
from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__a							  =						getLogger(__name__)


__a							  =						4
__a							  =						7

__a							  =						{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}

def    UpperCamelCase_ (	a_		)     ->int:
		if isinstance(a_  ,				torch.Tensor		):
				return image
		elif isinstance(a_  ,				PIL.Image.Image		):
				A				=[image]

		if isinstance(image[0]  ,				PIL.Image.Image		):
				A			,    A				=image[0].size
				A			,    A				=(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

				A				=[np.array(i.resize((w, h)		)		)[None, :] for i in image]
				A				=np.concatenate(a_  ,				axis=0		)
				A				=np.array(a_		).astype(np.floataa		) / 255.0
				A				=image.transpose(0  ,				3  ,				1  ,				2		)
				A				=2.0 * image - 1.0
				A				=torch.from_numpy(a_		)
		elif isinstance(image[0]  ,				torch.Tensor		):
				A				=torch.cat(a_  ,				dim=0		)

		return image






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	def __init__(      self				:			Dict							,			snake_case__				:			OnnxRuntimeModel							,			snake_case__				:			OnnxRuntimeModel							,			snake_case__				:			Any							,			snake_case__				:			OnnxRuntimeModel							,			snake_case__				:			DDPMScheduler							,			snake_case__				:			Any							,			snake_case__				:			int = 3_50							,			):
			"""simple docstring"""





			super().__init__(
			    vae=snake_case__							,			text_encoder=snake_case__							,			tokenizer=snake_case__							,			unet=snake_case__							,			low_res_scheduler=snake_case__							,			scheduler=snake_case__							,			safety_checker=snake_case__							,			feature_extractor=snake_case__							,			watermarker=snake_case__							,			max_noise_level=snake_case__							,			)







	def __call__(      self				:			List[Any]							,			snake_case__				:			Union[str, List[str]]							,			snake_case__				:			Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]							,			snake_case__				:			int = 75							,			snake_case__				:			float = 9.0							,			snake_case__				:			int = 20							,			snake_case__				:			Optional[Union[str, List[str]]] = None							,			snake_case__				:			Optional[int] = 1							,			snake_case__				:			float = 0.0							,			snake_case__				:			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,			snake_case__				:			Optional[torch.FloatTensor] = None							,			snake_case__				:			Optional[np.ndarray] = None							,			snake_case__				:			Optional[np.ndarray] = None							,			snake_case__				:			Optional[str] = "pil"							,			snake_case__				:			bool = True							,			snake_case__				:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,			snake_case__				:			Optional[int] = 1							,			):
			"""simple docstring"""





			self.check_inputs(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ )

			# 2. Define call parameters
			if prompt is not None and isinstance(snake_case__							,			snake_case__ ):
					A				=1
			elif prompt is not None and isinstance(snake_case__							,			snake_case__ ):
					A				=len(snake_case__ )
			else:
					A				=prompt_embeds.shape[0]

			A				=self._execution_device
			# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
			# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
			# corresponds to doing no classifier free guidance.
			A				=guidance_scale > 1.0

			# 3. Encode input prompt
			A				=self._encode_prompt(
			    snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			prompt_embeds=snake_case__							,			negative_prompt_embeds=snake_case__							,			)

			A				=ORT_TO_PT_TYPE[str(text_embeddings.dtype )]

			# 4. Preprocess image
			A				=preprocess(snake_case__ )
			A				=image.cpu()

			# 5. set timesteps
			self.scheduler.set_timesteps(snake_case__							,			device=snake_case__ )
			A				=self.scheduler.timesteps

			# 5. Add noise to image
			A				=torch.tensor([noise_level]							,			dtype=torch.long							,			device=snake_case__ )
			A				=torch.randn(image.shape							,			generator=snake_case__							,			device=snake_case__							,			dtype=snake_case__ )
			A				=self.low_res_scheduler.add_noise(snake_case__							,			snake_case__							,			snake_case__ )

			A				=2 if do_classifier_free_guidance else 1
			A				=np.concatenate([image] * batch_multiplier * num_images_per_prompt )
			A				=np.concatenate([noise_level] * image.shape[0] )

			# 6. Prepare latent variables
			A			,    A				=image.shape[2:]
			A				=self.prepare_latents(
			    batch_size * num_images_per_prompt							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			)

