




"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a      =							logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

a      =							list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a      =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def 							lowercase					(snake_case__   : str )     ->  Union[str, Any]:


				'''simple docstring'''






				with open(snake_case__      ,       """rb""" ) as f:
								lowerCAmelCase  =				Image.open(snake_case__ )
								return im.convert("""RGB""" )






@dataclass
class       SCREAMING_SNAKE_CASE__					:
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
						    }						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'A folder containing the training data.'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'A folder containing the validation data.'}					)
						_a    						=						field(
						    default=0.1_5						,				metadata={'help': 'Percent to split off of train for validation.'}					)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of training examples to this '
						            'value if set.'
						        )
						    }						,				)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
						            'value if set.'
						        )
						    }						,				)


						def   __lowercase (							self				:			int					):
										if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
														raise ValueError(
														    """You must specify either a dataset name from the hub or a train and/or validation directory."""					)






@dataclass
class       SCREAMING_SNAKE_CASE__					:
						_a    						=						field(
						    default='google/vit-base-patch16-224-in21k'						,				metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_a					)}						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}					)
						_a    						=						field(
						    default='main'						,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,				)
						_a    						=						field(default=_a						,				metadata={'help': 'Name or path of preprocessor config.'}					)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
						            'with private models).'
						        )
						    }						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}						,				)





def 							lowercase					(snake_case__   : str )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.stack([example["""pixel_values"""] for example in examples] )
				lowerCAmelCase  =				torch.tensor([example["""labels"""] for example in examples] )
				return {"pixel_values": pixel_values, "labels": labels}





def 							lowercase					()     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("""run_image_classification"""      ,       snake_case__      ,       snake_case__ )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,       datefmt="""%m/%d/%Y %H:%M:%S"""      ,       handlers=[logging.StreamHandler(sys.stdout )]      ,       )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				lowerCAmelCase  =				training_args.get_process_log_level()
				logger.setLevel(snake_case__ )
				transformers.utils.logging.set_verbosity(snake_case__ )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
				logger.info(f'''Training/evaluation parameters {training_args}''' )

				# Detecting last checkpoint.
				lowerCAmelCase  =				None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								lowerCAmelCase  =				get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    """Use --overwrite_output_dir to overcome.""" )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Set seed before initializing model.
				set_seed(training_args.seed )

				# Initialize our dataset and prepare it for the 'image-classification' task.
				if data_args.dataset_name is not None:
								lowerCAmelCase  =				load_dataset(
								    data_args.dataset_name      ,       data_args.dataset_config_name      ,       cache_dir=model_args.cache_dir      ,       task="""image-classification"""      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )
				else:
								lowerCAmelCase  =				{}
								if data_args.train_dir is not None:
												lowerCAmelCase  =				os.path.join(data_args.train_dir      ,       """**""" )
								if data_args.validation_dir is not None:
												lowerCAmelCase  =				os.path.join(data_args.validation_dir      ,       """**""" )
								lowerCAmelCase  =				load_dataset(
								    """imagefolder"""      ,       data_files=snake_case__      ,       cache_dir=model_args.cache_dir      ,       task="""image-classification"""      ,       )

				# If we don't have a validation split, split off a percentage of train as validation.
				lowerCAmelCase  =				None if """validation""" in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split      ,       snake_case__ ) and data_args.train_val_split > 0.0:
								lowerCAmelCase  =				dataset["""train"""].train_test_split(data_args.train_val_split )
								lowerCAmelCase  =				split["""train"""]
								lowerCAmelCase  =				split["""test"""]

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				lowerCAmelCase  =				dataset["""train"""].features["""labels"""].names
				lowerCAmelCase ,						lowerCAmelCase  =				{}, {}
				for i, label in enumerate(snake_case__ ):
								lowerCAmelCase  =				str(snake_case__ )
								lowerCAmelCase  =				label

				# Load the accuracy metric from the datasets package
				lowerCAmelCase  =				evaluate.load("""accuracy""" )

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(snake_case__   : List[Any] ):
								return metric.compute(predictions=np.argmax(p.predictions      ,       axis=1 )      ,       references=p.label_ids )

				lowerCAmelCase  =				AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path      ,       num_labels=len(snake_case__ )      ,       labelaid=snake_case__      ,       idalabel=snake_case__      ,       finetuning_task="""image-classification"""      ,       cache_dir=model_args.cache_dir      ,       revision=model_args.model_revision      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )
				lowerCAmelCase  =				AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path      ,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path )      ,       config=snake_case__      ,       cache_dir=model_args.cache_dir      ,       revision=model_args.model_revision      ,       use_auth_token=True if model_args.use_auth_token else None      ,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      ,       )
				lowerCAmelCase  =				AutoImageProcessor.from_pretrained(
				    model_args.image_processor_name or model_args.model_name_or_path      ,       cache_dir=model_args.cache_dir      ,       revision=model_args.model_revision      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )

