






'''simple docstring'''


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

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowerCamelCase_				: Tuple    	=							logging.getLogger(__name__)
lowerCamelCase_				: Optional[Any]    	=							list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase_				: List[str]    	=							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={
			        "help": (
			            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
			        )
			    }       ,)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(SCREAMING_SNAKE_CASE	)}       ,)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={
			        "help": (
			            "Override some existing default config settings when a model is trained from scratch. Example: "
			            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
			        )
			    }       ,)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,)
			__a							:							bool					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}       ,)
			__a							:							str					     =					field(
			    default="main"       ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,)
			__a							:							bool					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }       ,)






			def 				A      (   self							:						Optional[int]			)							->    str:

									'''simple docstring'''







									if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
															raise ValueError(
															    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""			)
@dataclass
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "The name of the dataset to use (via the datasets library)."}	)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
			__a							:							Optional[str]					     =					field(default=SCREAMING_SNAKE_CASE       ,metadata={"help": "The input training data file (a text file)."}	)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}       ,)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}       ,)
			__a							:							Optional[str]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}       ,)
			__a							:							bool					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "Overwrite the cached training and evaluation sets"}	)
			__a							:							Optional[int]					     =					field(
			    default=5       ,metadata={
			        "help": "The percentage of the train set used as validation set in case there's no validation split"
			    }       ,)
			__a							:							Optional[int]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated. Default to the max input length of the model."
			        )
			    }       ,)
			__a							:							Optional[int]					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={"help": "The number of processes to use for the preprocessing."}       ,)
			__a							:							float					     =					field(
			    default=0.1_5       ,metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}	)
			__a							:							bool					     =					field(
			    default=SCREAMING_SNAKE_CASE       ,metadata={
			        "help": (
			            "Whether to pad all samples to `max_seq_length`. "
			            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
			        )
			    }       ,)






			def 				A      (   self							:						Dict			)							->    List[str]:

									'''simple docstring'''







									if self.train_file is not None:
															UpperCamelCase__         = self.train_file.split("""."""			)[-1]
															assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
									if self.validation_file is not None:
															UpperCamelCase__         = self.validation_file.split("""."""			)[-1]
															assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						with open(_A						,      """r"""						,      encoding="""utf-8"""					) as f:
												UpperCamelCase__         = [json.loads(_A					) for line in f.read().splitlines() if (len(_A					) > 0 and not line.isspace())]
						assert len(_A					) == len(_A					)

						UpperCamelCase__         = {c: dataset[c] for c in dataset.column_names}
						UpperCamelCase__         = refs
						return Dataset.from_dict(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = 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__         = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
						else:
												UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						UpperCamelCase__         = None
						if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
												UpperCamelCase__         = 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:
																		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."""					)

    # 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					)]						,      )
						logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

						# 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}"					)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank					):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("""Training/evaluation parameters %s"""						,      _A					)

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

						# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
						# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
						# (the dataset will be downloaded automatically from the datasets Hub).
						#
						# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
						# 'text' is found. You can easily tweak this behavior (see below).
						#
						# In distributed training, the load_dataset function guarantee that only one local process can concurrently
						# download the dataset.
						if data_args.dataset_name is not None:
												# Downloading and loading a dataset from the hub.
												UpperCamelCase__         = load_dataset(data_args.dataset_name						,      data_args.dataset_config_name					)
												if "validation" not in datasets.keys():
																		UpperCamelCase__         = load_dataset(
																		    data_args.dataset_name						,      data_args.dataset_config_name						,      split=f"train[:{data_args.validation_split_percentage}%]"						,      )
																		UpperCamelCase__         = load_dataset(
																		    data_args.dataset_name						,      data_args.dataset_config_name						,      split=f"train[{data_args.validation_split_percentage}%:]"						,      )
						else:
												UpperCamelCase__         = {}
												if data_args.train_file is not None:
																		UpperCamelCase__         = data_args.train_file
												if data_args.validation_file is not None:
																		UpperCamelCase__         = data_args.validation_file
												UpperCamelCase__         = data_args.train_file.split("""."""					)[-1]
												if extension == "txt":
																		UpperCamelCase__         = """text"""
												UpperCamelCase__         = load_dataset(_A						,      data_files=_A					)
						# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
						# https://huggingface.co/docs/datasets/loading_datasets.html.

