
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase							=			logging.get_logger(__name__)


def       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	OrderedDict()
   for key, value in state_dict.items():
      if key.startswith('''module.encoder'''		):
         SCREAMING_SNAKE_CASE_     =	key.replace('''module.encoder''',   '''glpn.encoder'''		)
      if key.startswith('''module.decoder'''		):
         SCREAMING_SNAKE_CASE_     =	key.replace('''module.decoder''',   '''decoder.stages'''		)
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         SCREAMING_SNAKE_CASE_     =	key[key.find('''patch_embed'''		) + len('''patch_embed'''		)]
         SCREAMING_SNAKE_CASE_     =	key.replace(F'''patch_embed{idx}''',   F'''patch_embeddings.{int(snake_case__		)-1}'''		)
      if "norm" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''norm''',   '''layer_norm'''		)
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         SCREAMING_SNAKE_CASE_     =	key[key.find('''glpn.encoder.layer_norm'''		) + len('''glpn.encoder.layer_norm'''		)]
         SCREAMING_SNAKE_CASE_     =	key.replace(F'''layer_norm{idx}''',   F'''layer_norm.{int(snake_case__		)-1}'''		)
      if "layer_norm1" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''layer_norm1''',   '''layer_norm_1'''		)
      if "layer_norm2" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''layer_norm2''',   '''layer_norm_2'''		)
      if "block" in key:
         # replace for example block1 by block.0
         SCREAMING_SNAKE_CASE_     =	key[key.find('''block'''		) + len('''block'''		)]
         SCREAMING_SNAKE_CASE_     =	key.replace(F'''block{idx}''',   F'''block.{int(snake_case__		)-1}'''		)
      if "attn.q" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''attn.q''',   '''attention.self.query'''		)
      if "attn.proj" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''attn.proj''',   '''attention.output.dense'''		)
      if "attn" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''attn''',   '''attention.self'''		)
      if "fc1" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''fc1''',   '''dense1'''		)
      if "fc2" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''fc2''',   '''dense2'''		)
      if "linear_pred" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''linear_pred''',   '''classifier'''		)
      if "linear_fuse" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''linear_fuse.conv''',   '''linear_fuse'''		)
         SCREAMING_SNAKE_CASE_     =	key.replace('''linear_fuse.bn''',   '''batch_norm'''		)
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         SCREAMING_SNAKE_CASE_     =	key[key.find('''linear_c'''		) + len('''linear_c'''		)]
         SCREAMING_SNAKE_CASE_     =	key.replace(F'''linear_c{idx}''',   F'''linear_c.{int(snake_case__		)-1}'''		)
      if "bot_conv" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''bot_conv''',   '''0.convolution'''		)
      if "skip_conv1" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''skip_conv1''',   '''1.convolution'''		)
      if "skip_conv2" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''skip_conv2''',   '''2.convolution'''		)
      if "fusion1" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''fusion1''',   '''1.fusion'''		)
      if "fusion2" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''fusion2''',   '''2.fusion'''		)
      if "fusion3" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''fusion3''',   '''3.fusion'''		)
      if "fusion" in key and "conv" in key:
         SCREAMING_SNAKE_CASE_     =	key.replace('''conv''',   '''convolutional_layer'''		)
      if key.startswith('''module.last_layer_depth'''		):
         SCREAMING_SNAKE_CASE_     =	key.replace('''module.last_layer_depth''',   '''head.head'''		)
      SCREAMING_SNAKE_CASE_     =	value

   return new_state_dict


def       A__      (    __lowerCamelCase,   __lowerCamelCase		):
   # for each of the encoder blocks:
   for i in range(config.num_encoder_blocks		):
      for j in range(config.depths[i]		):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         SCREAMING_SNAKE_CASE_     =	state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
         SCREAMING_SNAKE_CASE_     =	state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''		)
         # next, add keys and values (in that order) to the state dict
         SCREAMING_SNAKE_CASE_     =	kv_weight[
             : config.hidden_sizes[i], :
         ]
         SCREAMING_SNAKE_CASE_     =	kv_bias[: config.hidden_sizes[i]]
         SCREAMING_SNAKE_CASE_     =	kv_weight[
             config.hidden_sizes[i] :, :
         ]
         SCREAMING_SNAKE_CASE_     =	kv_bias[config.hidden_sizes[i] :]


def       A__      (    ):
   SCREAMING_SNAKE_CASE_     =	'http://images.cocodataset.org/val2017/000000039769.jpg'
   SCREAMING_SNAKE_CASE_     =	Image.open(requests.get(snake_case__,   stream=snake_case__		).raw		)

   return image


@torch.no_grad()
def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase=False,   __lowerCamelCase=None		):
   SCREAMING_SNAKE_CASE_     =	GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12],   decoder_hidden_size=64,   depths=[3, 8, 27, 3]		)

