code: 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)
  
  
  
  
style_context: 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
  
  
  
  