





'''simple docstring'''






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     (HeunDiscreteScheduler,)
					_SCREAMING_SNAKE_CASE				 =     10


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  **__snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Any	       =		{
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0_001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**__snake_case							)
											return config


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											for timesteps in [10, 50, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,  [0.0_002, 0.002, 0.02]							):
																	self.check_over_configs(beta_start=__snake_case							,  beta_end=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											for schedule in ["linear", "scaled_linear", "exp"]:
																	self.check_over_configs(beta_schedule=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											for clip_sample_range in [1.0, 2.0, 3.0]:
																	self.check_over_configs(clip_sample_range=__snake_case							,  clip_sample=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.scheduler_classes[0]
											_snake_case: int	       =		self.get_scheduler_config()
											_snake_case: Dict	       =		scheduler_class(**__snake_case							)

											scheduler.set_timesteps(self.num_inference_steps							)

											_snake_case: Optional[int]	       =		self.dummy_model()
											_snake_case: Dict	       =		self.dummy_sample_deter * scheduler.init_noise_sigma
											_snake_case: Optional[int]	       =		sample.to(__snake_case							)

											for i, t in enumerate(scheduler.timesteps							):
																	_snake_case: Tuple	       =		scheduler.scale_model_input(__snake_case							,  __snake_case							)

																	_snake_case: Union[str, Any]	       =		model(__snake_case							,  __snake_case							)

																	_snake_case: Optional[int]	       =		scheduler.step(__snake_case							,  __snake_case							,  __snake_case							)
																	_snake_case: str	       =		output.prev_sample

											_snake_case: List[Any]	       =		torch.sum(torch.abs(__snake_case							)							)
											_snake_case: int	       =		torch.mean(torch.abs(__snake_case							)							)

											if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 0.1_233							) < 1e-2
																	assert abs(result_mean.item() - 0.0_002							) < 1e-3
											else:
																	# CUDA
																	assert abs(result_sum.item() - 0.1_233							) < 1e-2
																	assert abs(result_mean.item() - 0.0_002							) < 1e-3


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: Tuple	       =		self.scheduler_classes[0]
											_snake_case: List[Any]	       =		self.get_scheduler_config(prediction_type='v_prediction'							)
											_snake_case: Any	       =		scheduler_class(**__snake_case							)

											scheduler.set_timesteps(self.num_inference_steps							)

											_snake_case: int	       =		self.dummy_model()
											_snake_case: Optional[Any]	       =		self.dummy_sample_deter * scheduler.init_noise_sigma
											_snake_case: Any	       =		sample.to(__snake_case							)

											for i, t in enumerate(scheduler.timesteps							):
																	_snake_case: Optional[int]	       =		scheduler.scale_model_input(__snake_case							,  __snake_case							)

																	_snake_case: Tuple	       =		model(__snake_case							,  __snake_case							)

																	_snake_case: Union[str, Any]	       =		scheduler.step(__snake_case							,  __snake_case							,  __snake_case							)
																	_snake_case: Optional[Any]	       =		output.prev_sample

											_snake_case: Optional[int]	       =		torch.sum(torch.abs(__snake_case							)							)
											_snake_case: int	       =		torch.mean(torch.abs(__snake_case							)							)

											if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
																	assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
											else:
																	# CUDA
																	assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
																	assert abs(result_mean.item() - 0.0_002							) < 1e-3


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.scheduler_classes[0]
											_snake_case: List[str]	       =		self.get_scheduler_config()
											_snake_case: Optional[int]	       =		scheduler_class(**__snake_case							)

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

											_snake_case: List[Any]	       =		self.dummy_model()
											_snake_case: Dict	       =		self.dummy_sample_deter.to(__snake_case							) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																	_snake_case: List[str]	       =		scheduler.scale_model_input(__snake_case							,  __snake_case							)

																	_snake_case: List[Any]	       =		model(__snake_case							,  __snake_case							)

																	_snake_case: Optional[int]	       =		scheduler.step(__snake_case							,  __snake_case							,  __snake_case							)
																	_snake_case: List[Any]	       =		output.prev_sample

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

											if str(__snake_case							).startswith('cpu'							):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 0.1_233							) < 1e-2
																	assert abs(result_mean.item() - 0.0_002							) < 1e-3
											elif str(__snake_case							).startswith('mps'							):
																	# Larger tolerance on mps
																	assert abs(result_mean.item() - 0.0_002							) < 1e-2
											else:
																	# CUDA
																	assert abs(result_sum.item() - 0.1_233							) < 1e-2
																	assert abs(result_mean.item() - 0.0_002							) < 1e-3




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: int	       =		self.scheduler_classes[0]
											_snake_case: Union[str, Any]	       =		self.get_scheduler_config()
											_snake_case: Dict	       =		scheduler_class(**__snake_case							,  use_karras_sigmas=__snake_case							)

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

											_snake_case: Tuple	       =		self.dummy_model()
											_snake_case: Union[str, Any]	       =		self.dummy_sample_deter.to(__snake_case							) * scheduler.init_noise_sigma
											_snake_case: Union[str, Any]	       =		sample.to(__snake_case							)

											for t in scheduler.timesteps:
																	_snake_case: str	       =		scheduler.scale_model_input(__snake_case							,  __snake_case							)

																	_snake_case: List[Any]	       =		model(__snake_case							,  __snake_case							)

																	_snake_case: Union[str, Any]	       =		scheduler.step(__snake_case							,  __snake_case							,  __snake_case							)
																	_snake_case: List[str]	       =		output.prev_sample

											_snake_case: str	       =		torch.sum(torch.abs(__snake_case							)							)
											_snake_case: int	       =		torch.mean(torch.abs(__snake_case							)							)

											assert abs(result_sum.item() - 0.00_015							) < 1e-2
											assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07							) < 1e-2












'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    lowercase_   (		lowercase__					,	lowercase__    ) ->List[str]:
						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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: str	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: Union[str, Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: List[str]	       =		features.copy() if features else default_expected_features
						_snake_case: str	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Optional[int]	       =		CsvDatasetReader(lowercase__					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						_snake_case: Dict	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	split=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'					,	[str, list]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						if issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Optional[Any]	       =		csv_path
						elif issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Tuple	       =		[csv_path]
						_snake_case: Tuple	       =		tmp_path / 'cache'
						_snake_case: Any	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Union[str, Any]	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__=("train",)    ) ->str:
						assert isinstance(lowercase__					,	lowercase__    )
						for split in splits:
												_snake_case: Optional[int]	       =		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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: Optional[Any]	       =		tmp_path / 'cache'
						_snake_case: Optional[Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Dict	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: str	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		features.copy() if features else default_expected_features
						_snake_case: int	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Tuple	       =		CsvDatasetReader({'train': csv_path}					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						if split:
												_snake_case: Optional[Any]	       =		{split: csv_path}
						else:
												_snake_case: Tuple	       =		{'train': csv_path, 'test': csv_path}
						_snake_case: Union[str, Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__					,	splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )





def    lowercase_   (		lowercase__    ) ->Union[str, Any]:
						with open(lowercase__					,	encoding='utf-8'    ) as csvfile:
												yield from csv.reader(lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: Optional[Any]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=1    ).write()

						_snake_case: Optional[int]	       =		iter_csv_file(lowercase__    )
						_snake_case: List[Any]	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						_snake_case: List[str]	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: List[str]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=2    ).write()

						_snake_case: Optional[Any]	       =		iter_csv_file(lowercase__    )
						_snake_case: Tuple	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Any:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: Any	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: str	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						with pytest.raises(lowercase__    ):
												CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=0    )





