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

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__snake_case					:							Optional[Any]           =							'\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

__snake_case					:							Optional[int]           =							logging.getLogger(__name__)


@dataclass
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    Optional[str]												=			field(
						    default="""roberta-large-mnli"""						,			metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""})
						__a     :    Optional[str]												=			field(
						    default="""This example is {}."""						,			metadata={
						        """help""": (
						            """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
						            """where class name is inserted."""
						        )
						    }						,			)
						__a     :    Optional[int]												=			field(
						    default=3_2						,			metadata={"""help""": """Batch size for generating teacher predictions."""})
						__a     :    Optional[bool]												=			field(
						    default=UpperCAmelCase__						,			metadata={
						        """help""": (
						            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
						            """multi-class multi-label classification)."""
						        )
						    }						,			)
						__a     :    Optional[float]												=			field(
						    default=1.0						,			metadata={"""help""": """Temperature applied to teacher softmax for distillation."""})


@dataclass
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    Optional[str]												=			field(
						    default="""distilbert-base-uncased"""						,			metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""})


@dataclass
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    str												=			field(metadata={"""help""": """Text file with one unlabeled instance per line."""})
						__a     :    str												=			field(metadata={"""help""": """Text file with one class name per line."""})
						__a     :    bool												=			field(
						    default=UpperCAmelCase__						,			metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}						,			)


@dataclass
class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Optional[str]												=			field(
						    default=UpperCAmelCase__						,			metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}						,			)
						__a     :    int												=			field(
						    default=3_2						,			metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""})
						__a     :    int												=			field(
						    default=1_2_8						,			metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""})
						__a     :    float												=			field(default=1.0						,			metadata={"""help""": """Total number of training epochs to perform."""})
						__a     :    bool												=			field(default=UpperCAmelCase__						,			metadata={"""help""": """Whether to run training of student model."""})
						__a     :    bool												=			field(
						    default=UpperCAmelCase__						,			metadata={
						        """help""": (
						            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
						            """after training."""
						        )
						    }						,			)
						__a     :    Optional[int]												=			field(
						    default=0						,			metadata={
						        """help""": (
						            """Limit the total amount of checkpoints."""
						            """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
						        )
						    }						,			)


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self  , A  , A  , A=False	)  ->Any:
									UpperCAmelCase__		:List[str] 		=    inputs['labels']
									UpperCAmelCase__		:int 		=    model(inputs['input_ids']  , attention_mask=inputs['attention_mask']	)
									UpperCAmelCase__		:Dict 		=    outputs[0]

									UpperCAmelCase__		:Optional[int] 		=    -torch.sum(target_p * logits.log_softmax(dim=-1	)  , axis=-1	).mean()

									if return_outputs:
												return loss, outputs

									return loss




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    []
			with open(SCREAMING_SNAKE_CASE     ,						'r'							) as f:
						for line in f:
									UpperCAmelCase__		:str 		=    line.strip()
									if len(SCREAMING_SNAKE_CASE							) > 0:
												lines.append(SCREAMING_SNAKE_CASE							)
			return lines




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:str 		=    []
			UpperCAmelCase__		:List[Any] 		=    []
			for example in examples:
						for name in class_names:
									premises.append(SCREAMING_SNAKE_CASE							)
									hypotheses.append(hypothesis_template.format(SCREAMING_SNAKE_CASE							)							)
			return premises, hypotheses




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			for label, ind in config.labelaid.items():
						if label.lower().startswith('entail'							):
									return ind
			logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'							)
			return -1




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						):




			"""simple docstring"""


			UpperCAmelCase__		:Optional[Any] 		=    AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Optional[Any] 		=    model.config
			if not no_cuda and torch.cuda.is_available():
						UpperCAmelCase__		:Tuple 		=    nn.DataParallel(model.cuda()							)
						batch_size *= len(model.device_ids							)
			UpperCAmelCase__		:int 		=    AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     ,						use_fast=SCREAMING_SNAKE_CASE							)

			UpperCAmelCase__		,       UpperCAmelCase__		:Optional[int] 		=    get_premise_hypothesis_pairs(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    []

			for i in tqdm(range(0     ,						len(SCREAMING_SNAKE_CASE							)     ,						SCREAMING_SNAKE_CASE							)							):
						UpperCAmelCase__		:Optional[Any] 		=    premises[i : i + batch_size]
						UpperCAmelCase__		:Union[str, Any] 		=    hypotheses[i : i + batch_size]

						UpperCAmelCase__		:List[Any] 		=    tokenizer(
						    SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						padding=SCREAMING_SNAKE_CASE     ,						truncation='only_first'     ,						return_tensors='pt'     ,						)

						with torch.cuda.amp.autocast(enabled=SCREAMING_SNAKE_CASE							):
									with torch.no_grad():
												UpperCAmelCase__		:str 		=    model(**SCREAMING_SNAKE_CASE							)
						logits.append(outputs.logits.detach().cpu().float()							)