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						UpperCamelCase__         = {
						    """cache_dir""": model_args.cache_dir,
						    """revision""": model_args.model_revision,
						    """use_auth_token""": True if model_args.use_auth_token else None,
						}
						if model_args.config_name:
												UpperCamelCase__         = AutoConfig.from_pretrained(model_args.config_name						,      **_A					)
						elif model_args.model_name_or_path:
												UpperCamelCase__         = AutoConfig.from_pretrained(model_args.model_name_or_path						,      **_A					)
						else:
												UpperCamelCase__         = CONFIG_MAPPING[model_args.model_type]()
												logger.warning("""You are instantiating a new config instance from scratch."""					)
												if model_args.config_overrides is not None:
																		logger.info(f"Overriding config: {model_args.config_overrides}"					)
																		config.update_from_string(model_args.config_overrides					)
																		logger.info(f"New config: {config}"					)

						UpperCamelCase__         = {
						    """cache_dir""": model_args.cache_dir,
						    """use_fast""": model_args.use_fast_tokenizer,
						    """revision""": model_args.model_revision,
						    """use_auth_token""": True if model_args.use_auth_token else None,
						}
						if model_args.tokenizer_name:
												UpperCamelCase__         = AutoTokenizer.from_pretrained(model_args.tokenizer_name						,      **_A					)
						elif model_args.model_name_or_path:
												UpperCamelCase__         = AutoTokenizer.from_pretrained(model_args.model_name_or_path						,      **_A					)
						else:
												raise ValueError(
												    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
												    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""					)

						if model_args.model_name_or_path:
												UpperCamelCase__         = AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path						,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)						,      config=_A						,      cache_dir=model_args.cache_dir						,      revision=model_args.model_revision						,      use_auth_token=True if model_args.use_auth_token else None						,      )
						else:
												logger.info("""Training new model from scratch"""					)
												UpperCamelCase__         = AutoModelForMaskedLM.from_config(_A					)

						model.resize_token_embeddings(len(_A					)					)

						# Preprocessing the datasets.
						# First we tokenize all the texts.
						if training_args.do_train:
												UpperCamelCase__         = datasets["""train"""].column_names
						else:
												UpperCamelCase__         = datasets["""validation"""].column_names
						UpperCamelCase__         = """text""" if """text""" in column_names else column_names[0]

						UpperCamelCase__         = """max_length""" if data_args.pad_to_max_length else False

						def tokenize_function(_A					):
												# Remove empty lines
												UpperCamelCase__         = [line for line in examples["""text"""] if len(_A					) > 0 and not line.isspace()]
												return tokenizer(examples["""text"""]						,      padding=_A						,      truncation=_A						,      max_length=data_args.max_seq_length					)

						UpperCamelCase__         = datasets.map(
						    _A						,      batched=_A						,      num_proc=data_args.preprocessing_num_workers						,      remove_columns=[text_column_name]						,      load_from_cache_file=not data_args.overwrite_cache						,      )

						# Add the chinese references if provided
						if data_args.train_ref_file is not None:
												UpperCamelCase__         = add_chinese_references(tokenized_datasets["""train"""]						,      data_args.train_ref_file					)
						if data_args.validation_ref_file is not None:
												UpperCamelCase__         = add_chinese_references(
												    tokenized_datasets["""validation"""]						,      data_args.validation_ref_file					)
						# If we have ref files, need to avoid it removed by trainer
						UpperCamelCase__         = data_args.train_ref_file or data_args.validation_ref_file
						if has_ref:
												UpperCamelCase__         = False

