






'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     :   Tuple	      =		logging.get_logger(__name__)
def     A__      (					__lowerCAmelCase		:      int    ):
						lowerCamelCase__								=	DPTConfig(embedding_type="""hybrid"""    )

						if "large" in checkpoint_url:
												lowerCamelCase__								=	1024
												lowerCamelCase__								=	4096
												lowerCamelCase__								=	24
												lowerCamelCase__								=	16
												lowerCamelCase__								=	[5, 11, 17, 23]
												lowerCamelCase__								=	[256, 512, 1024, 1024]
												lowerCamelCase__								=	(1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												lowerCamelCase__								=	768
												lowerCamelCase__								=	[1, 1, 1, 0.5]
												lowerCamelCase__								=	[256, 512, 768, 768]
												lowerCamelCase__								=	150
												lowerCamelCase__								=	16
												lowerCamelCase__								=	(1, 384, 384)
												lowerCamelCase__								=	False
												lowerCamelCase__								=	"""project"""

						if "ade" in checkpoint_url:
												lowerCamelCase__								=	True
												lowerCamelCase__								=	768
												lowerCamelCase__								=	[1, 1, 1, 0.5]
												lowerCamelCase__								=	150
												lowerCamelCase__								=	16
												lowerCamelCase__								=	"""huggingface/label-files"""
												lowerCamelCase__								=	"""ade20k-id2label.json"""
												lowerCamelCase__								=	json.load(open(cached_download(hf_hub_url(__lowerCAmelCase ,     __lowerCAmelCase ,     repo_type="""dataset"""    )    ) ,     """r"""    )    )
												lowerCamelCase__								=	{int(__lowerCAmelCase    ): v for k, v in idalabel.items()}
												lowerCamelCase__								=	idalabel
												lowerCamelCase__								=	{v: k for k, v in idalabel.items()}
												lowerCamelCase__								=	[1, 150, 480, 480]

						return config, expected_shape
def     A__      (					__lowerCAmelCase		:      Optional[int]    ):
						lowerCamelCase__								=	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase ,     __lowerCAmelCase    )
def     A__      (					__lowerCAmelCase		:      List[Any]    ):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												lowerCamelCase__								=	name.replace("""pretrained.model""" ,     """dpt.encoder"""    )
						if "pretrained.model" in name:
												lowerCamelCase__								=	name.replace("""pretrained.model""" ,     """dpt.embeddings"""    )
						if "patch_embed" in name:
												lowerCamelCase__								=	name.replace("""patch_embed""" ,     """"""    )
						if "pos_embed" in name:
												lowerCamelCase__								=	name.replace("""pos_embed""" ,     """position_embeddings"""    )
						if "attn.proj" in name:
												lowerCamelCase__								=	name.replace("""attn.proj""" ,     """attention.output.dense"""    )
						if "proj" in name and "project" not in name:
												lowerCamelCase__								=	name.replace("""proj""" ,     """projection"""    )
						if "blocks" in name:
												lowerCamelCase__								=	name.replace("""blocks""" ,     """layer"""    )
						if "mlp.fc1" in name:
												lowerCamelCase__								=	name.replace("""mlp.fc1""" ,     """intermediate.dense"""    )
						if "mlp.fc2" in name:
												lowerCamelCase__								=	name.replace("""mlp.fc2""" ,     """output.dense"""    )
						if "norm1" in name and "backbone" not in name:
												lowerCamelCase__								=	name.replace("""norm1""" ,     """layernorm_before"""    )
						if "norm2" in name and "backbone" not in name:
												lowerCamelCase__								=	name.replace("""norm2""" ,     """layernorm_after"""    )
						if "scratch.output_conv" in name:
												lowerCamelCase__								=	name.replace("""scratch.output_conv""" ,     """head"""    )
						if "scratch" in name:
												lowerCamelCase__								=	name.replace("""scratch""" ,     """neck"""    )
						if "layer1_rn" in name:
												lowerCamelCase__								=	name.replace("""layer1_rn""" ,     """convs.0"""    )
						if "layer2_rn" in name:
												lowerCamelCase__								=	name.replace("""layer2_rn""" ,     """convs.1"""    )
						if "layer3_rn" in name:
												lowerCamelCase__								=	name.replace("""layer3_rn""" ,     """convs.2"""    )
						if "layer4_rn" in name:
												lowerCamelCase__								=	name.replace("""layer4_rn""" ,     """convs.3"""    )
						if "refinenet" in name:
												lowerCamelCase__								=	int(name[len("""neck.refinenet"""    ) : len("""neck.refinenet"""    ) + 1]    )
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												lowerCamelCase__								=	name.replace(F'''refinenet{layer_idx}''' ,     F'''fusion_stage.layers.{abs(layer_idx-4    )}'''    )
						if "out_conv" in name:
												lowerCamelCase__								=	name.replace("""out_conv""" ,     """projection"""    )
						if "resConfUnit1" in name:
												lowerCamelCase__								=	name.replace("""resConfUnit1""" ,     """residual_layer1"""    )
						if "resConfUnit2" in name:
												lowerCamelCase__								=	name.replace("""resConfUnit2""" ,     """residual_layer2"""    )
						if "conv1" in name:
												lowerCamelCase__								=	name.replace("""conv1""" ,     """convolution1"""    )
						if "conv2" in name:
												lowerCamelCase__								=	name.replace("""conv2""" ,     """convolution2"""    )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess1.0.project.0""" ,     """neck.reassemble_stage.readout_projects.0.0"""    )
						if "pretrained.act_postprocess2.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess2.0.project.0""" ,     """neck.reassemble_stage.readout_projects.1.0"""    )
						if "pretrained.act_postprocess3.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess3.0.project.0""" ,     """neck.reassemble_stage.readout_projects.2.0"""    )
						if "pretrained.act_postprocess4.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess4.0.project.0""" ,     """neck.reassemble_stage.readout_projects.3.0"""    )

