depth




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=0       )		->Optional[int]:

					# Mega expects embeddings to be (time, batch, embedding size), but
					# Hugging Face returns tokens as (batch, time)
					if batch_first:
									lowerCAmelCase				=	input_ids.T

					# to make things more confusing, Mega expects the attention mask to
					# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
					# which is the opposite of what HF returns
					if ignore_mask_value == 0:
									lowerCAmelCase				=	1 - attention_mask

					# get token embeddings from IDs
					lowerCAmelCase				=	self.embedding_layer(__SCREAMING_SNAKE_CASE       )

					# pass through the Mega layers
					# input is (time, batch, encoder dim) and output is the same
					for encoder in self.encoders:
									lowerCAmelCase				=	encoder(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

					# return according to the shape specified
					if batch_first:
									# (T, B, H) --> (B, T, H)
									return torch.transpose(__SCREAMING_SNAKE_CASE	,    0	,    1       )
					else:
									return embeds




class      lowercase_						(						nn.Module		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
					super().__init__()
					lowerCAmelCase				=	MegaLM(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
					lowerCAmelCase				=	nn.Linear(mega_args.encoder_embed_dim	,    __SCREAMING_SNAKE_CASE       )
					lowerCAmelCase				=	nn.Dropout(p=0.1       )




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=0       )		->List[str]:
					lowerCAmelCase				=	self.mega(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
					return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE       )       )

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Optional[Any]:
				with open(os.path.join(snake_case__     ,						'''model_args.pkl'''							)     ,						'''rb'''							) as f:
								lowerCAmelCase				=	pkl.load(snake_case__							)

				# load the original encoder
				lowerCAmelCase				=	OriginalMegaForMaskedLM(**snake_case__							).eval()

				# load its weights
				print(
				    '''Original Mega encoder:'''     ,						original_mlm.mega.load_state_dict(
				        torch.load(os.path.join(snake_case__     ,						'''encoder_weights.pt'''							)     ,						map_location='''cpu'''							)							)     ,						)
				print(
				    '''Original Mega MLM layer:'''     ,						original_mlm.mlm_head.load_state_dict(
				        torch.load(os.path.join(snake_case__     ,						'''mlm_head_weights.pt'''							)     ,						map_location='''cpu'''							)							)     ,						)

				# create a new config from the old one
				lowerCAmelCase				=	MegaConfig(
				    num_hidden_layers=mega_original_args['''depth''']     ,						vocab_size=mega_original_args['''vocab_size''']     ,						hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim     ,						shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim     ,						intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim     ,						ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim     ,						dropout_prob=mega_original_args['''mega_args'''].dropout     ,						attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout     ,						hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout     ,						activation=mega_original_args['''mega_args'''].activation_fn     ,						attention_activation=mega_original_args['''mega_args'''].attention_activation_fn     ,						bidirectional=mega_original_args['''mega_args'''].bidirectional     ,						use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0     ,						chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size     ,						truncation=mega_original_args['''mega_args'''].truncation_length     ,						normalization_type=mega_original_args['''mega_args'''].normalization_type     ,						normalize_before_mega=snake_case__     ,						norm_affine=snake_case__     ,						use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout     ,						relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias     ,						max_positions=mega_original_args['''mega_args'''].max_source_positions     ,						nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim     ,						normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before     ,						nffn_activation_dropout_prob=0.0     ,						add_token_type_embeddings=snake_case__     ,						add_lm_hidden_dense_layer=snake_case__     ,						)

				lowerCAmelCase				=	MegaForMaskedLM(snake_case__							).eval()

				# the originl checkpoint just uses nn.Embedding for the word embeddings
				# we use a wrapper module for embeddings to add support for positional embeddings
				lowerCAmelCase				=	original_mlm.mega.embedding_layer.weight

				# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
				# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
				# also renaming previously confusing parameter names
				lowerCAmelCase				=	original_mlm.mega.encoders.state_dict()
				lowerCAmelCase				=	{}
				for module_name in original_state_dict.keys():
								lowerCAmelCase				=	None
								# have to handle gamma, beta, and alpha differently due to their use
								# in multiple modules within the original repository;
								# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
								# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
								if "beta" in module_name:
												# EMA sub-layers were always called "move" in the original repo
												if "move.beta" in module_name:
																lowerCAmelCase				=	module_name.replace('''move.beta'''     ,						'''ema_gate.ema_expansion_matrix'''							)
												elif "mega_layer.beta" in module_name:
																lowerCAmelCase				=	module_name.replace('''beta'''     ,						'''qk_bias'''							)
												else:
																lowerCAmelCase				=	module_name.replace('''beta'''     ,						'''b_param'''							)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
								elif "gamma" in module_name:
												if "move.gamma" in module_name:
																lowerCAmelCase				=	module_name.replace('''move.gamma'''     ,						'''ema_gate.kernel_projection_matrix'''							)
												elif "mega_layer.gamma" in module_name:
																lowerCAmelCase				=	module_name.replace('''gamma'''     ,						'''qk_weight'''							)
												else:
																lowerCAmelCase				=	module_name.replace('''gamma'''     ,						'''g_param'''							)
        # alpha is used in EMA and positional bias; renaming to improve readability
								elif "move.alpha" in module_name:
												lowerCAmelCase				=	module_name.replace('''move.alpha'''     ,						'''ema_gate.decay_factor'''							)
								# delta is only used in EMA; renaming to improve readability
								elif "move.delta" in module_name:
												lowerCAmelCase				=	module_name.replace('''move.delta'''     ,						'''ema_gate.damping_factor'''							)
								# omega is only used in EMA; renaming to improve readability
								elif "omega" in module_name:
												lowerCAmelCase				=	module_name.replace('''move.omega'''     ,						'''ema_gate.residual_weight'''							)

								if new_module_name:
												lowerCAmelCase				=	new_module_name

				if len(snake_case__							) != 0:
								print(f"Renaming these keys: {updated_keys.keys()}"							)
				else:
								print('''No need to rename state dict entries'''							)
				for old, new in updated_keys.items():
								lowerCAmelCase				=	original_state_dict.pop(snake_case__							)

