

'''simple docstring'''


import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Dict:
	UpperCAmelCase_		:   List[Any]													=       TimesformerConfig()

	if "large" in model_name:
		UpperCAmelCase_		:   str													=       9_6

	if "hr" in model_name:
		UpperCAmelCase_		:   Tuple													=       1_6
		UpperCAmelCase_		:   List[str]													=       4_4_8

	UpperCAmelCase_		:   Optional[Any]													=       'huggingface/label-files'
	if "k400" in model_name:
		UpperCAmelCase_		:   List[str]													=       4_0_0
		UpperCAmelCase_		:   Optional[int]													=       'kinetics400-id2label.json'
	elif "k600" in model_name:
		UpperCAmelCase_		:   Any													=       6_0_0
		UpperCAmelCase_		:   List[str]													=       'kinetics600-id2label.json'
	elif "ssv2" in model_name:
		UpperCAmelCase_		:   List[Any]													=       1_7_4
		UpperCAmelCase_		:   Optional[int]													=       'something-something-v2-id2label.json'
	else:
		raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'	)
	UpperCAmelCase_		:   List[Any]													=       json.load(open(hf_hub_download(UpperCamelCase ,UpperCamelCase ,repo_type='dataset'	) ,'r'	)	)
	UpperCAmelCase_		:   Any													=       {int(UpperCamelCase	): v for k, v in idalabel.items()}
	UpperCAmelCase_		:   Union[str, Any]													=       idalabel
	UpperCAmelCase_		:   List[Any]													=       {v: k for k, v in idalabel.items()}

	return config







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Any:
	if "encoder." in name:
		UpperCAmelCase_		:   List[Any]													=       name.replace('encoder.' ,''	)
	if "cls_token" in name:
		UpperCAmelCase_		:   List[str]													=       name.replace('cls_token' ,'timesformer.embeddings.cls_token'	)
	if "pos_embed" in name:
		UpperCAmelCase_		:   Tuple													=       name.replace('pos_embed' ,'timesformer.embeddings.position_embeddings'	)
	if "time_embed" in name:
		UpperCAmelCase_		:   List[str]													=       name.replace('time_embed' ,'timesformer.embeddings.time_embeddings'	)
	if "patch_embed.proj" in name:
		UpperCAmelCase_		:   Optional[int]													=       name.replace('patch_embed.proj' ,'timesformer.embeddings.patch_embeddings.projection'	)
	if "patch_embed.norm" in name:
		UpperCAmelCase_		:   Dict													=       name.replace('patch_embed.norm' ,'timesformer.embeddings.norm'	)
	if "blocks" in name:
		UpperCAmelCase_		:   int													=       name.replace('blocks' ,'timesformer.encoder.layer'	)
	if "attn.proj" in name:
		UpperCAmelCase_		:   Optional[Any]													=       name.replace('attn.proj' ,'attention.output.dense'	)
	if "attn" in name and "bias" not in name and "temporal" not in name:
		UpperCAmelCase_		:   Tuple													=       name.replace('attn' ,'attention.self'	)
	if "attn" in name and "temporal" not in name:
		UpperCAmelCase_		:   Optional[Any]													=       name.replace('attn' ,'attention.attention'	)
	if "temporal_norm1" in name:
		UpperCAmelCase_		:   Union[str, Any]													=       name.replace('temporal_norm1' ,'temporal_layernorm'	)
	if "temporal_attn.proj" in name:
		UpperCAmelCase_		:   List[str]													=       name.replace('temporal_attn' ,'temporal_attention.output.dense'	)
	if "temporal_fc" in name:
		UpperCAmelCase_		:   Dict													=       name.replace('temporal_fc' ,'temporal_dense'	)
	if "norm1" in name and "temporal" not in name:
		UpperCAmelCase_		:   str													=       name.replace('norm1' ,'layernorm_before'	)
	if "norm2" in name:
		UpperCAmelCase_		:   List[Any]													=       name.replace('norm2' ,'layernorm_after'	)
	if "mlp.fc1" in name:
		UpperCAmelCase_		:   Optional[Any]													=       name.replace('mlp.fc1' ,'intermediate.dense'	)
	if "mlp.fc2" in name:
		UpperCAmelCase_		:   Optional[Any]													=       name.replace('mlp.fc2' ,'output.dense'	)
	if "norm.weight" in name and "fc" not in name and "temporal" not in name:
		UpperCAmelCase_		:   int													=       name.replace('norm.weight' ,'timesformer.layernorm.weight'	)
	if "norm.bias" in name and "fc" not in name and "temporal" not in name:
		UpperCAmelCase_		:   Any													=       name.replace('norm.bias' ,'timesformer.layernorm.bias'	)
	if "head" in name:
		UpperCAmelCase_		:   Tuple													=       name.replace('head' ,'classifier'	)

