






import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
   from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
   A_						:						str						      =					True
   from torch.cuda.amp import autocast


A_						:						str						      =					logging.getLogger(__name__)




@dataclass
class 							_lowerCAmelCase:





       """simple docstring"""







       a							:						str										=field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
       a							:						Optional[str]										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,      )
       a							:						Optional[bool]										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}   )
       a							:						Optional[bool]										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''Whether to log verbose messages or not.'''}				,      )
       a							:						Optional[float]										=field(
           default=2.0				,      metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}   )
       a							:						Optional[float]										=field(
           default=0.5				,      metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}   )
       a							:						Optional[float]										=field(
           default=0.9_9_9_9_9_5				,      metadata={'''help''': '''Decay of gumbel temperature during training.'''}   )







def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[int]:
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,       datefmt='%m/%d/%Y %H:%M:%S'      ,       handlers=[logging.StreamHandler(sys.stdout    )]      ,       )
  UpperCamelCase_:   Optional[int]        =							logging.WARNING
  if model_args.verbose_logging:
    UpperCamelCase_:   Union[str, Any]        =							logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank    ):
    UpperCamelCase_:   Optional[Any]        =							logging.INFO
  logger.setLevel(SCREAMING_SNAKE_CASE__    )




@dataclass
class 							_lowerCAmelCase:





       """simple docstring"""







       a							:						str										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}   )
       a							:						Optional[str]										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
       a							:						Optional[str]										=field(
           default='''train'''				,      metadata={
               '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
           }				,      )
       a							:						Optional[str]										=field(
           default='''validation'''				,      metadata={
               '''help''': (
                   '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
               )
           }				,      )
       a							:						Optional[str]										=field(
           default='''file'''				,      metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}				,      )
       a							:						bool										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}   )
       a							:						Optional[int]										=field(
           default=1				,      metadata={
               '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
           }				,      )
       a							:						Optional[int]										=field(
           default=UpperCamelCase_				,      metadata={'''help''': '''The number of processes to use for the preprocessing.'''}				,      )
       a							:						Optional[float]										=field(
           default=2_0.0				,      metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}   )







@dataclass
class 							_lowerCAmelCase:





       """simple docstring"""







       a							:						WavaVecaForPreTraining
       a							:						WavaVecaFeatureExtractor
       a							:						Union[bool, str]										="longest"
       a							:						Optional[int]										=None
       a							:						Optional[int]										=None





       def __call__(   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							self.feature_extractor.pad(
             lowercase_    ,     max_length=self.max_length    ,     padding=self.padding    ,     pad_to_multiple_of=self.pad_to_multiple_of    ,     return_tensors='pt'    ,     )
         UpperCamelCase_:   Optional[Any]        =							self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]				)

         UpperCamelCase_:   Dict        =							batch['input_values'].shape[0]

         # make sure that no loss is computed on padded inputs
         if batch["attention_mask"] is not None:
           # compute real output lengths according to convolution formula
           UpperCamelCase_:   List[Any]        =							self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1				)				).to(
               torch.long				)

           UpperCamelCase_:   Optional[int]        =							torch.zeros(
               (batch_size, mask_indices_seq_length)    ,     dtype=torch.long    ,     device=batch['input_values'].device				)

           # these two operations makes sure that all values
           # before the output lengths indices are attended to
           UpperCamelCase_:   List[Any]        =							1
           UpperCamelCase_:   Tuple        =							attention_mask.flip([-1]				).cumsum(-1				).flip([-1]				).bool()

         # sample randomly masked indices
         UpperCamelCase_:   List[str]        =							_compute_mask_indices(
             (batch_size, mask_indices_seq_length)    ,     self.model.config.mask_time_prob    ,     self.model.config.mask_time_length    ,     attention_mask=lowercase_    ,     min_masks=2    ,     )

         return batch







class 							_lowerCAmelCase(			UpperCamelCase_   ):





       """simple docstring"""







       def __init__(   self    ,     *_lowerCamelCase    ,     _lowerCamelCase=1    ,     _lowerCamelCase=0    ,     _lowerCamelCase=1.0    ,     **_lowerCamelCase				):
         super().__init__(*lowercase_    ,     **lowercase_				)
         UpperCamelCase_:   List[str]        =							0
         UpperCamelCase_:   Any        =							max_gumbel_temp
         UpperCamelCase_:   Union[str, Any]        =							min_gumbel_temp
         UpperCamelCase_:   Optional[int]        =							gumbel_temp_decay





