




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (UniPCMultistepScheduler,)
    UpperCAmelCase__													=   (('''num_inference_steps''', 25),)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				**UpperCAmelCase__   :			str)		->Dict:





           '''simple docstring'''

           A__   				=					{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
               '''solver_type''': '''bh2''',
           }

           config.update(**UpperCAmelCase__)
           return config


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[str]=0      ,				**UpperCAmelCase__   :			Union[str, Any])		->str:





           '''simple docstring'''

           A__   				=					dict(self.forward_default_kwargs)
           A__   				=					kwargs.pop('''num_inference_steps'''      ,				UpperCAmelCase__)
           A__   				=					self.dummy_sample
           A__   				=					0.1 * sample
           A__   				=					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                  A__				,							A__   				=					sample, sample
                  for t in range(UpperCAmelCase__      ,				time_step + scheduler.config.solver_order + 1):
                         A__   				=					scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__).prev_sample
                         A__   				=					new_scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__).prev_sample

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


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[Any]=0      ,				**UpperCAmelCase__   :			List[str])		->Dict:





           '''simple docstring'''

           A__   				=					dict(self.forward_default_kwargs)
           A__   				=					kwargs.pop('''num_inference_steps'''      ,				UpperCAmelCase__)
           A__   				=					self.dummy_sample
           A__   				=					0.1 * sample
           A__   				=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                  A__   				=					self.get_scheduler_config()
                  A__   				=					scheduler_class(**UpperCAmelCase__)
                  scheduler.set_timesteps(UpperCAmelCase__)

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

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

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

                  A__   				=					scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__).prev_sample
                  A__   				=					new_scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__).prev_sample

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Any=None      ,				**UpperCAmelCase__   :			List[Any])		->List[str]:





           '''simple docstring'''

           if scheduler is None:
                  A__   				=					self.scheduler_classes[0]
                  A__   				=					self.get_scheduler_config(**UpperCAmelCase__)
                  A__   				=					scheduler_class(**UpperCAmelCase__)

           A__   				=					self.scheduler_classes[0]
           A__   				=					self.get_scheduler_config(**UpperCAmelCase__)
           A__   				=					scheduler_class(**UpperCAmelCase__)

           A__   				=					10
           A__   				=					self.dummy_model()
           A__   				=					self.dummy_sample_deter
           scheduler.set_timesteps(UpperCAmelCase__)

           for i, t in enumerate(scheduler.timesteps):
                  A__   				=					model(UpperCAmelCase__      ,				UpperCAmelCase__)
                  A__   				=					scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__).prev_sample

           return sample


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->int:





           '''simple docstring'''

           A__   				=					dict(self.forward_default_kwargs)

           A__   				=					kwargs.pop('''num_inference_steps'''      ,				UpperCAmelCase__)

           for scheduler_class in self.scheduler_classes:
                  A__   				=					self.get_scheduler_config()
                  A__   				=					scheduler_class(**UpperCAmelCase__)

                  A__   				=					self.dummy_sample
                  A__   				=					0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCAmelCase__      ,				'''set_timesteps'''):
                         scheduler.set_timesteps(UpperCAmelCase__)
                  elif num_inference_steps is not None and not hasattr(UpperCAmelCase__      ,				'''set_timesteps'''):
                         A__   				=					num_inference_steps

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

                  A__   				=					scheduler.timesteps[5]
                  A__   				=					scheduler.timesteps[6]

                  A__   				=					scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__).prev_sample
                  A__   				=					scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__).prev_sample

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->str:





           '''simple docstring'''

           A__   				=					UniPCMultistepScheduler(**self.get_scheduler_config())
           A__   				=					self.full_loop(scheduler=UpperCAmelCase__)
           A__   				=					torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_mean.item() - 0.2464) < 1e-3

           A__   				=					DPMSolverSinglestepScheduler.from_config(scheduler.config)
           A__   				=					DEISMultistepScheduler.from_config(scheduler.config)
           A__   				=					DPMSolverMultistepScheduler.from_config(scheduler.config)
           A__   				=					UniPCMultistepScheduler.from_config(scheduler.config)

           A__   				=					self.full_loop(scheduler=UpperCAmelCase__)
           A__   				=					torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_mean.item() - 0.2464) < 1e-3


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[str]:





           '''simple docstring'''

           for timesteps in [25, 50, 100, 999, 1_000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Tuple:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[str]:





           '''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=UpperCAmelCase__      ,				solver_type=UpperCAmelCase__      ,				prediction_type=UpperCAmelCase__      ,				)
                                A__   				=					self.full_loop(
                                    solver_order=UpperCAmelCase__      ,				solver_type=UpperCAmelCase__      ,				prediction_type=UpperCAmelCase__      ,				)
                                assert not torch.isnan(UpperCAmelCase__).any(), "Samples have nan numbers"


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           self.check_over_configs(lower_order_final=UpperCAmelCase__)
           self.check_over_configs(lower_order_final=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->str:





           '''simple docstring'''

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
                  self.check_over_forward(num_inference_steps=UpperCAmelCase__      ,				time_step=0)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					self.full_loop()
           A__   				=					torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_mean.item() - 0.2464) < 1e-3


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Tuple:





           '''simple docstring'''

           A__   				=					self.full_loop(prediction_type='''v_prediction''')
           A__   				=					torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_mean.item() - 0.1014) < 1e-3


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Dict:





           '''simple docstring'''

           A__   				=					self.scheduler_classes[0]
           A__   				=					self.get_scheduler_config(thresholding=UpperCAmelCase__      ,				dynamic_thresholding_ratio=0)
           A__   				=					scheduler_class(**UpperCAmelCase__)

           A__   				=					10
           A__   				=					self.dummy_model()
           A__   				=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(UpperCAmelCase__)

           for i, t in enumerate(scheduler.timesteps):
                  A__   				=					model(UpperCAmelCase__      ,				UpperCAmelCase__)
                  A__   				=					scheduler.step(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__).prev_sample

           assert sample.dtype == torch.floataa





    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				**UpperCAmelCase__   :			Optional[Any])		->Optional[int]:





           '''simple docstring'''

           for scheduler_class in self.scheduler_classes:
                  A__   				=					self.get_scheduler_config(**UpperCAmelCase__)
                  A__   				=					scheduler_class(**UpperCAmelCase__)

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








import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''				,       [False, True]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_				,       keep_in_memory=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@pytest.mark.parametrize(
    '''features'''				,       [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,       )
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					features.copy() if features else default_expected_features
       A__   				=					(
           Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader(lowercase_				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_				,       split=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''				,       [str, list]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       if issubclass(lowercase_				,       lowercase_		):
              A__   				=					parquet_path
       elif issubclass(lowercase_				,       lowercase_		):
              A__   				=					[parquet_path]
       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=("train",)		)     -> List[str]:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       for split in splits:
              A__   				=					dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''				,       [False, True]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__   				=					ParquetDatasetReader(
                  {'''train''': parquet_path}				,       cache_dir=lowercase_				,       keep_in_memory=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_		)







@pytest.mark.parametrize(
    '''features'''				,       [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,       )
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					features.copy() if features else default_expected_features
       A__   				=					(
           Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader({'''train''': parquet_path}				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       if split:
              A__   				=					{split: parquet_path}
       else:
              A__   				=					'''train'''
              A__   				=					{'''train''': parquet_path, '''test''': parquet_path}
       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_				,       splits=list(path.keys()		)		)
       assert all(dataset[split].split == split for split in path.keys()		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					ParquetDatasetWriter(lowercase_				,       tmp_path / '''foo.parquet'''		)
       assert writer.write() > 0
       A__   				=					pq.ParquetFile(tmp_path / '''foo.parquet'''		)
       A__   				=					pf.read()
       assert dataset.data.table == output_table







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					str(shared_datadir / '''test_image_rgb.jpg'''		)
       A__   				=					{'''image''': [image_path]}
       A__   				=					Features({'''image''': Image()}		)
       A__   				=					Dataset.from_dict(lowercase_				,       features=lowercase_		)
       A__   				=					ParquetDatasetWriter(lowercase_				,       tmp_path / '''foo.parquet'''		)
       assert writer.write() > 0

       A__   				=					Dataset.from_parquet(str(tmp_path / '''foo.parquet'''		)		)
       assert dataset.features == reloaded_dataset.features

       A__   				=					ParquetDatasetReader(str(tmp_path / '''foo.parquet'''		)				,       streaming=lowercase_		).read()
       assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    '''feature, expected'''				,       [
        (Features({'''foo''': Value('''int32'''		)}		), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]				,       )
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert get_writer_batch_size(lowercase_		) == expected


