




"""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 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Union[str, Any]							     =   TimesformerConfig()

						if "large" in model_name:
												snake_case__    :   List[Any]							     =   96

						if "hr" in model_name:
												snake_case__    :   Dict							     =   16
												snake_case__    :   Dict							     =   448

						snake_case__    :   Dict							     =   '''huggingface/label-files'''
						if "k400" in model_name:
												snake_case__    :   List[str]							     =   400
												snake_case__    :   str							     =   '''kinetics400-id2label.json'''
						elif "k600" in model_name:
												snake_case__    :   Optional[Any]							     =   600
												snake_case__    :   List[str]							     =   '''kinetics600-id2label.json'''
						elif "ssv2" in model_name:
												snake_case__    :   Optional[int]							     =   174
												snake_case__    :   Tuple							     =   '''something-something-v2-id2label.json'''
						else:
												raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''						)
						snake_case__    :   List[str]							     =   json.load(open(hf_hub_download(lowerCamelCase ,     lowerCamelCase ,     repo_type='''dataset'''						) ,     '''r'''						)						)
						snake_case__    :   Optional[int]							     =   {int(lowerCamelCase						): v for k, v in idalabel.items()}
						snake_case__    :   List[str]							     =   idalabel
						snake_case__    :   Optional[int]							     =   {v: k for k, v in idalabel.items()}

						return config

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


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

						return name

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if key.startswith('''model.'''						):
																		snake_case__    :   Dict							     =   key.replace('''model.''' ,     ''''''						)

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

						return orig_state_dict

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Any							     =   hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''' ,     filename='''eating_spaghetti.npy''' ,     repo_type='''dataset'''						)
						snake_case__    :   str							     =   np.load(lowerCamelCase						)
						return list(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   get_timesformer_config(lowerCamelCase						)

						snake_case__    :   List[Any]							     =   TimesformerForVideoClassification(lowerCamelCase						)

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

						model.load_state_dict(lowerCamelCase						)
						model.eval()

						# verify model on basic input
						snake_case__    :   Tuple							     =   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,     image_std=[0.5, 0.5, 0.5]						)
						snake_case__    :   int							     =   prepare_video()
						snake_case__    :   Optional[int]							     =   image_processor(video[:8] ,     return_tensors='''pt'''						)

						snake_case__    :   Optional[int]							     =   model(**lowerCamelCase						)
						snake_case__    :   Dict							     =   outputs.logits

						snake_case__    :   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":
												snake_case__    :   Optional[Any]							     =   torch.Size([1, 400]						)
												snake_case__    :   List[str]							     =   torch.tensor([-0.3_016, -0.7_713, -0.4_205]						)
						elif model_name == "timesformer-base-finetuned-k600":
												snake_case__    :   Any							     =   torch.Size([1, 600]						)
												snake_case__    :   int							     =   torch.tensor([-0.7_267, -0.7_466, 3.2_404]						)
						elif model_name == "timesformer-base-finetuned-ssv2":
												snake_case__    :   List[Any]							     =   torch.Size([1, 174]						)
												snake_case__    :   List[str]							     =   torch.tensor([-0.9_059, 0.6_433, -3.1_457]						)
						elif model_name == "timesformer-large-finetuned-k400":
												snake_case__    :   List[str]							     =   torch.Size([1, 400]						)
												snake_case__    :   Optional[Any]							     =   torch.tensor([0, 0, 0]						)
						elif model_name == "timesformer-large-finetuned-k600":
												snake_case__    :   Dict							     =   torch.Size([1, 600]						)
												snake_case__    :   str							     =   torch.tensor([0, 0, 0]						)
						elif model_name == "timesformer-large-finetuned-ssv2":
												snake_case__    :   Tuple							     =   torch.Size([1, 174]						)
												snake_case__    :   Tuple							     =   torch.tensor([0, 0, 0]						)
						elif model_name == "timesformer-hr-finetuned-k400":
												snake_case__    :   int							     =   torch.Size([1, 400]						)
												snake_case__    :   List[str]							     =   torch.tensor([-0.9_617, -3.7_311, -3.7_708]						)
						elif model_name == "timesformer-hr-finetuned-k600":
												snake_case__    :   Tuple							     =   torch.Size([1, 600]						)
												snake_case__    :   Dict							     =   torch.tensor([2.5_273, 0.7_127, 1.8_848]						)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												snake_case__    :   List[Any]							     =   torch.Size([1, 174]						)
												snake_case__    :   Tuple							     =   torch.tensor([-3.6_756, -0.7_513, 0.7_180]						)
						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] ,     lowerCamelCase ,     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(lowerCamelCase						)
												model.save_pretrained(lowerCamelCase						)