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         model.train()
         UpperCamelCase_:   int        =							self._prepare_inputs(lowercase_				)

         if self.use_amp:
           with autocast():
             UpperCamelCase_:   Optional[Any]        =							self.compute_loss(lowercase_    ,     lowercase_				)
         else:
           UpperCamelCase_:   List[Any]        =							self.compute_loss(lowercase_    ,     lowercase_				)

         if self.args.n_gpu > 1 or self.deepspeed:
           if model.module.config.ctc_loss_reduction == "mean":
             UpperCamelCase_:   Union[str, Any]        =							loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
             UpperCamelCase_:   Union[str, Any]        =							loss.sum() / (inputs['mask_time_indices']).sum()
           else:
             raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''				)

         if self.args.gradient_accumulation_steps > 1:
           UpperCamelCase_:   int        =							loss / self.args.gradient_accumulation_steps

         if self.use_amp:
           self.scaler.scale(lowercase_				).backward()
         elif self.use_apex:
           with amp.scale_loss(lowercase_    ,     self.optimizer				) as scaled_loss:
             scaled_loss.backward()
         elif self.deepspeed:
           self.deepspeed.backward(lowercase_				)
         else:
           loss.backward()

         self.num_update_step += 1
         # make sure gumbel softmax temperature is decayed
         if self.args.n_gpu > 1 or self.deepspeed:
           model.module.set_gumbel_temperature(
               max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step    ,     self.min_gumbel_temp				)				)
         else:
           model.set_gumbel_temperature(
               max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step    ,     self.min_gumbel_temp				)				)

         return loss.detach()







def     snake_case   ()			->							List[str]:
  UpperCamelCase_:   Tuple        =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

  UpperCamelCase_:   List[str]        =							parser.parse_args_into_dataclasses()
  configure_logger(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__    )

  # Downloading and loading a dataset from the hub.
  UpperCamelCase_:   str        =							load_dataset(data_args.dataset_name      ,       data_args.dataset_config_name      ,       cache_dir=model_args.cache_dir    )

  if "validation" not in datasets.keys():
    # make sure only "validation" and "train" keys remain"
    UpperCamelCase_:   Union[str, Any]        =							DatasetDict()
    UpperCamelCase_:   str        =							load_dataset(
        data_args.dataset_name      ,       data_args.dataset_config_name      ,       split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''      ,       cache_dir=model_args.cache_dir      ,       )
    UpperCamelCase_:   List[Any]        =							load_dataset(
        data_args.dataset_name      ,       data_args.dataset_config_name      ,       split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''      ,       cache_dir=model_args.cache_dir      ,       )
  else:
    # make sure only "validation" and "train" keys remain"
    UpperCamelCase_:   Tuple        =							DatasetDict()
    UpperCamelCase_:   Optional[Any]        =							load_dataset(
        data_args.dataset_name      ,       data_args.dataset_config_name      ,       split='validation'      ,       cache_dir=model_args.cache_dir      ,       )
    UpperCamelCase_:   int        =							load_dataset(
        data_args.dataset_name      ,       data_args.dataset_config_name      ,       split=F'''{data_args.train_split_name}'''      ,       cache_dir=model_args.cache_dir      ,       )

  # only normalized-inputs-training is supported
  UpperCamelCase_:   List[str]        =							WavaVecaFeatureExtractor.from_pretrained(
      model_args.model_name_or_path      ,       cache_dir=model_args.cache_dir      ,       do_normalize=SCREAMING_SNAKE_CASE__    )

  def prepare_dataset(UpperCAmelCase__    ):
    # check that all files have the correct sampling rate
    UpperCamelCase_:   Optional[int]        =							librosa.load(batch[data_args.speech_file_column]      ,       sr=feature_extractor.sampling_rate    )
    return batch

  # load audio files into numpy arrays
  UpperCamelCase_:   Tuple        =							datasets.map(
      SCREAMING_SNAKE_CASE__      ,       num_proc=data_args.preprocessing_num_workers      ,       remove_columns=datasets['train'].column_names    )

  # filter audio files that are too long
  UpperCamelCase_:   List[str]        =							vectorized_datasets.filter(
      lambda UpperCAmelCase__    : len(data['speech']    ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate    )    )

  def normalize(UpperCAmelCase__    ):
    return feature_extractor(batch['speech']      ,       sampling_rate=feature_extractor.sampling_rate    )

  # normalize and transform to `BatchFeatures`
  UpperCamelCase_:   Dict        =							vectorized_datasets.map(
      SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__      ,       num_proc=data_args.preprocessing_num_workers      ,       load_from_cache_file=not data_args.overwrite_cache      ,       remove_columns=vectorized_datasets['train'].column_names      ,       )

  # pretraining is only supported for "newer" stable layer norm architecture
  # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
  UpperCamelCase_:   List[Any]        =							WavaVecaConfig.from_pretrained(
      model_args.model_name_or_path      ,       cache_dir=model_args.cache_dir      ,       gradient_checkpointing=training_args.gradient_checkpointing      ,       )

  if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
    raise ValueError(
        'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
        ' ``config.feat_extract_norm=\'layer\''    )

  UpperCamelCase_:   List[Any]        =							WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__    )

  UpperCamelCase_:   List[Any]        =							DataCollatorForWavaVecaPretraining(model=SCREAMING_SNAKE_CASE__      ,       feature_extractor=SCREAMING_SNAKE_CASE__    )