	return name







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Any:
	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_		:   Dict													=       orig_state_dict.pop(UpperCamelCase	)

		if key.startswith('model.'	):
			UpperCAmelCase_		:   Union[str, Any]													=       key.replace('model.' ,''	)

		if "qkv" in key:
			UpperCAmelCase_		:   Any													=       key.split('.'	)
			UpperCAmelCase_		:   Union[str, Any]													=       int(key_split[1]	)
			UpperCAmelCase_		:   List[Any]													=       'timesformer.encoder.layer.'
			if "temporal" in key:
				UpperCAmelCase_		:   List[Any]													=       '.temporal_attention.attention.qkv.'
			else:
				UpperCAmelCase_		:   int													=       '.attention.attention.qkv.'
			if "weight" in key:
				UpperCAmelCase_		:   Optional[Any]													=       val
			else:
				UpperCAmelCase_		:   Dict													=       val
		else:
			UpperCAmelCase_		:   int													=       val

	return orig_state_dict







def     SCREAMING_SNAKE_CASE(	)     ->     Optional[Any]:
	UpperCAmelCase_		:   Optional[int]													=       hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video' ,filename='eating_spaghetti.npy' ,repo_type='dataset'	)
	UpperCAmelCase_		:   Tuple													=       np.load(UpperCamelCase	)
	return list(UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     List[str]:
	UpperCAmelCase_		:   str													=       get_timesformer_config(UpperCamelCase	)

	UpperCAmelCase_		:   Dict													=       TimesformerForVideoClassification(UpperCamelCase	)

	# download original checkpoint, hosted on Google Drive
	UpperCAmelCase_		:   Any													=       'pytorch_model.bin'
	gdown.cached_download(UpperCamelCase ,UpperCamelCase ,quiet=UpperCamelCase	)
	UpperCAmelCase_		:   List[str]													=       torch.load(UpperCamelCase ,map_location='cpu'	)
	if "model" in files:
		UpperCAmelCase_		:   Union[str, Any]													=       files['model']
	elif "module" in files:
		UpperCAmelCase_		:   str													=       files['module']
	else:
		UpperCAmelCase_		:   List[str]													=       files['model_state']
	UpperCAmelCase_		:   List[Any]													=       convert_state_dict(UpperCamelCase ,UpperCamelCase	)

	model.load_state_dict(UpperCamelCase	)
	model.eval()

	# verify model on basic input
	UpperCAmelCase_		:   int													=       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5]	)
	UpperCAmelCase_		:   Any													=       prepare_video()
	UpperCAmelCase_		:   Union[str, Any]													=       image_processor(video[:8] ,return_tensors='pt'	)

	UpperCAmelCase_		:   Tuple													=       model(**UpperCamelCase	)
	UpperCAmelCase_		:   Optional[Any]													=       outputs.logits

	UpperCAmelCase_		:   Optional[int]													=       [
	    # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
	    'timesformer-base-finetuned-k400',
	    'timesformer-large-finetuned-k400',
	    'timesformer-hr-finetuned-k400',
	    # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
	    'timesformer-base-finetuned-k600',
	    'timesformer-large-finetuned-k600',
	    'timesformer-hr-finetuned-k600',
	    # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
	    'timesformer-base-finetuned-ssv2',
	    'timesformer-large-finetuned-ssv2',
	    'timesformer-hr-finetuned-ssv2',
	]

	# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
	if model_name == "timesformer-base-finetuned-k400":
		UpperCAmelCase_		:   List[str]													=       torch.Size([1, 4_0_0]	)
		UpperCAmelCase_		:   Any													=       torch.tensor([-0.3016, -0.7713, -0.4205]	)
	elif model_name == "timesformer-base-finetuned-k600":
		UpperCAmelCase_		:   Union[str, Any]													=       torch.Size([1, 6_0_0]	)
		UpperCAmelCase_		:   int													=       torch.tensor([-0.7267, -0.7466, 3.2404]	)
	elif model_name == "timesformer-base-finetuned-ssv2":
		UpperCAmelCase_		:   Any													=       torch.Size([1, 1_7_4]	)
		UpperCAmelCase_		:   List[Any]													=       torch.tensor([-0.9059, 0.6433, -3.1457]	)
	elif model_name == "timesformer-large-finetuned-k400":
		UpperCAmelCase_		:   Union[str, Any]													=       torch.Size([1, 4_0_0]	)
		UpperCAmelCase_		:   Optional[int]													=       torch.tensor([0, 0, 0]	)
	elif model_name == "timesformer-large-finetuned-k600":
		UpperCAmelCase_		:   List[str]													=       torch.Size([1, 6_0_0]	)
		UpperCAmelCase_		:   Optional[Any]													=       torch.tensor([0, 0, 0]	)
	elif model_name == "timesformer-large-finetuned-ssv2":
		UpperCAmelCase_		:   Optional[int]													=       torch.Size([1, 1_7_4]	)
		UpperCAmelCase_		:   int													=       torch.tensor([0, 0, 0]	)
	elif model_name == "timesformer-hr-finetuned-k400":
		UpperCAmelCase_		:   Optional[Any]													=       torch.Size([1, 4_0_0]	)
		UpperCAmelCase_		:   Union[str, Any]													=       torch.tensor([-0.9617, -3.7311, -3.7708]	)
	elif model_name == "timesformer-hr-finetuned-k600":
		UpperCAmelCase_		:   Dict													=       torch.Size([1, 6_0_0]	)
		UpperCAmelCase_		:   Optional[Any]													=       torch.tensor([2.5273, 0.7127, 1.8848]	)
	elif model_name == "timesformer-hr-finetuned-ssv2":
		UpperCAmelCase_		:   Optional[int]													=       torch.Size([1, 1_7_4]	)
		UpperCAmelCase_		:   Optional[Any]													=       torch.tensor([-3.6756, -0.7513, 0.7180]	)
	else:
		raise ValueError(f"""Model name not supported. Should be one of {model_names}"""	)