			UpperCAmelCase__		:List[str] 		=    get_entailment_id(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    -1 if entail_id == 0 else 0
			UpperCAmelCase__		:int 		=    torch.cat(SCREAMING_SNAKE_CASE     ,						dim=0							)  # N*K x 3
			UpperCAmelCase__		:Dict 		=    logits.reshape(len(SCREAMING_SNAKE_CASE							)     ,						len(SCREAMING_SNAKE_CASE							)     ,						-1							)[..., [contr_id, entail_id]]  # N x K x 2

			if multi_label:
						# softmax over (contr, entail) logits for each class independently
						UpperCAmelCase__		:Optional[int] 		=    (nli_logits / temperature).softmax(-1							)
			else:
						# softmax over entail logits across classes s.t. class probabilities sum to 1.
						UpperCAmelCase__		:str 		=    (nli_logits / temperature).softmax(1							)

			return nli_prob[..., 1]  # N x K




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    HfArgumentParser(
			    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)     ,						description=SCREAMING_SNAKE_CASE     ,						)

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

			# Detecting last checkpoint.
			UpperCAmelCase__		:Any 		=    None
			if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCAmelCase__		: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:
									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							):
						utils.logging.set_verbosity_info()
						utils.logging.enable_default_handler()
						utils.logging.enable_explicit_format()

			if training_args.local_rank != -1:
						raise ValueError('Distributed training is not currently supported.'							)
			if training_args.tpu_num_cores is not None:
						raise ValueError('TPU acceleration is not currently supported.'							)

			logger.info(f"""Training/evaluation parameters {training_args}"""							)

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

			# 1. read in data
			UpperCAmelCase__		:int 		=    read_lines(data_args.data_file							)
			UpperCAmelCase__		:Optional[int] 		=    read_lines(data_args.class_names_file							)

			# 2. get teacher predictions and load into dataset
			logger.info('Generating predictions from zero-shot teacher model'							)
			UpperCAmelCase__		:Any 		=    get_teacher_predictions(
			    teacher_args.teacher_name_or_path     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						teacher_args.hypothesis_template     ,						teacher_args.teacher_batch_size     ,						teacher_args.temperature     ,						teacher_args.multi_label     ,						data_args.use_fast_tokenizer     ,						training_args.no_cuda     ,						training_args.fpaa     ,						)
			UpperCAmelCase__		:Union[str, Any] 		=    Dataset.from_dict(
			    {
			        'text': examples,
			        'labels': teacher_soft_preds,
			    }							)

			# 3. create student
			logger.info('Initializing student model'							)
			UpperCAmelCase__		:Optional[int] 		=    AutoModelForSequenceClassification.from_pretrained(
			    student_args.student_name_or_path     ,						num_labels=len(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:List[Any] 		=    AutoTokenizer.from_pretrained(student_args.student_name_or_path     ,						use_fast=data_args.use_fast_tokenizer							)
			UpperCAmelCase__		:Any 		=    dict(enumerate(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Optional[Any] 		=    {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE							)}

			# 4. train student on teacher predictions
			UpperCAmelCase__		:str 		=    dataset.map(SCREAMING_SNAKE_CASE     ,						input_columns='text'							)
			dataset.set_format('torch'							)

			def compute_metrics(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=False							):
						UpperCAmelCase__		:List[Any] 		=    p.predictions.argmax(-1							)
						UpperCAmelCase__		:Any 		=    p.label_ids.argmax(-1							)  # "label_ids" are actually distributions
						return {"agreement": (preds == proxy_labels).mean().item()}

			UpperCAmelCase__		:List[Any] 		=    DistillationTrainer(
			    model=SCREAMING_SNAKE_CASE     ,						tokenizer=SCREAMING_SNAKE_CASE     ,						args=SCREAMING_SNAKE_CASE     ,						train_dataset=SCREAMING_SNAKE_CASE     ,						compute_metrics=SCREAMING_SNAKE_CASE     ,						)

			if training_args.do_train:
						logger.info('Training student model on teacher predictions'							)
						trainer.train()

			if training_args.do_eval:
						UpperCAmelCase__		:Tuple 		=    trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE							)['eval_agreement']
						logger.info(f"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%"""							)

			trainer.save_model()


if __name__ == "__main__":
						main()







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			(DPMSolverMultistepScheduler,)
						__a     :    Dict												=			(("""num_inference_steps""", 2_5),)


						def    A__       (  self  , **A	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    {
									    'num_train_timesteps': 10_00,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'	),
									    'variance_type': None,
									}

									config.update(**A	)
									return config


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:int 		=    self.dummy_sample
									UpperCAmelCase__		:Optional[int] 		=    0.1 * sample
									UpperCAmelCase__		:Any 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:List[Any] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)
												# copy over dummy past residuals
												UpperCAmelCase__		:Optional[Any] 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:List[Any] 		=    scheduler_class.from_pretrained(A	)
															new_scheduler.set_timesteps(A	)
															# copy over dummy past residuals
															UpperCAmelCase__		:Union[str, Any] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    sample, sample
												for t in range(A  , time_step + scheduler.config.solver_order + 1	):
															UpperCAmelCase__		:Dict 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
															UpperCAmelCase__		:Optional[Any] 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->List[Any]:
									pass