  UpperCamelCase_:   Optional[Any]        =							WavaVecaPreTrainer(
      model=SCREAMING_SNAKE_CASE__      ,       data_collator=SCREAMING_SNAKE_CASE__      ,       args=SCREAMING_SNAKE_CASE__      ,       train_dataset=vectorized_datasets['train']      ,       eval_dataset=vectorized_datasets['validation']      ,       tokenizer=SCREAMING_SNAKE_CASE__      ,       max_gumbel_temp=model_args.max_gumbel_temperature      ,       min_gumbel_temp=model_args.min_gumbel_temperature      ,       gumbel_temp_decay=model_args.gumbel_temperature_decay      ,       )
  trainer.train()


if __name__ == "__main__":
   main()



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =(PNDMScheduler,)
      UpperCamelCase__				:    Dict =(('num_inference_steps', 50),)






      def 			lowerCamelCase			(   self		:	Dict     ,  **lowercase_		:	Dict					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            ={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_					)
             return config






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	str=0     ,  **lowercase_		:	str					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Optional[Any]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Tuple            =self.dummy_sample
             _lowerCamelCase			:						int            =0.1 * sample
             _lowerCamelCase			:						int            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						str            =self.get_scheduler_config(**lowercase_					)
                    _lowerCamelCase			:						str            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)
                    # copy over dummy past residuals
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           new_scheduler.set_timesteps(lowercase_					)
                           # copy over dummy past residuals
                           _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    _lowerCamelCase			:						Union[str, Any]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Optional[int]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=0     ,  **lowercase_		:	int					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Dict            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
             _lowerCamelCase			:						Optional[int]            =0.1 * sample
             _lowerCamelCase			:						List[str]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Any            =self.get_scheduler_config()
                    _lowerCamelCase			:						int            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =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)
                           _lowerCamelCase			:						Optional[int]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[str]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  **lowercase_		:	Any					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config(**lowercase_					)
             _lowerCamelCase			:						Any            =scheduler_class(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =10
             _lowerCamelCase			:						str            =self.dummy_model()
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_					)

             for i, t in enumerate(scheduler.prk_timesteps					):
                    _lowerCamelCase			:						Union[str, Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						Any            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             for i, t in enumerate(scheduler.plms_timesteps					):
                    _lowerCamelCase			:						List[Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						int            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             return sample






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)

             _lowerCamelCase			:						Optional[int]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						List[str]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
                    _lowerCamelCase			:						Optional[int]            =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'					):
                           _lowerCamelCase			:						Tuple            =num_inference_steps

                    # copy over dummy past residuals (must be done after set_timesteps)
                    _lowerCamelCase			:						List[Any]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                    _lowerCamelCase			:						Union[str, Any]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[Any]            =scheduler.step_prk(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =scheduler.step_prk(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Tuple            =scheduler.step_plms(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Any:
             """simple docstring"""




             for timesteps in [100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Dict            =self.get_scheduler_config(steps_offset=1					)
             _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)
             scheduler.set_timesteps(10					)
             assert torch.equal(
                 scheduler.timesteps     ,  torch.LongTensor(
                     [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)     ,  )






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		str:
             """simple docstring"""




             for beta_start, beta_end in zip([0.0001, 0.001]     ,  [0.002, 0.02]					):
                    self.check_over_configs(beta_start=lowercase_     ,  beta_end=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Union[str, Any]:
             """simple docstring"""




             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             for t in [1, 5, 10]:
                    self.check_over_forward(time_step=lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[Any]:
             """simple docstring"""




             for t, num_inference_steps in zip([1, 5, 10]     ,  [10, 50, 100]					):
                    self.check_over_forward(num_inference_steps=lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =27

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Dict            =self.dummy_sample
                    _lowerCamelCase			:						List[Any]            =0.1 * sample

                    _lowerCamelCase			:						List[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[str]            =scheduler_class(**lowercase_					)

                    scheduler.set_timesteps(lowercase_					)

                    # before power of 3 fix, would error on first step, so we only need to do two
                    for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                           _lowerCamelCase			:						Tuple            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Optional[int]:
             """simple docstring"""




             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Dict            =self.scheduler_classes[0]
                    _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    scheduler.step_plms(self.dummy_sample     ,  1     ,  self.dummy_sample					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop()
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 198.1318					) < 1E-2
             assert abs(result_mean.item() - 0.2580					) < 1E-3






      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.full_loop(prediction_type='v_prediction'					)
             _lowerCamelCase			:						Dict            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 67.3986					) < 1E-2
             assert abs(result_mean.item() - 0.0878					) < 1E-3






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 230.0399					) < 1E-2
             assert abs(result_mean.item() - 0.2995					) < 1E-3






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Union[str, Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 186.9482					) < 1E-2
             assert abs(result_mean.item() - 0.2434					) < 1E-3