				# Define torchvision transforms to be applied to each image.
				if "shortest_edge" in image_processor.size:
								lowerCAmelCase  =				image_processor.size["""shortest_edge"""]
				else:
								lowerCAmelCase  =				(image_processor.size["""height"""], image_processor.size["""width"""])
				lowerCAmelCase  =				Normalize(mean=image_processor.image_mean      ,       std=image_processor.image_std )
				lowerCAmelCase  =				Compose(
				    [
				        RandomResizedCrop(snake_case__ ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ] )
				lowerCAmelCase  =				Compose(
				    [
				        Resize(snake_case__ ),
				        CenterCrop(snake_case__ ),
				        ToTensor(),
				        normalize,
				    ] )

				def train_transforms(snake_case__   : List[str] ):
								lowerCAmelCase  =				[
								    _train_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch["""image"""]
								]
								return example_batch

				def val_transforms(snake_case__   : Dict ):
								lowerCAmelCase  =				[_val_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch["""image"""]]
								return example_batch

				if training_args.do_train:
								if "train" not in dataset:
												raise ValueError("""--do_train requires a train dataset""" )
								if data_args.max_train_samples is not None:
												lowerCAmelCase  =				(
												    dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
												)
								# Set the training transforms
								dataset["train"].set_transform(snake_case__ )

				if training_args.do_eval:
								if "validation" not in dataset:
												raise ValueError("""--do_eval requires a validation dataset""" )
								if data_args.max_eval_samples is not None:
												lowerCAmelCase  =				(
												    dataset["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
												)
								# Set the validation transforms
								dataset["validation"].set_transform(snake_case__ )

				# Initalize our trainer
				lowerCAmelCase  =				Trainer(
				    model=snake_case__      ,       args=snake_case__      ,       train_dataset=dataset["""train"""] if training_args.do_train else None      ,       eval_dataset=dataset["""validation"""] if training_args.do_eval else None      ,       compute_metrics=snake_case__      ,       tokenizer=snake_case__      ,       data_collator=snake_case__      ,       )

				# Training
				if training_args.do_train:
								lowerCAmelCase  =				None
								if training_args.resume_from_checkpoint is not None:
												lowerCAmelCase  =				training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowerCAmelCase  =				last_checkpoint
								lowerCAmelCase  =				trainer.train(resume_from_checkpoint=snake_case__ )
								trainer.save_model()
								trainer.log_metrics("""train"""      ,       train_result.metrics )
								trainer.save_metrics("""train"""      ,       train_result.metrics )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								lowerCAmelCase  =				trainer.evaluate()
								trainer.log_metrics("""eval"""      ,       snake_case__ )
								trainer.save_metrics("""eval"""      ,       snake_case__ )

				# Write model card and (optionally) push to hub
				lowerCAmelCase  =				{
				    """finetuned_from""": model_args.model_name_or_path,
				    """tasks""": """image-classification""",
				    """dataset""": data_args.dataset_name,
				    """tags""": ["""image-classification""", """vision"""],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**snake_case__ )
				else:
								trainer.create_model_card(**snake_case__ )


if __name__ == "__main__":
		main()











"""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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : int=0.999      ,       snake_case__   : Tuple="cosine"      ,       )     ->  Any:


				'''simple docstring'''






				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				lowerCAmelCase  =				[]
				for i in range(snake_case__ ):
								lowerCAmelCase  =				i / num_diffusion_timesteps
								lowerCAmelCase  =				(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       SCREAMING_SNAKE_CASE__					(					_a						,				_a					):
						_a    						=						[e.name for e in KarrasDiffusionSchedulers]
						_a    						=						2