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


if __name__ == "__main__":
							_lowercase		 =      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."""
							)

							_lowercase		 =      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 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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=0.999 ,     lowerCamelCase="cosine" ,     ):
						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowerCamelCase						):
																		return math.cos((t + 0.008) / 1.008 * 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__    :   Tuple							     =   []
						for i in range(lowerCamelCase						):
												snake_case__    :   int							     =   i / num_diffusion_timesteps
												snake_case__    :   Tuple							     =   (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  __lowercase    (     snake_case_  ,				snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =[e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase		     =2

				@register_to_config
				def __init__(				self				:       Optional[Any] ,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 ,):
										if trained_betas is not None:
																snake_case__    :   Union[str, Any]							     =   torch.tensor(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										elif beta_schedule == "linear":
																snake_case__    :   List[str]							     =   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__    :   Optional[Any]							     =   (
																    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__    :   str							     =   betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
										else:
																raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

										snake_case__    :   Tuple							     =   1.0 - self.betas
										snake_case__    :   List[Any]							     =   torch.cumprod(self.alphas ,dim=0 )

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

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       List[Any]=None ):
										if schedule_timesteps is None:
																snake_case__    :   Union[str, Any]							     =   self.timesteps

										snake_case__    :   Optional[int]							     =   (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__    :   List[str]							     =   1 if len(SCREAMING_SNAKE_CASE_ ) > 1 else 0
										else:
																snake_case__    :   Any							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
																snake_case__    :   int							     =   self._index_counter[timestep_int]

										return indices[pos].item()

				@property
				def  _lowerCamelCase   (				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  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,):
										snake_case__    :   Optional[int]							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										if self.state_in_first_order:
																snake_case__    :   Optional[Any]							     =   self.sigmas[step_index]
										else:
																snake_case__    :   str							     =   self.sigmas_interpol[step_index - 1]

										snake_case__    :   Dict							     =   sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, torch.device] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,):
										snake_case__    :   str							     =   num_inference_steps

										snake_case__    :   Union[str, 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":
																snake_case__    :   Optional[Any]							     =   np.linspace(0 ,num_train_timesteps - 1 ,SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case__    :   Optional[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
																snake_case__    :   Dict							     =   (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__    :   Union[str, 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
																snake_case__    :   List[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\'.''' )

										snake_case__    :   List[str]							     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
										snake_case__    :   List[str]							     =   torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )

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

										# compute up and down sigmas
										snake_case__    :   str							     =   sigmas.roll(-1 )
										snake_case__    :   str							     =   0.0
										snake_case__    :   List[Any]							     =   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
										snake_case__    :   List[str]							     =   (sigmas_next**2 - sigmas_up**2) ** 0.5
										snake_case__    :   List[Any]							     =   0.0

										# compute interpolated sigmas
										snake_case__    :   Tuple							     =   sigmas.log().lerp(sigmas_down.log() ,0.5 ).exp()
										snake_case__    :   Optional[Any]							     =   0.0