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess1.3""" ,     """neck.reassemble_stage.layers.0.projection"""    )
						if "pretrained.act_postprocess1.4" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess1.4""" ,     """neck.reassemble_stage.layers.0.resize"""    )
						if "pretrained.act_postprocess2.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess2.3""" ,     """neck.reassemble_stage.layers.1.projection"""    )
						if "pretrained.act_postprocess2.4" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess2.4""" ,     """neck.reassemble_stage.layers.1.resize"""    )
						if "pretrained.act_postprocess3.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess3.3""" ,     """neck.reassemble_stage.layers.2.projection"""    )
						if "pretrained.act_postprocess4.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess4.3""" ,     """neck.reassemble_stage.layers.3.projection"""    )
						if "pretrained.act_postprocess4.4" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess4.4""" ,     """neck.reassemble_stage.layers.3.resize"""    )
						if "pretrained" in name:
												lowerCamelCase__								=	name.replace("""pretrained""" ,     """dpt"""    )
						if "bn" in name:
												lowerCamelCase__								=	name.replace("""bn""" ,     """batch_norm"""    )
						if "head" in name:
												lowerCamelCase__								=	name.replace("""head""" ,     """head.head"""    )
						if "encoder.norm" in name:
												lowerCamelCase__								=	name.replace("""encoder.norm""" ,     """layernorm"""    )
						if "auxlayer" in name:
												lowerCamelCase__								=	name.replace("""auxlayer""" ,     """auxiliary_head.head"""    )
						if "backbone" in name:
												lowerCamelCase__								=	name.replace("""backbone""" ,     """backbone.bit.encoder"""    )

						if ".." in name:
												lowerCamelCase__								=	name.replace("""..""" ,     """."""    )

						if "stem.conv" in name:
												lowerCamelCase__								=	name.replace("""stem.conv""" ,     """bit.embedder.convolution"""    )
						if "blocks" in name:
												lowerCamelCase__								=	name.replace("""blocks""" ,     """layers"""    )
						if "convolution" in name and "backbone" in name:
												lowerCamelCase__								=	name.replace("""convolution""" ,     """conv"""    )
						if "layer" in name and "backbone" in name:
												lowerCamelCase__								=	name.replace("""layer""" ,     """layers"""    )
						if "backbone.bit.encoder.bit" in name:
												lowerCamelCase__								=	name.replace("""backbone.bit.encoder.bit""" ,     """backbone.bit"""    )
						if "embedder.conv" in name:
												lowerCamelCase__								=	name.replace("""embedder.conv""" ,     """embedder.convolution"""    )
						if "backbone.bit.encoder.stem.norm" in name:
												lowerCamelCase__								=	name.replace("""backbone.bit.encoder.stem.norm""" ,     """backbone.bit.embedder.norm"""    )
						return name
def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      int    ):
						for i in range(config.num_hidden_layers    ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCamelCase__								=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''    )
												lowerCamelCase__								=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''    )
												# next, add query, keys and values (in that order) to the state dict
												lowerCamelCase__								=	in_proj_weight[: config.hidden_size, :]
												lowerCamelCase__								=	in_proj_bias[: config.hidden_size]
												lowerCamelCase__								=	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCamelCase__								=	in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCamelCase__								=	in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCamelCase__								=	in_proj_bias[-config.hidden_size :]
def     A__      (					):
						lowerCamelCase__								=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCamelCase__								=	Image.open(requests.get(__lowerCAmelCase ,     stream=__lowerCAmelCase    ).raw    )
						return im
@torch.no_grad()
def     A__      (					__lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      Any    ):
						lowerCamelCase__							,      lowerCamelCase__								=	get_dpt_config(__lowerCAmelCase    )
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						lowerCamelCase__								=	torch.load(__lowerCAmelCase ,     map_location="""cpu"""    )
						# remove certain keys
						remove_ignore_keys_(__lowerCAmelCase    )
						# rename keys
						for key in state_dict.copy().keys():
												lowerCamelCase__								=	state_dict.pop(__lowerCAmelCase    )
												lowerCamelCase__								=	val
						# read in qkv matrices
						read_in_q_k_v(__lowerCAmelCase ,     __lowerCAmelCase    )