			# 7. Check that sizes of image and latents match
			A				=image.shape[1]
			if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
					raise ValueError(
					    "Incorrect configuration settings! The config of `pipeline.unet` expects"
					    f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
					    f''' `num_channels_image`: {num_channels_image} '''
					    f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
					    " `pipeline.unet` or your `image` input." )

			# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
			A				=self.prepare_extra_step_kwargs(snake_case__							,			snake_case__ )

			A				=next(
			    (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")							,			"tensor(float)" )
			A				=ORT_TO_NP_TYPE[timestep_dtype]

			# 9. Denoising loop
			A				=len(snake_case__ ) - num_inference_steps * self.scheduler.order
			with self.progress_bar(total=snake_case__ ) as progress_bar:
					for i, t in enumerate(snake_case__ ):
							# expand the latents if we are doing classifier free guidance
							A				=np.concatenate([latents] * 2 ) if do_classifier_free_guidance else latents

							# concat latents, mask, masked_image_latents in the channel dimension
							A				=self.scheduler.scale_model_input(snake_case__							,			snake_case__ )
							A				=np.concatenate([latent_model_input, image]							,			axis=1 )

							# timestep to tensor
							A				=np.array([t]							,			dtype=snake_case__ )

							# predict the noise residual
							A				=self.unet(
							    sample=snake_case__							,			timestep=snake_case__							,			encoder_hidden_states=snake_case__							,			class_labels=noise_level.astype(np.intaa )							,			)[0]

							# perform guidance
							if do_classifier_free_guidance:
									A			,    A				=np.split(snake_case__							,			2 )
							A				=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							A				=self.scheduler.step(
							    torch.from_numpy(snake_case__ )							,			snake_case__							,			snake_case__							,			**snake_case__ ).prev_sample

							# call the callback, if provided
							if i == len(snake_case__ ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(snake_case__							,			snake_case__							,			snake_case__ )

        # 10. Post-processing
			A				=self.decode_latents(latents.float() )

			# 11. Convert to PIL
			if output_type == "pil":
					A				=self.numpy_to_pil(snake_case__ )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=snake_case__ )







	def        _a					(      self				:			List[str]							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=1 / 0.08_333 * latents
			A				=self.vae(latent_sample=snake_case__ )[0]
			A				=np.clip(image / 2 + 0.5							,			0							,			1 )
			A				=image.transpose((0, 2, 3, 1) )
			return image







	def        _a					(      self				:			int							,			snake_case__				:			Union[str, List[str]]							,			snake_case__				:			List[str]							,			snake_case__				:			Optional[int]							,			snake_case__				:			bool							,			snake_case__				:			Optional[str]							,			snake_case__				:			Optional[torch.FloatTensor] = None							,			snake_case__				:			Optional[torch.FloatTensor] = None							,			):
			"""simple docstring"""





			if prompt is not None and isinstance(snake_case__							,			snake_case__ ):
					A				=1
			elif prompt is not None and isinstance(snake_case__							,			snake_case__ ):
					A				=len(snake_case__ )
			else:
					A				=prompt_embeds.shape[0]

			if prompt_embeds is None:
					A				=self.tokenizer(
					    snake_case__							,			padding="max_length"							,			max_length=self.tokenizer.model_max_length							,			truncation=snake_case__							,			return_tensors="pt"							,			)
					A				=text_inputs.input_ids
					A				=self.tokenizer(snake_case__							,			padding="longest"							,			return_tensors="pt" ).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
					    snake_case__							,			snake_case__ ):
							A				=self.tokenizer.batch_decode(
							    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
							logger.warning(
							    "The following part of your input was truncated because CLIP can only handle sequences up to"
							    f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )

					# no positional arguments to text_encoder
					A				=self.text_encoder(
					    input_ids=text_input_ids.int().to(snake_case__ )							,			)
					A				=prompt_embeds[0]

			A			,    A			,    A				=prompt_embeds.shape
			# duplicate text embeddings for each generation per prompt, using mps friendly method
			A				=prompt_embeds.repeat(1							,			snake_case__ )
			A				=prompt_embeds.reshape(bs_embed * num_images_per_prompt							,			snake_case__							,			-1 )