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:str 		=    self.dummy_sample
									UpperCAmelCase__		:Union[str, Any] 		=    0.1 * sample
									UpperCAmelCase__		:Union[str, Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:List[Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:List[str] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)

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

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:int 		=    scheduler_class.from_pretrained(A	)
															# copy over dummy past residuals
															new_scheduler.set_timesteps(A	)

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

												UpperCAmelCase__		:Any 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:Dict 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self  , A=None  , **A	)  ->List[str]:
									if scheduler is None:
												UpperCAmelCase__		:Optional[Any] 		=    self.scheduler_classes[0]
												UpperCAmelCase__		:Optional[Any] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler_class(**A	)

									UpperCAmelCase__		:List[Any] 		=    10
									UpperCAmelCase__		:int 		=    self.dummy_model()
									UpperCAmelCase__		:int 		=    self.dummy_sample_deter
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:Union[str, Any] 		=    model(A  , A	)
												UpperCAmelCase__		:str 		=    scheduler.step(A  , A  , A	).prev_sample

									return sample


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    dict(self.forward_default_kwargs	)

									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('num_inference_steps'  , A	)

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:Optional[Any] 		=    scheduler_class(**A	)

												UpperCAmelCase__		:Dict 		=    self.dummy_sample
												UpperCAmelCase__		:Dict 		=    0.1 * sample

												if num_inference_steps is not None and hasattr(A  , 'set_timesteps'	):
															scheduler.set_timesteps(A	)
												elif num_inference_steps is not None and not hasattr(A  , 'set_timesteps'	):
															UpperCAmelCase__		:List[Any] 		=    num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												UpperCAmelCase__		:List[Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]
												UpperCAmelCase__		:Any 		=    dummy_past_residuals[: scheduler.config.solver_order]

												UpperCAmelCase__		:str 		=    scheduler.timesteps[5]
												UpperCAmelCase__		:str 		=    scheduler.timesteps[6]

												UpperCAmelCase__		:List[str] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:List[Any] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->Optional[Any]:
									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
												self.check_over_configs(num_train_timesteps=A	)


						def    A__       (  self	)  ->str:
									self.check_over_configs(thresholding=A	)
									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=A  , prediction_type=A  , sample_max_value=A  , algorithm_type='dpmsolver++'  , solver_order=A  , solver_type=A  , )


						def    A__       (  self	)  ->Optional[int]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=A	)


						def    A__       (  self	)  ->List[str]:
									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
															for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																								if order == 3:
																											continue
																					else:
																								self.check_over_configs(
																								    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					UpperCAmelCase__		:Optional[int] 		=    self.full_loop(
																					    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					assert not torch.isnan(A	).any(), "Samples have nan numbers"


						def    A__       (  self	)  ->List[Any]:
									self.check_over_configs(lower_order_final=A	)
									self.check_over_configs(lower_order_final=A	)


						def    A__       (  self	)  ->Optional[Any]:
									self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
									self.check_over_configs(lambda_min_clipped=-5.1	)


						def    A__       (  self	)  ->Union[str, Any]:
									self.check_over_configs(variance_type=A	)
									self.check_over_configs(variance_type='learned_range'	)


						def    A__       (  self	)  ->List[str]:
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
												self.check_over_forward(num_inference_steps=A  , time_step=0	)


						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    self.full_loop()
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(thresholding=A  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5	)
									UpperCAmelCase__		:Any 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 1.1364	) < 1e-3


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.full_loop(prediction_type='v_prediction'	)
									UpperCAmelCase__		:int 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2251	) < 1e-3


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(prediction_type='v_prediction'  , use_karras_sigmas=A	)
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2096	) < 1e-3


						def    A__       (  self	)  ->List[Any]:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									UpperCAmelCase__		:Tuple 		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:List[Any] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3

									UpperCAmelCase__		:Dict 		=    DPMSolverSinglestepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    UniPCMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    DEISMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    DPMSolverMultistepScheduler.from_config(scheduler.config	)

									UpperCAmelCase__		:Dict 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:Dict 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    self.scheduler_classes[0]
									UpperCAmelCase__		:List[str] 		=    self.get_scheduler_config(thresholding=A  , dynamic_thresholding_ratio=0	)
									UpperCAmelCase__		:str 		=    scheduler_class(**A	)

									UpperCAmelCase__		:int 		=    10
									UpperCAmelCase__		:Optional[int] 		=    self.dummy_model()
									UpperCAmelCase__		:List[str] 		=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:str 		=    model(A  , A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler.step(A  , A  , A	).prev_sample

									assert sample.dtype == torch.floataa


						def    A__       (  self  , **A	)  ->Any:
									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:int 		=    scheduler_class(**A	)

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





