


'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( __a	):



     """simple docstring"""






     __magic_name__    			=    (PNDMScheduler,)
     __magic_name__    			=    (("num_inference_steps", 5_0),)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

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

            config.update(**A__   )
            return config







     def a	( self      ,	snake_case__=0      ,	**snake_case__   ):

            '''simple docstring'''

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

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

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

                   _lowerCAmelCase    :							Tuple   			=       scheduler.step_prk(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample
                   _lowerCAmelCase    :							int   			=       new_scheduler.step_prk(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample

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

                   _lowerCAmelCase    :							List[Any]   			=       scheduler.step_plms(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample
                   _lowerCAmelCase    :							Optional[Any]   			=       new_scheduler.step_plms(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample

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







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self      ,	snake_case__=0      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       dict(self.forward_default_kwargs   )
            _lowerCAmelCase    :							List[Any]   			=       kwargs.pop('num_inference_steps'      ,	A__   )
            _lowerCAmelCase    :							List[str]   			=       self.dummy_sample
            _lowerCAmelCase    :							str   			=       0.1 * sample
            _lowerCAmelCase    :							Optional[Any]   			=       [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    :							Optional[int]   			=       scheduler_class(**A__   )
                   scheduler.set_timesteps(A__   )

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

                   with tempfile.TemporaryDirectory() as tmpdirname:
                          scheduler.save_config(A__   )
                          _lowerCAmelCase    :							Optional[Any]   			=       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    :							List[str]   			=       dummy_past_residuals[:]

                   _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.step_prk(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample
                   _lowerCAmelCase    :							Union[str, Any]   			=       new_scheduler.step_prk(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample

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

                   _lowerCAmelCase    :							List[Any]   			=       scheduler.step_plms(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample
                   _lowerCAmelCase    :							Union[str, Any]   			=       new_scheduler.step_plms(A__      ,	A__      ,	A__      ,	**A__   ).prev_sample

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







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							str   			=       self.get_scheduler_config(**A__   )
            _lowerCAmelCase    :							Optional[Any]   			=       scheduler_class(**A__   )

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

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

            for i, t in enumerate(scheduler.plms_timesteps   ):
                   _lowerCAmelCase    :							str   			=       model(A__      ,	A__   )
                   _lowerCAmelCase    :							int   			=       scheduler.step_plms(A__      ,	A__      ,	A__   ).prev_sample

            return sample







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       dict(self.forward_default_kwargs   )

            _lowerCAmelCase    :							Tuple   			=       kwargs.pop('num_inference_steps'      ,	A__   )

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

                   _lowerCAmelCase    :							List[Any]   			=       self.dummy_sample
                   _lowerCAmelCase    :							List[Any]   			=       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    :							Any   			=       num_inference_steps

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

                   _lowerCAmelCase    :							Dict   			=       scheduler.step_prk(A__      ,	0      ,	A__      ,	**A__   ).prev_sample
                   _lowerCAmelCase    :							Optional[Any]   			=       scheduler.step_prk(A__      ,	1      ,	A__      ,	**A__   ).prev_sample

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

                   _lowerCAmelCase    :							Any   			=       scheduler.step_plms(A__      ,	0      ,	A__      ,	**A__   ).prev_sample
                   _lowerCAmelCase    :							str   			=       scheduler.step_plms(A__      ,	1      ,	A__      ,	**A__   ).prev_sample

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Any   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(steps_offset=1   )
            _lowerCAmelCase    :							Optional[int]   			=       scheduler_class(**A__   )
            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 a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       27

            for scheduler_class in self.scheduler_classes:
                   _lowerCAmelCase    :							Dict   			=       self.dummy_sample
                   _lowerCAmelCase    :							Optional[int]   			=       0.1 * sample

                   _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
                   _lowerCAmelCase    :							Any   			=       scheduler_class(**A__   )

                   scheduler.set_timesteps(A__   )

                   # 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    :							Dict   			=       scheduler.step_prk(A__      ,	A__      ,	A__   ).prev_sample







     def a	( self   ):

            '''simple docstring'''

            with self.assertRaises(A__   ):
                   _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
                   _lowerCAmelCase    :							str   			=       self.get_scheduler_config()
                   _lowerCAmelCase    :							Tuple   			=       scheduler_class(**A__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.full_loop()
            _lowerCAmelCase    :							Optional[int]   			=       torch.sum(torch.abs(A__   )   )
            _lowerCAmelCase    :							Optional[int]   			=       torch.mean(torch.abs(A__   )   )

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







     def a	( self   ):

            '''simple docstring'''

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

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.full_loop(set_alpha_to_one=A__      ,	beta_start=0.01   )
            _lowerCAmelCase    :							Optional[int]   			=       torch.sum(torch.abs(A__   )   )
            _lowerCAmelCase    :							List[Any]   			=       torch.mean(torch.abs(A__   )   )

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







     def a	( self   ):

            '''simple docstring'''

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

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





'''simple docstring'''







import pytest

import datasets


# Import fixture modules as plugins
lowerCAmelCase    :     List[str]					=						["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       for item in items:
              if any(marker in item.keywords for marker in ['integration', 'unit']					):
                     continue
              item.add_marker(pytest.mark.unit					)






def 			lowercase				(_A					):







       """simple docstring"""







       config.addinivalue_line('markers'   ,						'torchaudio_latest: mark test to run with torchaudio>=0.12'					)






@pytest.fixture(autouse=_A					)
def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       tmp_path_factory.getbasetemp() / 'cache'
       _lowerCAmelCase    :							Dict   			=       test_hf_cache_home / 'datasets'
       _lowerCAmelCase    :							List[Any]   			=       test_hf_cache_home / 'metrics'
       _lowerCAmelCase    :							List[Any]   			=       test_hf_cache_home / 'modules'
       monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'   ,						str(_A					)					)
       monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'   ,						str(_A					)					)
       monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'   ,						str(_A					)					)
       _lowerCAmelCase    :							Dict   			=       test_hf_datasets_cache / 'downloads'
       monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'   ,						str(_A					)					)
       _lowerCAmelCase    :							Union[str, Any]   			=       test_hf_datasets_cache / 'downloads' / 'extracted'
       monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,						str(_A					)					)






@pytest.fixture(autouse=_A   ,						scope='session'					)
def 			lowercase				():







       """simple docstring"""







       datasets.disable_progress_bar()






@pytest.fixture(autouse=_A					)
def 			lowercase				(_A					):







       """simple docstring"""







       monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'   ,						_A					)






@pytest.fixture
def 			lowercase				(_A					):







       """simple docstring"""







       monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'   ,						_A					)