						# load HuggingFace model
						lowerCamelCase__								=	DPTForSemanticSegmentation(__lowerCAmelCase    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase    )
						model.load_state_dict(__lowerCAmelCase    )
						model.eval()

						# Check outputs on an image
						lowerCamelCase__								=	480 if """ade""" in checkpoint_url else 384
						lowerCamelCase__								=	DPTImageProcessor(size=__lowerCAmelCase    )

						lowerCamelCase__								=	prepare_img()
						lowerCamelCase__								=	image_processor(__lowerCAmelCase ,     return_tensors="""pt"""    )

						# forward pass
						lowerCamelCase__								=	model(**__lowerCAmelCase    ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase    ).predicted_depth

						if show_prediction:
												lowerCamelCase__								=	(
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1    ) ,     size=(image.size[1], image.size[0]) ,     mode="""bicubic""" ,     align_corners=__lowerCAmelCase ,     )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255    ).show()

						if pytorch_dump_folder_path is not None:
												Path(__lowerCAmelCase    ).mkdir(exist_ok=__lowerCAmelCase    )
												print(F'''Saving model to {pytorch_dump_folder_path}'''    )
												model.save_pretrained(__lowerCAmelCase    )
												print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
												image_processor.save_pretrained(__lowerCAmelCase    )

						if push_to_hub:
												model.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )
												image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )


if __name__ == "__main__":
							UpperCamelCase     :   Any	      =		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint_url',
							    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
							    type=str,
							    help='URL of the original DPT checkpoint you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							)
							parser.add_argument(
							    '--model_name',
							    default='dpt-large',
							    type=str,
							    help='Name of the model, in case you\'re pushing to the hub.',
							)
							parser.add_argument(
							    '--show_prediction',
							    action='store_true',
							)

							UpperCamelCase     :   List[str]	      =		parser.parse_args()
							convert_dpt_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
							)





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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=0.9_99   ,						_lowerCAmelCase="cosine"   ,						)    ->						int:


						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCAmelCase			):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

						elif alpha_transform_type == "exp":

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

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

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






class      UpperCamelCase(	_a  ,				_a ):
					snake_case_		:					Tuple    					=						[e.name for e in KarrasDiffusionSchedulers]
					snake_case_		:					Union[str, Any]    					=						2


					@register_to_config
					def __init__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		int = 1_0_0_0  ,     SCREAMING_SNAKE_CASE						:		float = 0.00085  ,     SCREAMING_SNAKE_CASE						:		float = 0.012  ,     SCREAMING_SNAKE_CASE						:		str = "linear"  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[np.ndarray, List[float]]] = None  ,     SCREAMING_SNAKE_CASE						:		str = "epsilon"  ,     SCREAMING_SNAKE_CASE						:		str = "linspace"  ,     SCREAMING_SNAKE_CASE						:		int = 0  ,     )      ->						Optional[int]:




											'''simple docstring'''

											if trained_betas is not None:
																	__snake_case				=		torch.tensor(SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											elif beta_schedule == "linear":
																	__snake_case				=		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.
																	__snake_case				=		(
																	    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
																	__snake_case				=		betas_for_alpha_bar(SCREAMING_SNAKE_CASE    )
											else:
																	raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

											__snake_case				=		1.0 - self.betas
											__snake_case				=		torch.cumprod(self.alphas  ,     dim=0    )

											#  set all values
											self.set_timesteps(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		int=None    )      ->						Optional[int]:




											'''simple docstring'''

											if schedule_timesteps is None:
																	__snake_case				=		self.timesteps

											__snake_case				=		(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:
																	__snake_case				=		1 if len(SCREAMING_SNAKE_CASE    ) > 1 else 0
											else:
																	__snake_case				=		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE    ) else timestep
																	__snake_case				=		self._index_counter[timestep_int]

											return indices[pos].item()


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						int:




											'''simple docstring'''

											if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		Union[float, torch.FloatTensor]  ,     )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		self.index_for_timestep(SCREAMING_SNAKE_CASE    )

											if self.state_in_first_order:
																	__snake_case				=		self.sigmas[step_index]
											else:
																	__snake_case				=		self.sigmas_interpol[step_index]