			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance and negative_prompt_embeds is None:
					A				=42
					if negative_prompt is None:
							A				=[""] * batch_size
					elif type(snake_case__ ) is not type(snake_case__ ):
							raise TypeError(
							    f'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__ )} !='''
							    f''' {type(snake_case__ )}.''' )
					elif isinstance(snake_case__							,			snake_case__ ):
							A				=[negative_prompt]
					elif batch_size != len(snake_case__ ):
							raise ValueError(
							    f'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case__ )}, but `prompt`:'''
							    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
							    " the batch size of `prompt`." )
					else:
							A				=negative_prompt

					A				=text_input_ids.shape[-1]
					A				=self.tokenizer(
					    snake_case__							,			padding="max_length"							,			max_length=snake_case__							,			truncation=snake_case__							,			return_tensors="pt"							,			)

					# if hasattr(uncond_input, "attention_mask"):
					#     attention_mask = uncond_input.attention_mask.to(device)
					# else:
					#     attention_mask = None

					A				=self.text_encoder(
					    input_ids=uncond_input.input_ids.int().to(snake_case__ )							,			)
					A				=uncond_embeddings[0]

			if do_classifier_free_guidance:
					A				=uncond_embeddings.shape[1]
					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					A				=uncond_embeddings.repeat(1							,			snake_case__ )
					A				=uncond_embeddings.reshape(batch_size * num_images_per_prompt							,			snake_case__							,			-1 )

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					A				=np.concatenate([uncond_embeddings, prompt_embeds] )

			return prompt_embeds






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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		42
	_A  =		None


def    UpperCamelCase_ (	a_  ,				a_=0.999  ,				a_="cosine"  ,				)     ->List[Any]:
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

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






class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		[e.name for e in KarrasDiffusionSchedulers]
	_A  =		1







	@register_to_config
	def __init__(      self				:			Any							,			snake_case__				:			int = 10_00							,			snake_case__				:			float = 0.0_001							,			snake_case__				:			float = 0.02							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[Union[np.ndarray, List[float]]] = None							,			snake_case__				:			str = "epsilon"							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[bool] = False							,			snake_case__				:			str = "linspace"							,			snake_case__				:			int = 0							,			):
			"""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 )

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

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







	@property
	def        _a					(      self				:			Dict ):
			"""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				:			str							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor] ):
			"""simple docstring"""





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

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

			A				=True
			return sample







	def        _a					(      self				:			str							,			snake_case__				:			int							,			snake_case__				:			Union[str, torch.device] = None ):
			"""simple docstring"""





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

			A				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
			A				=np.log(snake_case__ )

			if self.config.interpolation_type == "linear":
					A				=np.interp(snake_case__							,			np.arange(0							,			len(snake_case__ ) )							,			snake_case__ )
			elif self.config.interpolation_type == "log_linear":
					A				=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:
					A				=self._convert_to_karras(in_sigmas=snake_case__							,			num_inference_steps=self.num_inference_steps )
					A				=np.array([self._sigma_to_t(snake_case__							,			snake_case__ ) for sigma in sigmas] )

			A				=np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
			A				=torch.from_numpy(snake_case__ ).to(device=snake_case__ )
			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(device=snake_case__ )







	def        _a					(      self				:			Dict							,			snake_case__				:			Dict							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=np.log(snake_case__ )

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

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

			A				=log_sigmas[low_idx]
			A				=log_sigmas[high_idx]

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

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







	def        _a					(      self				:			List[str]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Dict ):
			"""simple docstring"""





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

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







	def        _a					(      self				:			Any							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = float("inf" )							,			snake_case__				:			float = 1.0							,			snake_case__				:			Optional[torch.Generator] = None							,			snake_case__				:			bool = True							,			):
			"""simple docstring"""





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

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

			if isinstance(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )

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

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

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

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

			if gamma > 0:
					A				=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":
					A				=model_output
			elif self.config.prediction_type == "epsilon":
					A				=sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					A				=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
			A				=(sample - pred_original_sample) / sigma_hat

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

			A				=sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=snake_case__							,			pred_original_sample=snake_case__ )







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			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				=[(schedule_timesteps == t).nonzero().item() 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				:			Tuple ):
			"""simple docstring"""





			return self.config.num_train_timesteps




