

import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
    from transformers.trainer import DEFAULT_CALLBACKS

    from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class  _A		(  __magic_name__):

  def __init__(				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     []

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_init_end'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_train_begin'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_train_end'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_epoch_begin'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_epoch_end'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_step_begin'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_step_end'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_evaluate'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_predict'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_save'					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_log'					)



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.events.append('on_prediction_step'					)

@require_torch
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     tempfile.mkdtemp()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        shutil.rmtree(self.output_dir					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE=64 ,					_SCREAMING_SNAKE_CASE=64 ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     RegressionDataset(length=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     RegressionDataset(length=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     RegressionModelConfig(a=_SCREAMING_SNAKE_CASE ,					b=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     RegressionPreTrainedModel(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     TrainingArguments(self.output_dir ,					disable_tqdm=_SCREAMING_SNAKE_CASE ,					report_to=[] ,					**_SCREAMING_SNAKE_CASE					)
        return Trainer(
            _SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					train_dataset=_SCREAMING_SNAKE_CASE ,					eval_dataset=_SCREAMING_SNAKE_CASE ,					callbacks=_SCREAMING_SNAKE_CASE ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.assertEqual(len(_SCREAMING_SNAKE_CASE					) ,					len(_SCREAMING_SNAKE_CASE					)					)

        # Order doesn't matter
        SCREAMING_SNAKE_CASE_				:  List[str]           =     sorted(_SCREAMING_SNAKE_CASE ,					key=lambda _SCREAMING_SNAKE_CASE					: cb.__name__ if isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) else cb.__class__.__name__					)
        SCREAMING_SNAKE_CASE_				:  Any           =     sorted(_SCREAMING_SNAKE_CASE ,					key=lambda _SCREAMING_SNAKE_CASE					: cb.__name__ if isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) else cb.__class__.__name__					)

        for cba, cba in zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              if isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) and isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) and not isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    self.assertEqual(_SCREAMING_SNAKE_CASE ,					cba.__class__					)
              elif not isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) and isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    self.assertEqual(cba.__class__ ,					_SCREAMING_SNAKE_CASE					)
              else:
                    self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ['on_init_end', 'on_train_begin']
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     len(trainer.get_eval_dataloader()					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     ['on_prediction_step'] * len(trainer.get_eval_dataloader()					) + ['on_log', 'on_evaluate']
        for _ in range(trainer.state.num_train_epochs					):
              expected_events.append('on_epoch_begin'					)
              for _ in range(_SCREAMING_SNAKE_CASE					):
                    step += 1
                    expected_events += ["on_step_begin", "on_step_end"]
                    if step % trainer.args.logging_steps == 0:
                          expected_events.append('on_log'					)
                    if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                          expected_events += evaluation_events.copy()
                    if step % trainer.args.save_steps == 0:
                          expected_events.append('on_save'					)
              expected_events.append('on_epoch_end'					)
              if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                    expected_events += evaluation_events.copy()
        expected_events += ["on_log", "on_train_end"]
        return expected_events

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_trainer()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        # Callbacks passed at init are added to the default callbacks
        SCREAMING_SNAKE_CASE_				:  int           =     self.get_trainer(callbacks=[MyTestTrainerCallback]					)
        expected_callbacks.append(_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_trainer(disable_tqdm=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     DEFAULT_CALLBACKS.copy() + [PrinterCallback]
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_trainer()

        # We can add, pop, or remove by class name
        trainer.remove_callback(_SCREAMING_SNAKE_CASE					)
        expected_callbacks.remove(_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     self.get_trainer()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     trainer.pop_callback(_SCREAMING_SNAKE_CASE					)
        self.assertEqual(cb.__class__ ,					_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        trainer.add_callback(_SCREAMING_SNAKE_CASE					)
        expected_callbacks.insert(0 ,					_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        # We can also add, pop, or remove by instance
        SCREAMING_SNAKE_CASE_				:  str           =     self.get_trainer()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     trainer.callback_handler.callbacks[0]
        trainer.remove_callback(_SCREAMING_SNAKE_CASE					)
        expected_callbacks.remove(_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_trainer()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     trainer.callback_handler.callbacks[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     trainer.pop_callback(_SCREAMING_SNAKE_CASE					)
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)

        trainer.add_callback(_SCREAMING_SNAKE_CASE					)
        expected_callbacks.insert(0 ,					_SCREAMING_SNAKE_CASE					)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_SCREAMING_SNAKE_CASE					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        import warnings

        # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
        warnings.simplefilter(action='ignore' ,					category=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  int           =     self.get_trainer(callbacks=[MyTestTrainerCallback]					)
        trainer.train()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     trainer.callback_handler.callbacks[-2].events
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					self.get_expected_events(_SCREAMING_SNAKE_CASE					)					)

        # Independent log/save/eval
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_trainer(callbacks=[MyTestTrainerCallback] ,					logging_steps=5					)
        trainer.train()
        SCREAMING_SNAKE_CASE_				:  Any           =     trainer.callback_handler.callbacks[-2].events
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					self.get_expected_events(_SCREAMING_SNAKE_CASE					)					)

        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_trainer(callbacks=[MyTestTrainerCallback] ,					save_steps=5					)
        trainer.train()
        SCREAMING_SNAKE_CASE_				:  int           =     trainer.callback_handler.callbacks[-2].events
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					self.get_expected_events(_SCREAMING_SNAKE_CASE					)					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_trainer(callbacks=[MyTestTrainerCallback] ,					eval_steps=5 ,					evaluation_strategy='steps'					)
        trainer.train()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     trainer.callback_handler.callbacks[-2].events
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					self.get_expected_events(_SCREAMING_SNAKE_CASE					)					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_trainer(callbacks=[MyTestTrainerCallback] ,					evaluation_strategy='epoch'					)
        trainer.train()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     trainer.callback_handler.callbacks[-2].events
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					self.get_expected_events(_SCREAMING_SNAKE_CASE					)					)

        # A bit of everything
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_trainer(
            callbacks=[MyTestTrainerCallback] ,					logging_steps=3 ,					save_steps=10 ,					eval_steps=5 ,					evaluation_strategy='steps' ,					)
        trainer.train()
        SCREAMING_SNAKE_CASE_				:  int           =     trainer.callback_handler.callbacks[-2].events
        self.assertEqual(_SCREAMING_SNAKE_CASE ,					self.get_expected_events(_SCREAMING_SNAKE_CASE					)					)

        # warning should be emitted for duplicated callbacks
        with patch('transformers.trainer_callback.logger.warning'					) as warn_mock:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_trainer(
                  callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] ,					)
              assert str(_SCREAMING_SNAKE_CASE					) in warn_mock.call_args[0][0]







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  (PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :      Dict       =  (('''num_inference_steps''', 50),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Any           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  int           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

                    # copy over dummy past residual (must be after setting timesteps)
                    SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  List[str]           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     10
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.prk_timesteps					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_inference_steps

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

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(steps_offset=1					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
        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  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # 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]					):
                    SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

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



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

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