				# now attempt to load the state dictionary with updated names
				# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
				print('''HF Mega encoder:'''     ,						hf_mlm.mega.layers.load_state_dict(snake_case__							)							)

				# load the MLM head weights directly
				print(
				    '''HF Mega MLM layer:'''     ,						hf_mlm.mlm_head.load_state_dict(
				        torch.load(os.path.join(snake_case__     ,						'''mlm_head_weights.pt'''							)     ,						map_location='''cpu'''							)							)     ,						)

				# test on a randomly generated input sequence
				lowerCAmelCase				=	torch.randint(0     ,						hf_config.vocab_size     ,						size=(4, 2_5_6)							)
				lowerCAmelCase				=	torch.ones_like(snake_case__							)
				# mask a few tokens to make sure masking is applied appropriately :)
				lowerCAmelCase				=	0

				# run forward passes
				lowerCAmelCase				=	original_mlm(snake_case__     ,						snake_case__     ,						batch_first=snake_case__     ,						ignore_mask_value=0							)
				lowerCAmelCase				=	hf_mlm(snake_case__     ,						snake_case__							)[0]

				# print shapes and diff
				print(f"original output {original_output.shape}"							)
				print(f"hf output {hf_output.shape}"							)
				print(f"max diff: {(original_output - hf_output).max()}"							)  # 0.0
				lowerCAmelCase				=	torch.allclose(snake_case__     ,						snake_case__     ,						atol=1E-3							)

				if success:
								print('''Yay!'''							)
								hf_mlm.save_pretrained(snake_case__							)
				else:
								raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"							)

				if includes_tokenizer:
								print('''Transferring tokenizer'''							)
								lowerCAmelCase				=	AutoTokenizer.from_pretrained(snake_case__							)
								tokenizer.save_pretrained(snake_case__							)


if __name__ == "__main__":
	lowercase__    :       int						=			argparse.ArgumentParser()

	parser.add_argument(
	    '''--pretrained_checkpoint_path''',
	    default=None,
	    type=str,
	    required=True,
	    help='''Point to the directory containing your model weights using the official Mega repo''',
	)

	parser.add_argument(
	    '''--output_path''', default=None, type=str, required=True, help='''Location to save the Hugging Face version'''
	)

	parser.add_argument(
	    '''--includes_tokenizer''',
	    action='''store_true''',
	    help='''Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo''',
	)

	lowercase__    :       Union[str, Any]						=			parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)




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

import numpy as np
import torch

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


lowercase__    :       List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      torch.FloatTensor




class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Tuple     =       1


	@register_to_config
	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 40	,    __SCREAMING_SNAKE_CASE = 0.0_0_2	,    __SCREAMING_SNAKE_CASE = 8_0.0	,    __SCREAMING_SNAKE_CASE = 0.5	,    __SCREAMING_SNAKE_CASE = 1.0	,    __SCREAMING_SNAKE_CASE = 7.0	,    __SCREAMING_SNAKE_CASE = True	,    )		->Tuple:
								# standard deviation of the initial noise distribution
								lowerCAmelCase				=	sigma_max

								lowerCAmelCase				=	np.linspace(0	,    1	,    __SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )

								# setable values
								lowerCAmelCase				=	None
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	False
								lowerCAmelCase				=	False


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								if schedule_timesteps is None:
															lowerCAmelCase				=	self.timesteps

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


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->torch.FloatTensor:
								# Get sigma corresponding to timestep
								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	self.sigmas[step_idx]

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

								lowerCAmelCase				=	True
								return sample


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								if not isinstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       ):
															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.floataa       )

								lowerCAmelCase				=	1000 * 0.2_5 * np.log(sigmas + 1e-44       )

								return timesteps


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    )		->str:
								if num_inference_steps is None and timesteps is None:
															raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

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

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

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

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

															lowerCAmelCase				=	num_inference_steps

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

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

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

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


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.config.rho
								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     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								lowerCAmelCase				=	self.config.sigma_data

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


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_data

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


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    )		->Union[CMStochasticIterativeSchedulerOutput, Tuple]:

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

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

								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
															lowerCAmelCase				=	timestep.to(self.timesteps.device       )

								lowerCAmelCase				=	self.config.sigma_min
								lowerCAmelCase				=	self.config.sigma_max

								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

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

								# Get scalings for boundary conditions
								lowerCAmelCase	,   lowerCAmelCase				=	self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )

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

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

								lowerCAmelCase				=	sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,    max=__SCREAMING_SNAKE_CASE       )

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

								if not return_dict:
															return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    )		->torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCAmelCase				=	self.sigmas.to(device=original_samples.device	,    dtype=original_samples.dtype       )
								if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE       ):
															# 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       )		->int:
								return self.config.num_train_timesteps