											__snake_case				=		sample / ((sigma**2 + 1) ** 0.5)
											return sample


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, torch.device] = None  ,     SCREAMING_SNAKE_CASE						:		Optional[int] = None  ,     )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		num_inference_steps

											__snake_case				=		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":
																	__snake_case				=		np.linspace(0  ,     num_train_timesteps - 1  ,     SCREAMING_SNAKE_CASE  ,     dtype=SCREAMING_SNAKE_CASE    )[::-1].copy()
											elif self.config.timestep_spacing == "leading":
																	__snake_case				=		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
																	__snake_case				=		(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":
																	__snake_case				=		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
																	__snake_case				=		(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\'.'''    )

											__snake_case				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											__snake_case				=		torch.from_numpy(np.log(SCREAMING_SNAKE_CASE    )    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		np.interp(SCREAMING_SNAKE_CASE  ,     np.arange(0  ,     len(SCREAMING_SNAKE_CASE    )    )  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
											__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(device=SCREAMING_SNAKE_CASE    )

											# interpolate sigmas
											__snake_case				=		sigmas.log().lerp(sigmas.roll(1    ).log()  ,     0.5    ).exp()

											__snake_case				=		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
											__snake_case				=		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
																	__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											else:
																	__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(SCREAMING_SNAKE_CASE    )

											# interpolate timesteps
											__snake_case				=		self.sigma_to_t(SCREAMING_SNAKE_CASE    ).to(SCREAMING_SNAKE_CASE  ,     dtype=timesteps.dtype    )
											__snake_case				=		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,     dim=-1    ).flatten()

											__snake_case				=		torch.cat([timesteps[:1], interleaved_timesteps]    )

											__snake_case				=		None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											__snake_case				=		defaultdict(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		sigma.log()

											# get distribution
											__snake_case				=		log_sigma - self.log_sigmas[:, None]

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

											__snake_case				=		self.log_sigmas[low_idx]
											__snake_case				=		self.log_sigmas[high_idx]

											# interpolate sigmas
											__snake_case				=		(low - log_sigma) / (low - high)
											__snake_case				=		w.clamp(0  ,     1    )

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


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											return self.sample is None


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, np.ndarray]  ,     SCREAMING_SNAKE_CASE						:		Union[float, torch.FloatTensor]  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, np.ndarray]  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     )      ->						Union[SchedulerOutput, Tuple]:




											'''simple docstring'''

											__snake_case				=		self.index_for_timestep(SCREAMING_SNAKE_CASE    )

											# advance index counter by 1
											__snake_case				=		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE    ) else timestep
											self._index_counter[timestep_int] += 1

											if self.state_in_first_order:
																	__snake_case				=		self.sigmas[step_index]
																	__snake_case				=		self.sigmas_interpol[step_index + 1]
																	__snake_case				=		self.sigmas[step_index + 1]
											else:
																	# 2nd order / KDPM2's method
																	__snake_case				=		self.sigmas[step_index - 1]
																	__snake_case				=		self.sigmas_interpol[step_index]
																	__snake_case				=		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
											__snake_case				=		0
											__snake_case				=		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":
																	__snake_case				=		sigma_hat if self.state_in_first_order else sigma_interpol
																	__snake_case				=		sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
																	__snake_case				=		sigma_hat if self.state_in_first_order else sigma_interpol
																	__snake_case				=		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
																	__snake_case				=		(sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	__snake_case				=		sigma_interpol - sigma_hat

																	# store for 2nd order step
																	__snake_case				=		sample
											else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	__snake_case				=		(sample - pred_original_sample) / sigma_interpol

																	# 3. delta timestep
																	__snake_case				=		sigma_next - sigma_hat

																	__snake_case				=		self.sample
																	__snake_case				=		None

											__snake_case				=		sample + derivative * dt

											if not return_dict:
																	return (prev_sample,)

											return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		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
																	__snake_case				=		self.timesteps.to(original_samples.device  ,     dtype=torch.floataa    )
																	__snake_case				=		timesteps.to(original_samples.device  ,     dtype=torch.floataa    )
											else:
																	__snake_case				=		self.timesteps.to(original_samples.device    )
																	__snake_case				=		timesteps.to(original_samples.device    )

											__snake_case				=		[self.index_for_timestep(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ) for t in timesteps]

											__snake_case				=		sigmas[step_indices].flatten()
											while len(sigma.shape    ) < len(original_samples.shape    ):
																	__snake_case				=		sigma.unsqueeze(-1    )

											__snake_case				=		original_samples + noise * sigma
											return noisy_samples






					def __len__(    self						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											return self.config.num_train_timesteps