						# Data collator
						# This one will take care of randomly masking the tokens.
						UpperCamelCase__         = DataCollatorForWholeWordMask(tokenizer=_A						,      mlm_probability=data_args.mlm_probability					)

						# Initialize our Trainer
						UpperCamelCase__         = Trainer(
						    model=_A						,      args=_A						,      train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None						,      eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None						,      tokenizer=_A						,      data_collator=_A						,      )

						# Training
						if training_args.do_train:
												if last_checkpoint is not None:
																		UpperCamelCase__         = last_checkpoint
												elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
																		UpperCamelCase__         = model_args.model_name_or_path
												else:
																		UpperCamelCase__         = None
												UpperCamelCase__         = trainer.train(resume_from_checkpoint=_A					)
												trainer.save_model()  # Saves the tokenizer too for easy upload

												UpperCamelCase__         = os.path.join(training_args.output_dir						,      """train_results.txt"""					)
												if trainer.is_world_process_zero():
																		with open(_A						,      """w"""					) as writer:
																								logger.info("""***** Train results *****"""					)
																								for key, value in sorted(train_result.metrics.items()					):
																														logger.info(f"  {key} = {value}"					)
																														writer.write(f"{key} = {value}\n"					)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir						,      """trainer_state.json"""					)					)

    # Evaluation
						UpperCamelCase__         = {}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""					)

												UpperCamelCase__         = trainer.evaluate()

												UpperCamelCase__         = math.exp(eval_output["""eval_loss"""]					)
												UpperCamelCase__         = perplexity

												UpperCamelCase__         = os.path.join(training_args.output_dir						,      """eval_results_mlm_wwm.txt"""					)
												if trainer.is_world_process_zero():
																		with open(_A						,      """w"""					) as writer:
																								logger.info("""***** Eval results *****"""					)
																								for key, value in sorted(results.items()					):
																														logger.info(f"  {key} = {value}"					)
																														writer.write(f"{key} = {value}\n"					)

						return results







def 	__magic_name__( _A					):
						'''simple docstring'''





						main()


if __name__ == "__main__":
					main()








'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Dict					     =					(DPMSolverSinglestepScheduler,)
			__a							:							List[Any]					     =					(("num_inference_steps", 25),)






			def 				A      (   self							:						List[Any] ,      **lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = {
									    """num_train_timesteps""": 1_0_0_0,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									    """solver_order""": 2,
									    """prediction_type""": """epsilon""",
									    """thresholding""": False,
									    """sample_max_value""": 1.0,
									    """algorithm_type""": """dpmsolver++""",
									    """solver_type""": """midpoint""",
									    """lambda_min_clipped""": -float("""inf"""			),
									    """variance_type""": None,
									}

									config.update(**lowercase			)
									return config






			def 				A      (   self							:						List[Any] ,      lowercase							:						Tuple=0 ,      **lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)
									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)
									UpperCamelCase__         = self.dummy_sample
									UpperCamelCase__         = 0.1 * sample
									UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

															UpperCamelCase__    ,   UpperCamelCase__         = sample, sample
															for t in range(lowercase ,      time_step + scheduler.config.solver_order + 1			):
																					UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
																					UpperCamelCase__         = 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 				A      (   self							:						List[str]			)							->    Any:

									'''simple docstring'''







									pass






			def 				A      (   self							:						Any ,      lowercase							:						List[Any]=0 ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = dict(self.forward_default_kwargs			)
									UpperCamelCase__         = kwargs.pop("""num_inference_steps""" ,      lowercase			)
									UpperCamelCase__         = self.dummy_sample
									UpperCamelCase__         = 0.1 * sample
									UpperCamelCase__         = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
															UpperCamelCase__         = self.get_scheduler_config()
															UpperCamelCase__         = scheduler_class(**lowercase			)
															scheduler.set_timesteps(lowercase			)