										# set sigmas
										snake_case__    :   List[str]							     =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
										snake_case__    :   Optional[int]							     =   torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
										snake_case__    :   str							     =   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
										snake_case__    :   Optional[Any]							     =   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

										if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
																# mps does not support float64
																snake_case__    :   Tuple							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										else:
																snake_case__    :   int							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Tuple							     =   self.sigma_to_t(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=timesteps.dtype )
										snake_case__    :   int							     =   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,dim=-1 ).flatten()

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

										snake_case__    :   Optional[int]							     =   None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										snake_case__    :   Optional[int]							     =   defaultdict(SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										# get log sigma
										snake_case__    :   Dict							     =   sigma.log()

										# get distribution
										snake_case__    :   Union[str, Any]							     =   log_sigma - self.log_sigmas[:, None]

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

										snake_case__    :   Any							     =   self.log_sigmas[low_idx]
										snake_case__    :   str							     =   self.log_sigmas[high_idx]

										# interpolate sigmas
										snake_case__    :   int							     =   (low - log_sigma) / (low - high)
										snake_case__    :   List[Any]							     =   w.clamp(0 ,1 )

										# transform interpolation to time range
										snake_case__    :   Optional[int]							     =   (1 - w) * low_idx + w * high_idx
										snake_case__    :   int							     =   t.view(sigma.shape )
										return t

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										return self.sample is None

				def  _lowerCamelCase   (				self				:       List[Any] ,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_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										snake_case__    :   str							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										# advance index counter by 1
										snake_case__    :   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:
																snake_case__    :   List[Any]							     =   self.sigmas[step_index]
																snake_case__    :   str							     =   self.sigmas_interpol[step_index]
																snake_case__    :   str							     =   self.sigmas_up[step_index]
																snake_case__    :   Optional[int]							     =   self.sigmas_down[step_index - 1]
										else:
																# 2nd order / KPDM2's method
																snake_case__    :   int							     =   self.sigmas[step_index - 1]
																snake_case__    :   Tuple							     =   self.sigmas_interpol[step_index - 1]
																snake_case__    :   Optional[Any]							     =   self.sigmas_up[step_index - 1]
																snake_case__    :   Dict							     =   self.sigmas_down[step_index - 1]

										# 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__    :   Optional[int]							     =   0
										snake_case__    :   List[Any]							     =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										snake_case__    :   Dict							     =   model_output.device
										snake_case__    :   Tuple							     =   randn_tensor(model_output.shape ,dtype=model_output.dtype ,device=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ )

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																snake_case__    :   List[str]							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   Any							     =   sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																snake_case__    :   Tuple							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   List[Any]							     =   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__    :   List[str]							     =   (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case__    :   Tuple							     =   sigma_interpol - sigma_hat

																# store for 2nd order step
																snake_case__    :   int							     =   sample
																snake_case__    :   Tuple							     =   dt
																snake_case__    :   Tuple							     =   sample + derivative * dt
										else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																snake_case__    :   Tuple							     =   (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																snake_case__    :   Union[str, Any]							     =   sigma_down - sigma_hat

																snake_case__    :   int							     =   self.sample
																snake_case__    :   Tuple							     =   None

																snake_case__    :   Dict							     =   sample + derivative * dt
																snake_case__    :   Any							     =   prev_sample + noise * sigma_up

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										snake_case__    :   Optional[int]							     =   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__    :   List[str]							     =   self.timesteps.to(original_samples.device ,dtype=torch.floataa )
																snake_case__    :   int							     =   timesteps.to(original_samples.device ,dtype=torch.floataa )
										else:
																snake_case__    :   Any							     =   self.timesteps.to(original_samples.device )
																snake_case__    :   List[str]							     =   timesteps.to(original_samples.device )

										snake_case__    :   List[Any]							     =   [self.index_for_timestep(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for t in timesteps]

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

										snake_case__    :   List[str]							     =   original_samples + noise * sigma
										return noisy_samples





				def __len__(				self				:       int ):
										return self.config.num_train_timesteps


