

"""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
snake_case_   :   Optional[int]        =       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""")

snake_case_   :   str        =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
snake_case_   :   Optional[int]        =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					with open(_lowercase							,      "rb"	) as f:
										UpperCAmelCase			:   Tuple      =				Image.open(_lowercase	)
										return im.convert("RGB"	)







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







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



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



def   lowercase_	(    _lowercase       :		Any	):




					'''simple docstring'''






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



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				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.
										UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   Tuple      =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
					else:
										UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   int      =				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"							,      _lowercase							,      _lowercase	)

					# 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()

					UpperCAmelCase			:   Optional[Any]      =				training_args.get_process_log_level()
					logger.setLevel(_lowercase	)
					transformers.utils.logging.set_verbosity(_lowercase	)
					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.
					UpperCAmelCase			:   Tuple      =				None
					if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
										UpperCAmelCase			:   Optional[int]      =				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:
										UpperCAmelCase			:   Dict      =				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:
										UpperCAmelCase			:   Optional[Any]      =				{}
										if data_args.train_dir is not None:
															UpperCAmelCase			:   List[Any]      =				os.path.join(data_args.train_dir							,      "**"	)
										if data_args.validation_dir is not None:
															UpperCAmelCase			:   Optional[Any]      =				os.path.join(data_args.validation_dir							,      "**"	)
										UpperCAmelCase			:   Any      =				load_dataset(
										    "imagefolder"							,      data_files=_lowercase							,      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.
					UpperCAmelCase			:   Optional[int]      =				None if "validation" in dataset.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split							,      _lowercase	) and data_args.train_val_split > 0.0:
										UpperCAmelCase			:   Union[str, Any]      =				dataset["train"].train_test_split(data_args.train_val_split	)
										UpperCAmelCase			:   Optional[Any]      =				split["train"]
										UpperCAmelCase			:   Dict      =				split["test"]

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

					# Load the accuracy metric from the datasets package
					UpperCAmelCase			:   int      =				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(_lowercase       :		List[Any]	):
										return metric.compute(predictions=np.argmax(p.predictions							,      axis=1	)							,      references=p.label_ids	)

					UpperCAmelCase			:   List[str]      =				AutoConfig.from_pretrained(
					    model_args.config_name or model_args.model_name_or_path							,      num_labels=len(_lowercase	)							,      labelaid=_lowercase							,      idalabel=_lowercase							,      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							,      )
					UpperCAmelCase			:   Optional[int]      =				AutoModelForImageClassification.from_pretrained(
					    model_args.model_name_or_path							,      from_tf=bool(".ckpt" in model_args.model_name_or_path	)							,      config=_lowercase							,      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							,      )
					UpperCAmelCase			:   Any      =				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:
										UpperCAmelCase			:   Optional[Any]      =				image_processor.size["shortest_edge"]
					else:
										UpperCAmelCase			:   Any      =				(image_processor.size["height"], image_processor.size["width"])
					UpperCAmelCase			:   Union[str, Any]      =				Normalize(mean=image_processor.image_mean							,      std=image_processor.image_std	)
					UpperCAmelCase			:   List[str]      =				Compose(
					    [
					        RandomResizedCrop(_lowercase	),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        normalize,
					    ]	)
					UpperCAmelCase			:   Any      =				Compose(
					    [
					        Resize(_lowercase	),
					        CenterCrop(_lowercase	),
					        ToTensor(),
					        normalize,
					    ]	)

					def train_transforms(_lowercase       :		int	):
										UpperCAmelCase			:   int      =				[
										    _train_transforms(pil_img.convert("RGB"	)	) for pil_img in example_batch["image"]
										]
										return example_batch

					def val_transforms(_lowercase       :		List[str]	):
										UpperCAmelCase			:   str      =				[_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:
															UpperCAmelCase			:   Tuple      =				(
															    dataset["train"].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
															)
										# Set the training transforms
										dataset["train"].set_transform(_lowercase	)

					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:
															UpperCAmelCase			:   str      =				(
															    dataset["validation"].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
															)
										# Set the validation transforms
										dataset["validation"].set_transform(_lowercase	)

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

					# Training
					if training_args.do_train:
										UpperCAmelCase			:   Optional[int]      =				None
										if training_args.resume_from_checkpoint is not None:
															UpperCAmelCase			:   int      =				training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															UpperCAmelCase			:   List[str]      =				last_checkpoint
										UpperCAmelCase			:   Optional[Any]      =				trainer.train(resume_from_checkpoint=_lowercase	)
										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:
										UpperCAmelCase			:   Optional[int]      =				trainer.evaluate()
										trainer.log_metrics("eval"							,      _lowercase	)
										trainer.save_metrics("eval"							,      _lowercase	)

					# Write model card and (optionally) push to hub
					UpperCAmelCase			:   str      =				{
					    "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(**_lowercase	)
					else:
										trainer.create_model_card(**_lowercase	)


if __name__ == "__main__":
	main()





"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						(UniPCMultistepScheduler,)
			SCREAMING_SNAKE_CASE__     				=						(('''num_inference_steps''', 25),)







			def 			__lowerCAmelCase				(			self			: Tuple			,  **lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**lowercase  )
								return config







