


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[int]       =		(DPMSolverSDEScheduler,)
      snake_case__       :  List[str]       =		10
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    Tuple:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_1_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'noise_sampler_seed': 0,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Any:
             for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]  ,							[0.0002, 0.002, 0.02]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Any:
             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[Any]:
             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	Dict							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  )

             a_      :	str							=						self.dummy_model()
             a_      :	Union[str, Any]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
             a_      :	List[Any]							=						sample.to(SCREAMING_SNAKE_CASE__  )

             for i, t in enumerate(scheduler.timesteps  ):
                    a_      :	Dict							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	List[str]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	str							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[int]							=						output.prev_sample

             a_      :	Union[str, Any]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 167.47821044921875  ) < 1E-2
                    assert abs(result_mean.item() - 0.2178705964565277  ) < 1E-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 171.59352111816406  ) < 1E-2
                    assert abs(result_mean.item() - 0.22342906892299652  ) < 1E-3
             else:
                    assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
                    assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             a_      :	Optional[Any]							=						self.scheduler_classes[0]
             a_      :	Tuple							=						self.get_scheduler_config(prediction_type='v_prediction'  )
             a_      :	List[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	List[str]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
             a_      :	Union[str, Any]							=						sample.to(SCREAMING_SNAKE_CASE__  )

             for i, t in enumerate(scheduler.timesteps  ):
                    a_      :	int							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Optional[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	List[str]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	str							=						output.prev_sample

             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 124.77149200439453  ) < 1E-2
                    assert abs(result_mean.item() - 0.16226289014816284  ) < 1E-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 128.1663360595703  ) < 1E-2
                    assert abs(result_mean.item() - 0.16688326001167297  ) < 1E-3
             else:
                    assert abs(result_sum.item() - 119.8487548828125  ) < 1E-2
                    assert abs(result_mean.item() - 0.1560530662536621  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Union[str, Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  ,							device=SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__  ) * scheduler.init_noise_sigma

             for t in scheduler.timesteps:
                    a_      :	Optional[Any]							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Union[str, Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Any							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	List[Any]							=						output.prev_sample

             a_      :	Tuple							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 167.46957397460938  ) < 1E-2
                    assert abs(result_mean.item() - 0.21805934607982635  ) < 1E-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 171.59353637695312  ) < 1E-2
                    assert abs(result_mean.item() - 0.22342908382415771  ) < 1E-3
             else:
                    assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
                    assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config()
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  ,							use_karras_sigmas=SCREAMING_SNAKE_CASE__  )

             scheduler.set_timesteps(self.num_inference_steps  ,							device=SCREAMING_SNAKE_CASE__  )

             a_      :	Dict							=						self.dummy_model()
             a_      :	str							=						self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__  ) * scheduler.init_noise_sigma
             a_      :	int							=						sample.to(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	Optional[int]							=						scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	str							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						output.prev_sample

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 176.66974135742188  ) < 1E-2
                    assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 177.63653564453125  ) < 1E-2
                    assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
             else:
                    assert abs(result_sum.item() - 170.3135223388672  ) < 1E-2
                    assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2






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_		(    __A				:    Union[str, Any]						,					__A				:    Tuple       )			->	Any:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       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_		(    __A				:    List[Any]						,					__A				:    Any						,					__A				:    Tuple       )			->	Tuple:

       """simple docstring"""

       a_      :	Dict							=						tmp_path / 'cache'
       a_      :	int							=						{'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_      :	Union[str, Any]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@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_		(    __A				:    str						,					__A				:    Dict						,					__A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

       a_      :	Tuple							=						tmp_path / 'cache'
       a_      :	str							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	List[str]							=						features.copy() if features else default_expected_features
       a_      :	int							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						ParquetDatasetReader(__A						,					features=__A						,					cache_dir=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    List[str]						,					__A				:    int       )			->	List[str]:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	str							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	List[str]							=						ParquetDatasetReader(__A						,					cache_dir=__A						,					split=__A       ).read()
       _check_parquet_dataset(__A						,					__A       )
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    str						,					__A				:    Optional[int]       )			->	Any:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Dict						,					__A				:    Optional[Any]=("train",)       )			->	Optional[int]:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       for split in splits:
              a_      :	str							=						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_		(    __A				:    Optional[Any]						,					__A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Union[str, Any]							=						tmp_path / 'cache'
       a_      :	Any							=						{'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_      :	Tuple							=						ParquetDatasetReader(
                  {'train': parquet_path}						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A       )







@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_		(    __A				:    Any						,					__A				:    Optional[int]						,					__A				:    Tuple       )			->	List[Any]:

       """simple docstring"""

       a_      :	Optional[Any]							=						tmp_path / 'cache'
       a_      :	List[str]							=						{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       a_      :	Optional[int]							=						features.copy() if features else default_expected_features
       a_      :	Tuple							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	Optional[Any]							=						ParquetDatasetReader({'train': parquet_path}						,					features=__A						,					cache_dir=__A       ).read()
       _check_parquet_datasetdict(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[Any]						,					__A				:    Optional[Any]       )			->	Any:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

       a_      :	List[str]							=						ParquetDatasetWriter(__A						,					tmp_path / 'foo.parquet'       )
       assert writer.write() > 0
       a_      :	List[str]							=						pq.ParquetFile(tmp_path / 'foo.parquet'       )
       a_      :	Dict							=						pf.read()
       assert dataset.data.table == output_table







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any       )			->	Optional[int]:

       """simple docstring"""

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

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

       a_      :	str							=						ParquetDatasetReader(str(tmp_path / 'foo.parquet'       )						,					streaming=__A       ).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_		(    __A				:    int						,					__A				:    List[str]       )			->	List[str]:

       """simple docstring"""

       assert get_writer_batch_size(__A       ) == expected