   # load image processor (only resize + rescale)
   SCREAMING_SNAKE_CASE_     =	GLPNImageProcessor()

   # prepare image
   SCREAMING_SNAKE_CASE_     =	prepare_img()
   SCREAMING_SNAKE_CASE_     =	image_processor(images=snake_case__,   return_tensors='''pt'''		).pixel_values

   logger.info('''Converting model...'''		)

   # load original state dict
   SCREAMING_SNAKE_CASE_     =	torch.load(snake_case__,   map_location=torch.device('''cpu'''		)		)

   # rename keys
   SCREAMING_SNAKE_CASE_     =	rename_keys(snake_case__		)

   # key and value matrices need special treatment
   read_in_k_v(snake_case__,   snake_case__		)

   # create HuggingFace model and load state dict
   SCREAMING_SNAKE_CASE_     =	GLPNForDepthEstimation(snake_case__		)
   model.load_state_dict(snake_case__		)
   model.eval()

   # forward pass
   SCREAMING_SNAKE_CASE_     =	model(snake_case__		)
   SCREAMING_SNAKE_CASE_     =	outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         SCREAMING_SNAKE_CASE_     =	torch.tensor(
             [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]		)
      elif "kitti" in model_name:
         SCREAMING_SNAKE_CASE_     =	torch.tensor(
             [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]		)
      else:
         raise ValueError(F'''Unknown model name: {model_name}'''		)

      SCREAMING_SNAKE_CASE_     =	torch.Size([1, 4_80, 6_40]		)

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3],   snake_case__,   atol=1E-4		)
      print('''Looks ok!'''		)

   # finally, push to hub if required
   if push_to_hub:
      logger.info('''Pushing model and image processor to the hub...'''		)
      model.push_to_hub(
          repo_path_or_name=Path(snake_case__,   snake_case__		),   organization='''nielsr''',   commit_message='''Add model''',   use_temp_dir=snake_case__,   )
      image_processor.push_to_hub(
          repo_path_or_name=Path(snake_case__,   snake_case__		),   organization='''nielsr''',   commit_message='''Add image processor''',   use_temp_dir=snake_case__,   )


if __name__ == "__main__":
   __UpperCAmelCase							=			argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path",
       default=None,
       type=str,
       help="Path to the original PyTorch checkpoint (.pth file).",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
   )
   parser.add_argument(
       "--model_name",
       default="glpn-kitti",
       type=str,
       help="Name of the model in case you\'re pushing to the hub.",
   )
   __UpperCAmelCase							=			parser.parse_args()
   convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any=0.999       ,     snake_case__       :    List[Any]="cosine"       ,     )	->     Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case__       :    Optional[Any]	):
         return math.exp(t * -12.0	)

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

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



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[Any]        =  [e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase__				:					List[str]        =  2
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = 0.0_00_85,			SCREAMING_SNAKE_CASE_ = 0.0_12,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "linspace",			SCREAMING_SNAKE_CASE_ = 0,			)     ->		List[str]:
          if trained_betas is not None:
             UpperCamelCase    :  Union[str, Any]							=	torch.tensor(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "linear":
             UpperCamelCase    :  List[Any]							=	torch.linspace(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase    :  int							=	(
                 torch.linspace(beta_start**0.5,			beta_end**0.5,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       ) ** 2
             )
          elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase    :  Optional[int]							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )
          else:
             raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

          UpperCamelCase    :  Optional[int]							=	1.0 - self.betas
          UpperCamelCase    :  int							=	torch.cumprod(self.alphas,			dim=0       )

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		str:
          if schedule_timesteps is None:
             UpperCamelCase    :  Union[str, Any]							=	self.timesteps

          UpperCamelCase    :  Dict							=	(schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter       ) == 0:
             UpperCamelCase    :  Optional[int]							=	1 if len(SCREAMING_SNAKE_CASE_       ) > 1 else 0
          else:
             UpperCamelCase    :  str							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
             UpperCamelCase    :  int							=	self._index_counter[timestep_int]

          return indices[pos].item()
       @property
       def 					snake_case_       (			self       )     ->		List[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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          UpperCamelCase    :  Optional[int]							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          if self.state_in_first_order:
             UpperCamelCase    :  Dict							=	self.sigmas[step_index]
          else:
             UpperCamelCase    :  int							=	self.sigmas_interpol[step_index]