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[str]=0			,  **lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Dict      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   str      =				self.dummy_sample
								UpperCAmelCase			:   Tuple      =				0.1 * sample
								UpperCAmelCase			:   int      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Optional[Any]      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )
													# copy over dummy past residuals
													UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase  )
																		UpperCAmelCase			:   Any      =				scheduler_class.from_pretrained(lowercase  )
																		new_scheduler.set_timesteps(lowercase  )
																		# copy over dummy past residuals
																		UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: new_scheduler.config.solver_order]

													UpperCAmelCase      ,							UpperCAmelCase			:   List[Any]      =				sample, sample
													for t in range(lowercase			,  time_step + scheduler.config.solver_order + 1  ):
																		UpperCAmelCase			:   List[str]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
																		UpperCAmelCase			:   List[Any]      =				new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

																		assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: int=0			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Union[str, Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   Tuple      =				self.dummy_sample
								UpperCAmelCase			:   Any      =				0.1 * sample
								UpperCAmelCase			:   int      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Optional[int]      =				self.get_scheduler_config()
													UpperCAmelCase			:   List[Any]      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )

													# copy over dummy past residuals (must be after setting timesteps)
													UpperCAmelCase			:   Dict      =				dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase  )
																		UpperCAmelCase			:   str      =				scheduler_class.from_pretrained(lowercase  )
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(lowercase  )

																		# copy over dummy past residual (must be after setting timesteps)
																		UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: new_scheduler.config.solver_order]

													UpperCAmelCase			:   Dict      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   Any      =				new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

													assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Optional[int]=None			,  **lowercase			: int  ):





								'''simple docstring'''



								if scheduler is None:
													UpperCAmelCase			:   Dict      =				self.scheduler_classes[0]
													UpperCAmelCase			:   int      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   str      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Optional[int]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Dict      =				self.get_scheduler_config(**lowercase  )
								UpperCAmelCase			:   int      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   List[str]      =				10
								UpperCAmelCase			:   Optional[Any]      =				self.dummy_model()
								UpperCAmelCase			:   Union[str, Any]      =				self.dummy_sample_deter
								scheduler.set_timesteps(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   List[str]      =				model(lowercase			,  lowercase  )
													UpperCAmelCase			:   Optional[Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample

								return sample







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				dict(self.forward_default_kwargs  )

								UpperCAmelCase			:   List[Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   str      =				self.get_scheduler_config()
													UpperCAmelCase			:   Dict      =				scheduler_class(**lowercase  )

													UpperCAmelCase			:   Any      =				self.dummy_sample
													UpperCAmelCase			:   Any      =				0.1 * sample

													if num_inference_steps is not None and hasattr(lowercase			,  "set_timesteps"  ):
																		scheduler.set_timesteps(lowercase  )
													elif num_inference_steps is not None and not hasattr(lowercase			,  "set_timesteps"  ):
																		UpperCAmelCase			:   str      =				num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													UpperCAmelCase			:   Union[str, Any]      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													UpperCAmelCase			:   Dict      =				dummy_past_residuals[: scheduler.config.solver_order]

													UpperCAmelCase			:   str      =				scheduler.timesteps[5]
													UpperCAmelCase			:   List[Any]      =				scheduler.timesteps[6]

													UpperCAmelCase			:   Union[str, Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   int      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

													self.assertEqual(output_a.shape			,  sample.shape  )
													self.assertEqual(output_a.shape			,  output_a.shape  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				UniPCMultistepScheduler(**self.get_scheduler_config()  )
								UpperCAmelCase			:   Tuple      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3

								UpperCAmelCase			:   int      =				DPMSolverSinglestepScheduler.from_config(scheduler.config  )
								UpperCAmelCase			:   List[str]      =				DEISMultistepScheduler.from_config(scheduler.config  )
								UpperCAmelCase			:   Optional[int]      =				DPMSolverMultistepScheduler.from_config(scheduler.config  )
								UpperCAmelCase			:   int      =				UniPCMultistepScheduler.from_config(scheduler.config  )

								UpperCAmelCase			:   Dict      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								self.check_over_configs(thresholding=lowercase  )
								for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																							for prediction_type in ["epsilon", "sample"]:
																												self.check_over_configs(
																												    thresholding=lowercase			,  prediction_type=lowercase			,  sample_max_value=lowercase			,  solver_order=lowercase			,  solver_type=lowercase			,  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							UpperCAmelCase			:   Union[str, Any]      =				self.full_loop(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							assert not torch.isnan(lowercase  ).any(), "Samples have nan numbers"







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.check_over_configs(lower_order_final=lowercase  )
								self.check_over_configs(lower_order_final=lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=lowercase			,  time_step=0  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop()
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop(prediction_type="v_prediction"  )
								UpperCAmelCase			:   int      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.1_0_1_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   int      =				self.get_scheduler_config(thresholding=lowercase			,  dynamic_thresholding_ratio=0  )
								UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Any      =				10
								UpperCAmelCase			:   Dict      =				self.dummy_model()
								UpperCAmelCase			:   List[str]      =				self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   Any      =				model(lowercase			,  lowercase  )
													UpperCAmelCase			:   List[Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample

								assert sample.dtype == torch.floataa







			def 			__lowerCAmelCase				(			self			: List[str]			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Union[str, Any]      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   Union[str, Any]      =				scheduler_class(**lowercase  )

													scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
													assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps


