

"""simple docstring"""






import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel

def 		lowerCamelCase__		(					_lowerCamelCase=32 ,   _lowerCamelCase=10 ,   _lowerCamelCase=100 ,   _lowerCamelCase=1026 ,   _lowerCamelCase=True ,   _lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl" ,   _lowerCamelCase="igf_context_pairs.jbl" ,   ):

    '''simple docstring'''



    set_seed(3   )
    # generate train_data and objective_set
    _lowerCAmelCase,					_lowerCAmelCase       :   int  =    generate_datasets(
        _lowerCamelCase ,   _lowerCamelCase ,   number=_lowerCamelCase ,   min_len=1026 ,   trim=_lowerCamelCase   )
    # keeps model same across runs
    set_seed(4   )
    # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
    # can we train on GPU?
    _lowerCAmelCase       :   int  =    torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'   )

    # load pretrained model
    _lowerCAmelCase       :   Dict  =    load_gpta('gpt2'   ).to(_lowerCamelCase   )
    print('computing perplexity on objective set'   )
    _lowerCAmelCase       :   str  =    compute_perplexity(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ).item()
    print('perplexity on objective set:' ,   _lowerCamelCase   )

    # collect igf pairs and save to file demo.jbl
    collect_objective_set(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

    # clean up, delete model and data we don't need anymore
    del model, train_data, objective_set
    torch.cuda.empty_cache()

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=15 ,   _lowerCamelCase=128 ,   _lowerCamelCase=100 ,   _lowerCamelCase="igf_model.pt" ,   ):

    '''simple docstring'''



    set_seed(42   )

    # Load pre-trained model
    _lowerCAmelCase       :   int  =    GPTaLMHeadModel.from_pretrained('gpt2'   )

    # Initialize secondary learner to use embedding weights of model
    _lowerCAmelCase       :   Optional[int]  =    SecondaryLearner(_lowerCamelCase   )

    # Train secondary learner
    _lowerCAmelCase       :   int  =    train_secondary_learner(
        _lowerCamelCase ,   _lowerCamelCase ,   max_epochs=_lowerCamelCase ,   batch_size=_lowerCamelCase ,   eval_freq=100 ,   igf_model_path=_lowerCamelCase ,   )

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=32 ,   _lowerCamelCase=1000 ,   _lowerCamelCase=16 ,   _lowerCamelCase=1.0 ,   _lowerCamelCase=recopy_gpta ,   _lowerCamelCase=None ,   _lowerCamelCase=10 ,   _lowerCamelCase="gpt2_finetuned.pt" ,   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'   )
    _lowerCAmelCase       :   Tuple  =    RandomSampler(_lowerCamelCase   )
    _lowerCAmelCase       :   Tuple  =    DataLoader(_lowerCamelCase ,   sampler=_lowerCamelCase   )

    _lowerCAmelCase       :   List[str]  =    max_steps // (len(_lowerCamelCase   )) + 1
    _lowerCAmelCase       :   Any  =    0
    _lowerCAmelCase       :   Any  =    torch.zeros((1, context_len) ,   dtype=torch.long ,   device=_lowerCamelCase   )
    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    recopy_model(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

    model.train()
    if secondary_learner is not None:
        secondary_learner.to(_lowerCamelCase   )
        secondary_learner.eval()
    _lowerCAmelCase       :   Tuple  =    []
    _lowerCAmelCase       :   int  =    0

    _lowerCAmelCase       :   Optional[int]  =    []
    _lowerCAmelCase       :   str  =    []

    # Compute the performance of the transformer model at the beginning
    _lowerCAmelCase       :   Any  =    compute_perplexity(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
    test_perps.append(_lowerCamelCase   )
    print('Test perplexity, step' ,   _lowerCamelCase ,   ':' ,   _lowerCamelCase   )
    for epoch in range(int(_lowerCamelCase   )   ):
        for step, example in enumerate(_lowerCamelCase   ):
            torch.cuda.empty_cache()
            _lowerCAmelCase       :   List[str]  =    random.randint(0 ,   example.size(2   ) - context_len - 1   )
            _lowerCAmelCase       :   List[Any]  =    example[0, 0, start : start + context_len]
            lm_optimizer.zero_grad()
            _lowerCAmelCase       :   Optional[int]  =    model(_lowerCamelCase ,   labels=_lowerCamelCase   )
            _lowerCAmelCase       :   List[str]  =    True

            if secondary_learner is not None:
                _lowerCAmelCase       :   Dict  =    secondary_learner.forward(
                    torch.tensor(_lowerCamelCase ,   dtype=torch.long ,   device=_lowerCamelCase   ).unsqueeze(0   )   )[0].item()
                observed_qs.append(float(_lowerCamelCase   )   )

                # Here we implement the simple non-constant threshold for the predicted IG(X) value
                # We will decay the selectivity of our secondary learner filter from
                # 1 standard deviation above average to 1 below average after 10 batches.

                if global_step == 10:
                    _lowerCAmelCase       :   Union[str, Any]  =    -1
                if predicted_q < threshold:
                    _lowerCAmelCase       :   List[Any]  =    False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()   )   )
                _lowerCAmelCase       :   Optional[Any]  =    outputs[0]
                lm_loss.backward()
                examples += 1

            del outputs

            # Once the batch is filled with enough contexts, backprop on the batch.
            if examples == batch_size:
                torch.cuda.empty_cache()
                _lowerCAmelCase       :   str  =    0
                # Do LM backprop
                torch.nn.utils.clip_grad_norm_(model.parameters() ,   3.0   )
                lm_optimizer.step()
                lm_scheduler.step()  # Update learning rate schedule
                global_step += 1
                # Compute the performance of the transformer model at this batch
                if global_step % eval_interval == 0:
                    _lowerCAmelCase       :   Tuple  =    compute_perplexity(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
                    test_perps.append(_lowerCamelCase   )

                    print('Test perplexity, step' ,   _lowerCamelCase ,   ':' ,   _lowerCamelCase   )
            # Break out of the loop after 60 batches
            if max_steps > 0 and global_step > 60:
                break
        if max_steps > 0 and global_step > 60:
            break

    # save finetuned transformer model
    torch.save(model.state_dict() ,   _lowerCamelCase   )
    torch.cuda.empty_cache()
    # Do some cleaning up so we can reinitialize for the next run of this function
    del lm_optimizer
    del lm_scheduler
    return model

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'   )

    # Required parameters
    parser.add_argument(
        '--data_dir' ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help='The input data dir. Should contain data files for WikiText.' ,   )
    parser.add_argument(
        '--model_name_or_path' ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help='Path to pretrained model or model identifier from huggingface.co/models' ,   )
    parser.add_argument(
        '--data_file' ,   type=_lowerCamelCase ,   default=_lowerCamelCase ,   help=(
            'A jbl file containing tokenized data which can be split as objective dataset, '
            'train_dataset and test_dataset.'
        ) ,   )

    parser.add_argument(
        '--igf_data_file' ,   type=_lowerCamelCase ,   default=_lowerCamelCase ,   help='A jbl file containing the context and information gain pairs to train secondary learner.' ,   )

    parser.add_argument(
        '--output_dir' ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help='The output directory where the final fine-tuned model is stored.' ,   )

    parser.add_argument(
        '--tokenizer_name' ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   help='Pretrained tokenizer name or path if not the same as model_name' ,   )
    parser.add_argument('--seed' ,   type=_lowerCamelCase ,   default=_lowerCamelCase ,   help='A seed for reproducible training.'   )

    parser.add_argument(
        '--context_len' ,   default=32 ,   type=_lowerCamelCase ,   help=(
            'The maximum total input sequence length after tokenization. Sequences longer '
            'than this will be truncated, sequences shorter will be padded.'
        ) ,   )

    parser.add_argument(
        '--size_objective_set' ,   default=100 ,   type=_lowerCamelCase ,   help='number of articles that are long enough to be used as our objective set' ,   )
    parser.add_argument(
        '--eval_freq' ,   default=100 ,   type=_lowerCamelCase ,   help='secondary model evaluation is triggered at eval_freq'   )

    parser.add_argument('--max_steps' ,   default=1000 ,   type=_lowerCamelCase ,   help='To calculate training epochs'   )

    parser.add_argument(
        '--secondary_learner_batch_size' ,   default=128 ,   type=_lowerCamelCase ,   help='batch size of training data for secondary learner' ,   )

    parser.add_argument(
        '--batch_size' ,   default=16 ,   type=_lowerCamelCase ,   help='batch size of training data of language model(gpt2) '   )

    parser.add_argument(
        '--eval_interval' ,   default=10 ,   type=_lowerCamelCase ,   help=(
            'decay the selectivity of our secondary learner filter from'
            '1 standard deviation above average to 1 below average after 10 batches'
        ) ,   )

    parser.add_argument(
        '--number' ,   default=100 ,   type=_lowerCamelCase ,   help='The number of examples split to be used as objective_set/test_data'   )

    parser.add_argument(
        '--min_len' ,   default=1026 ,   type=_lowerCamelCase ,   help='The minimum length of the article to be used as objective set'   )

    parser.add_argument(
        '--secondary_learner_max_epochs' ,   default=15 ,   type=_lowerCamelCase ,   help='number of epochs to train secondary learner'   )

    parser.add_argument('--trim' ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   help='truncate the example if it exceeds context length'   )

    parser.add_argument(
        '--threshold' ,   default=1.0 ,   type=_lowerCamelCase ,   help=(
            'The threshold value used by secondary learner to filter the train_data and allow only'
            ' informative data as input to the model'
        ) ,   )

    parser.add_argument('--finetuned_model_name' ,   default='gpt2_finetuned.pt' ,   type=_lowerCamelCase ,   help='finetuned_model_name'   )

    parser.add_argument(
        '--recopy_model' ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   help='Reset the model to the original pretrained GPT-2 weights after each iteration' ,   )

    # function calls
    # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
    generate_n_pairs(
        context_len=32 ,   max_steps=10 ,   size_objective_set=100 ,   min_len=1026 ,   trim=_lowerCamelCase ,   data_file='data/tokenized_stories_train_wikitext103.jbl' ,   igf_data_file='igf_context_pairs.jbl' ,   )

    # Load train data for secondary learner
    _lowerCAmelCase       :   Optional[int]  =    joblib.load('data/IGF_values.jbl'   )

    # Train secondary learner
    _lowerCAmelCase       :   str  =    training_secondary_learner(
        _lowerCamelCase ,   secondary_learner_max_epochs=15 ,   secondary_learner_batch_size=128 ,   eval_freq=100 ,   igf_model_path='igf_model.pt' ,   )

    # load pretrained gpt2 model
    _lowerCAmelCase       :   Any  =    GPTaLMHeadModel.from_pretrained('gpt2'   )
    set_seed(42   )

    # Generate train and test data to train and evaluate gpt2 model
    _lowerCAmelCase,					_lowerCAmelCase       :   Any  =    generate_datasets(
        context_len=32 ,   file='data/tokenized_stories_train_wikitext103.jbl' ,   number=100 ,   min_len=1026 ,   trim=_lowerCamelCase   )

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   context_len=32 ,   max_steps=1000 ,   batch_size=16 ,   threshold=1.0 ,   recopy_model=_lowerCamelCase ,   secondary_learner=_lowerCamelCase ,   eval_interval=10 ,   finetuned_model_name='gpt2_finetuned.pt' ,   )


if __name__ == "__main__":
 main()








"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		(UniPCMultistepScheduler,)
  _UpperCAmelCase          =		(("num_inference_steps", 25),)

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_A      )
      return config

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   int  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Union[str, Any]  =    0.1 * sample
      _lowerCAmelCase       :   Tuple  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Optional[int]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )
          # copy over dummy past residuals
          _lowerCAmelCase       :   Tuple  =    dummy_past_residuals[: scheduler.config.solver_order]

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

          _lowerCAmelCase,					_lowerCAmelCase       :   str  =    sample, sample
          for t in range(_A    ,time_step + scheduler.config.solver_order + 1      ):
              _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
              _lowerCAmelCase       :   Union[str, 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 __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   List[str]  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Dict  =    0.1 * sample
      _lowerCAmelCase       :   str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Any  =    self.get_scheduler_config()
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )

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

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   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)
              _lowerCAmelCase       :   str  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Union[str, 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 __lowerCamelCase		(		self    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if scheduler is None:
          _lowerCAmelCase       :   int  =    self.scheduler_classes[0]
          _lowerCAmelCase       :   List[str]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
      _lowerCAmelCase       :   int  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   List[str]  =    10
      _lowerCAmelCase       :   str  =    self.dummy_model()
      _lowerCAmelCase       :   str  =    self.dummy_sample_deter
      scheduler.set_timesteps(_A      )

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

      return sample

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

      _lowerCAmelCase       :   Any  =    kwargs.pop('num_inference_steps'    ,_A      )

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   str  =    self.get_scheduler_config()
          _lowerCAmelCase       :   List[str]  =    scheduler_class(**_A      )

          _lowerCAmelCase       :   Any  =    self.dummy_sample
          _lowerCAmelCase       :   Tuple  =    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'      ):
              _lowerCAmelCase       :   Optional[Any]  =    num_inference_steps

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

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

          _lowerCAmelCase       :   List[str]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    UniPCMultistepScheduler(**self.get_scheduler_config()      )
      _lowerCAmelCase       :   Optional[Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

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

      _lowerCAmelCase       :   int  =    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   List[str]  =    DEISMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Tuple  =    DPMSolverMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Any  =    UniPCMultistepScheduler.from_config(scheduler.config      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   List[str]  =    torch.mean(torch.abs(_A      )      )

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for timesteps in [25, 50, 100, 999, 1000]:
          self.check_over_configs(num_train_timesteps=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(thresholding=_A      )
      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=_A    ,prediction_type=_A    ,sample_max_value=_A    ,solver_order=_A    ,solver_type=_A    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''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=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  _lowerCAmelCase       :   List[Any]  =    self.full_loop(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  assert not torch.isnan(_A      ).any(), "Samples have nan numbers"

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(lower_order_final=_A      )
      self.check_over_configs(lower_order_final=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
          self.check_over_forward(num_inference_steps=_A    ,time_step=0      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.full_loop()
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.full_loop(prediction_type='v_prediction'      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.mean(torch.abs(_A      )      )

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   int  =    self.get_scheduler_config(thresholding=_A    ,dynamic_thresholding_ratio=0      )
      _lowerCAmelCase       :   Tuple  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    10
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_model()
      _lowerCAmelCase       :   Dict  =    self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Tuple  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      assert sample.dtype == torch.floataa







  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   str  =    scheduler_class(**_A      )

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