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(lowercase			)
																					UpperCamelCase__         = 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__         = dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase ,      **lowercase			).prev_sample
															UpperCamelCase__         = 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 				A      (   self							:						Union[str, Any] ,      lowercase							:						Any=None ,      **lowercase							:						List[Any]			)							->    Dict:

									'''simple docstring'''







									if scheduler is None:
															UpperCamelCase__         = self.scheduler_classes[0]
															UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
															UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(**lowercase			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter
									scheduler.set_timesteps(lowercase			)

									for i, t in enumerate(scheduler.timesteps			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									return sample






			def 				A      (   self							:						Any			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = 5_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter
									scheduler.set_timesteps(lowercase			)

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_5_7_4			) < 1e-3






			def 				A      (   self							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=lowercase			)






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

									'''simple docstring'''







									UpperCamelCase__         = DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3

									UpperCamelCase__         = DEISMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = DPMSolverMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = UniPCMultistepScheduler.from_config(scheduler.config			)
									UpperCamelCase__         = DPMSolverSinglestepScheduler.from_config(scheduler.config			)

									UpperCamelCase__         = self.full_loop(scheduler=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3






			def 				A      (   self							:						Tuple			)							->    Union[str, Any]:

									'''simple docstring'''







									self.check_over_configs(thresholding=lowercase			)
									for order in [1, 2, 3]:
															for solver_type in ["midpoint", "heun"]:
																					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 ,      algorithm_type="""dpmsolver++""" ,      solver_order=lowercase ,      solver_type=lowercase ,      )






			def 				A      (   self							:						Optional[int]			)							->    Tuple:

									'''simple docstring'''







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






			def 				A      (   self							:						Optional[int]			)							->    int:

									'''simple docstring'''







									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
															for solver_type in ["midpoint", "heun"]:
																					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 ,      algorithm_type=lowercase ,      )
																																	UpperCamelCase__         = self.full_loop(
																																	    solver_order=lowercase ,      solver_type=lowercase ,      prediction_type=lowercase ,      algorithm_type=lowercase ,      )
																																	assert not torch.isnan(lowercase			).any(), "Samples have nan numbers"






			def 				A      (   self							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







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






			def 				A      (   self							:						Tuple			)							->    Optional[int]:

									'''simple docstring'''







									self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
									self.check_over_configs(lambda_min_clipped=-5.1			)






			def 				A      (   self							:						List[Any]			)							->    int:

									'''simple docstring'''







									self.check_over_configs(variance_type=lowercase			)
									self.check_over_configs(variance_type="""learned_range"""			)






			def 				A      (   self							:						Tuple			)							->    int:

									'''simple docstring'''







									for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
															self.check_over_forward(num_inference_steps=lowercase ,      time_step=0			)






			def 				A      (   self							:						List[str]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop()
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(use_karras_sigmas=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.2_2_4_8			) < 1e-3






			def 				A      (   self							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction"""			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.1_4_5_3			) < 1e-3






			def 				A      (   self							:						int			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.full_loop(prediction_type="""v_prediction""" ,      use_karras_sigmas=lowercase			)
									UpperCamelCase__         = torch.mean(torch.abs(lowercase			)			)

									assert abs(result_mean.item() - 0.0_6_4_9			) < 1e-3







			def 				A      (   self							:						Optional[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = self.scheduler_classes[0]
									UpperCamelCase__         = self.get_scheduler_config(thresholding=lowercase ,      dynamic_thresholding_ratio=0			)
									UpperCamelCase__         = scheduler_class(**lowercase			)

									UpperCamelCase__         = 1_0
									UpperCamelCase__         = self.dummy_model()
									UpperCamelCase__         = self.dummy_sample_deter.half()
									scheduler.set_timesteps(lowercase			)

									for i, t in enumerate(scheduler.timesteps			):
															UpperCamelCase__         = model(lowercase ,      lowercase			)
															UpperCamelCase__         = scheduler.step(lowercase ,      lowercase ,      lowercase			).prev_sample

									assert sample.dtype == torch.floataa