						@register_to_config
						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			int = 1000						,						lowerCAmelCase				:			float = 0.0_0085						,						lowerCAmelCase				:			float = 0.012						,						lowerCAmelCase				:			str = "linear"						,						lowerCAmelCase				:			Optional[Union[np.ndarray, List[float]]] = None						,						lowerCAmelCase				:			str = "epsilon"						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			float = 1.0						,						lowerCAmelCase				:			str = "linspace"						,						lowerCAmelCase				:			int = 0						,						):
										if trained_betas is not None:
														lowerCAmelCase  =				torch.tensor(lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "linear":
														lowerCAmelCase  =				torch.linspace(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														lowerCAmelCase  =				(
														    torch.linspace(beta_start**0.5						,						beta_end**0.5						,						lowerCAmelCase						,						dtype=torch.floataa					) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""cosine"""					)
										elif beta_schedule == "exp":
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""exp"""					)
										else:
														raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

										#  set all values
										self.set_timesteps(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										lowerCAmelCase  =				use_karras_sigmas


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[int]=None					):
										if schedule_timesteps is None:
														lowerCAmelCase  =				self.timesteps

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

										return indices[pos].item()


						@property
						def   __lowercase (							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   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

										lowerCAmelCase  =				self.sigmas[step_index]
										lowerCAmelCase  =				sample / ((sigma**2 + 1) ** 0.5)
										return sample


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Union[str, torch.device] = None						,						lowerCAmelCase				:			Optional[int] = None						,						):
										lowerCAmelCase  =				num_inference_steps

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

										lowerCAmelCase  =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										lowerCAmelCase  =				np.log(lowerCAmelCase					)
										lowerCAmelCase  =				np.interp(lowerCAmelCase						,						np.arange(0						,						len(lowerCAmelCase					)					)						,						lowerCAmelCase					)

										if self.config.use_karras_sigmas:
														lowerCAmelCase  =				self._convert_to_karras(in_sigmas=lowerCAmelCase						,						num_inference_steps=self.num_inference_steps					)
														lowerCAmelCase  =				np.array([self._sigma_to_t(lowerCAmelCase						,						lowerCAmelCase					) for sigma in sigmas]					)

										lowerCAmelCase  =				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

										if str(lowerCAmelCase					).startswith("""mps"""					):
														# mps does not support float64
														lowerCAmelCase  =				timesteps.to(lowerCAmelCase						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				timesteps.to(device=lowerCAmelCase					)

										# empty dt and derivative
										lowerCAmelCase  =				None
										lowerCAmelCase  =				None

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


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any					):
										# get log sigma
										lowerCAmelCase  =				np.log(lowerCAmelCase					)

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

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

										lowerCAmelCase  =				log_sigmas[low_idx]
										lowerCAmelCase  =				log_sigmas[high_idx]

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

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


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				in_sigmas[-1].item()
										lowerCAmelCase  =				in_sigmas[0].item()

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


						@property
						def   __lowercase (							self				:			Dict					):
										return self.dt is None


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			bool = True						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

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

										if self.state_in_first_order:
														lowerCAmelCase  =				self.sigmas[step_index]
														lowerCAmelCase  =				self.sigmas[step_index + 1]
										else:
														# 2nd order / Heun's method
														lowerCAmelCase  =				self.sigmas[step_index - 1]
														lowerCAmelCase  =				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
										lowerCAmelCase  =				0
										lowerCAmelCase  =				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":
														lowerCAmelCase  =				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase  =				sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
														lowerCAmelCase  =				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase  =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
										elif self.config.prediction_type == "sample":
														lowerCAmelCase  =				model_output
										else:
														raise ValueError(
														    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

										if self.config.clip_sample:
														lowerCAmelCase  =				pred_original_sample.clamp(
														    -self.config.clip_sample_range						,						self.config.clip_sample_range					)

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

														# store for 2nd order step
														lowerCAmelCase  =				derivative
														lowerCAmelCase  =				dt
														lowerCAmelCase  =				sample
										else:
														# 2. 2nd order / Heun's method
														lowerCAmelCase  =				(sample - pred_original_sample) / sigma_next
														lowerCAmelCase  =				(self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														lowerCAmelCase  =				self.dt
														lowerCAmelCase  =				self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None

										lowerCAmelCase  =				sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCAmelCase  =				self.sigmas.to(device=original_samples.device						,						dtype=original_samples.dtype					)
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase					):
														# mps does not support float64
														lowerCAmelCase  =				self.timesteps.to(original_samples.device						,						dtype=torch.floataa					)
														lowerCAmelCase  =				timesteps.to(original_samples.device						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				self.timesteps.to(original_samples.device					)
														lowerCAmelCase  =				timesteps.to(original_samples.device					)

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

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

										lowerCAmelCase  =				original_samples + noise * sigma
										return noisy_samples




						def __len__(							self				:			Union[str, Any]					):
										return self.config.num_train_timesteps