	# verify logits
	assert logits.shape == expected_shape
	assert torch.allclose(logits[0, :3] ,UpperCamelCase ,atol=1e-4	)
	print('Logits ok!'	)

	if pytorch_dump_folder_path is not None:
		print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""	)
		image_processor.save_pretrained(UpperCamelCase	)
		model.save_pretrained(UpperCamelCase	)

	if push_to_hub:
		print('Pushing to the hub...'	)
		model.push_to_hub(f"""fcakyon/{model_name}"""	)


if __name__ == "__main__":
						lowerCAmelCase__      				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download",
						    type=str,
						    help=(
						        "URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"
						        " download link."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--model_name", default="timesformer-base-finetuned-k400", type=str, help="Name of the model.")
						parser.add_argument(
						    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
						)

						lowerCAmelCase__      				=			parser.parse_args()
						convert_timesformer_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
						)









'''simple docstring'''


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


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	UpperCAmelCase_		:   Optional[int]													=       []
	for i in range(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[Any]													=       i / num_diffusion_timesteps
		UpperCAmelCase_		:   Any													=       (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase	) / alpha_bar_fn(UpperCamelCase	) ,UpperCamelCase	)	)
	return torch.tensor(UpperCamelCase ,dtype=torch.floataa	)







class        lowercase    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       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.
									UpperCAmelCase_		:   List[Any]													=       (
									    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
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

								UpperCAmelCase_		:   List[Any]													=       1.0 - self.betas
								UpperCAmelCase_		:   Union[str, Any]													=       torch.cumprod(self.alphas     ,      dim=0)

								UpperCAmelCase_		:   Union[str, Any]													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   int													=       np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case)

								# setable values
								UpperCAmelCase_		:   List[str]													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       np.linspace(0     ,      num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[::-1].copy()
								UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       False


							@property
							def     _snake_case				(      self)				->					Optional[Any]:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

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


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       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":
									UpperCAmelCase_		:   List[Any]													=       np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									UpperCAmelCase_		:   Any													=       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
									UpperCAmelCase_		:   List[Any]													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									UpperCAmelCase_		:   List[Any]													=       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
									UpperCAmelCase_		:   Any													=       (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'.""")

								UpperCAmelCase_		:   Any													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   Dict													=       np.interp(_snake_case     ,      np.arange(0     ,      len(_snake_case))     ,      _snake_case)
								UpperCAmelCase_		:   Optional[int]													=       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case).to(device=_snake_case)
								if str(_snake_case).startswith('mps'):
									# mps does not support float64
									UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case).to(_snake_case     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       torch.from_numpy(_snake_case).to(device=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								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):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   List[Any]													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   int													=       self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									UpperCAmelCase_		:   Tuple													=       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCAmelCase_		:   str													=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`""")

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								UpperCAmelCase_		:   Dict													=       (sample - pred_original_sample) / sigma

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

								UpperCAmelCase_		:   Any													=       model_output.device
								UpperCAmelCase_		:   List[Any]													=       randn_tensor(model_output.shape     ,      dtype=model_output.dtype     ,      device=_snake_case     ,      generator=_snake_case)

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   List[str]													=       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
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[Any]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   str													=       timesteps.to(original_samples.device)

								UpperCAmelCase_		:   Optional[Any]													=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								UpperCAmelCase_		:   Any													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   Union[str, Any]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





							def __len__(      self)				->					Optional[Any]:
								return self.config.num_train_timesteps