          UpperCamelCase    :  Any							=	sample / ((sigma**2 + 1) ** 0.5)
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	num_inference_steps

          UpperCamelCase    :  List[Any]							=	num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
             UpperCamelCase    :  int							=	np.linspace(0,			num_train_timesteps - 1,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             UpperCamelCase    :  List[Any]							=	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    :  Optional[Any]							=	(np.arange(0,			SCREAMING_SNAKE_CASE_       ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_       )
             timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
             UpperCamelCase    :  Optional[int]							=	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(SCREAMING_SNAKE_CASE_,			0,			-step_ratio       )).round().copy().astype(SCREAMING_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    :  Tuple							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	np.interp(SCREAMING_SNAKE_CASE_,			np.arange(0,			len(SCREAMING_SNAKE_CASE_       )       ),			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )

          # interpolate sigmas
          UpperCamelCase    :  Union[str, Any]							=	sigmas.log().lerp(sigmas.roll(1       ).log(),			0.5       ).exp()

          UpperCamelCase    :  Any							=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          UpperCamelCase    :  Optional[Any]							=	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             # mps does not support float64
             UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )

          # interpolate timesteps
          UpperCamelCase    :  int							=	self.sigma_to_t(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=timesteps.dtype       )
          UpperCamelCase    :  List[str]							=	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),			dim=-1       ).flatten()

          UpperCamelCase    :  Optional[Any]							=	torch.cat([timesteps[:1], interleaved_timesteps]       )

          UpperCamelCase    :  Optional[Any]							=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase    :  Dict							=	defaultdict(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # get log sigma
          UpperCamelCase    :  List[Any]							=	sigma.log()

          # get distribution
          UpperCamelCase    :  Optional[int]							=	log_sigma - self.log_sigmas[:, None]

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

          UpperCamelCase    :  List[str]							=	self.log_sigmas[low_idx]
          UpperCamelCase    :  Optional[Any]							=	self.log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase    :  int							=	(low - log_sigma) / (low - high)
          UpperCamelCase    :  Tuple							=	w.clamp(0,			1       )

          # transform interpolation to time range
          UpperCamelCase    :  List[str]							=	(1 - w) * low_idx + w * high_idx
          UpperCamelCase    :  Dict							=	t.view(sigma.shape       )
          return t
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self.sample is None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[SchedulerOutput, Tuple]:
          UpperCamelCase    :  str							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          # advance index counter by 1
          UpperCamelCase    :  Optional[int]							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
             UpperCamelCase    :  Tuple							=	self.sigmas[step_index]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index + 1]
             UpperCamelCase    :  Optional[int]							=	self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             UpperCamelCase    :  str							=	self.sigmas[step_index - 1]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index]
             UpperCamelCase    :  Any							=	self.sigmas[step_index]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          UpperCamelCase    :  Dict							=	0
          UpperCamelCase    :  Any							=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
             UpperCamelCase    :  Any							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  List[Any]							=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             UpperCamelCase    :  Union[str, Any]							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  Optional[int]							=	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
          elif self.config.prediction_type == "sample":
             raise NotImplementedError('prediction_type not implemented yet: sample'       )
          else:
             raise ValueError(
                 F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

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

             # store for 2nd order step
             UpperCamelCase    :  Tuple							=	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase    :  Union[str, Any]							=	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase    :  Dict							=	sigma_next - sigma_hat

             UpperCamelCase    :  Any							=	self.sample
             UpperCamelCase    :  str							=	None

          UpperCamelCase    :  Any							=	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )
       def 					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
          UpperCamelCase    :  Optional[Any]							=	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
             UpperCamelCase    :  List[str]							=	self.timesteps.to(original_samples.device,			dtype=torch.floataa       )
             UpperCamelCase    :  str							=	timesteps.to(original_samples.device,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	self.timesteps.to(original_samples.device       )
             UpperCamelCase    :  int							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  str							=	[self.index_for_timestep(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) for t in timesteps]

          UpperCamelCase    :  List[Any]							=	sigmas[step_indices].flatten()
          while len(sigma.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  int							=	sigma.unsqueeze(-1       )

          UpperCamelCase    :  Any							=	original_samples + noise * sigma
          return noisy_samples






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



