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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				str			):
						assert isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							str  =		tmp_path / """cache"""
						UpperCamelCase_		:							Union[str, 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():
												UpperCamelCase_		:							Dict  =		ParquetDatasetReader(_SCREAMING_SNAKE_CASE		, cache_dir=_SCREAMING_SNAKE_CASE		, keep_in_memory=_SCREAMING_SNAKE_CASE			).read()
						_check_parquet_dataset(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)






@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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				List[Any]			):
						UpperCamelCase_		:							Optional[Any]  =		tmp_path / """cache"""
						UpperCamelCase_		:							Union[str, Any]  =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase_		:							Optional[int]  =		features.copy() if features else default_expected_features
						UpperCamelCase_		:							Dict  =		(
						    Features({feature: Value(_SCREAMING_SNAKE_CASE			) for feature, dtype in features.items()}			) if features is not None else None
						)
						UpperCamelCase_		:							str  =		ParquetDatasetReader(_SCREAMING_SNAKE_CASE		, features=_SCREAMING_SNAKE_CASE		, cache_dir=_SCREAMING_SNAKE_CASE			).read()
						_check_parquet_dataset(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)






@pytest.mark.parametrize("""split"""		, [None, NamedSplit("""train"""			), """train""", """test"""]			)
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Optional[int]  =		tmp_path / """cache"""
						UpperCamelCase_		:							Tuple  =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase_		:							Dict  =		ParquetDatasetReader(_SCREAMING_SNAKE_CASE		, cache_dir=_SCREAMING_SNAKE_CASE		, split=_SCREAMING_SNAKE_CASE			).read()
						_check_parquet_dataset(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""		, [str, list]			)
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Optional[int]			):
						if issubclass(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
												UpperCamelCase_		:							Any  =		parquet_path
						elif issubclass(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
												UpperCamelCase_		:							Optional[Any]  =		[parquet_path]
						UpperCamelCase_		:							int  =		tmp_path / """cache"""
						UpperCamelCase_		:							Optional[Any]  =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase_		:							Union[str, Any]  =		ParquetDatasetReader(_SCREAMING_SNAKE_CASE		, cache_dir=_SCREAMING_SNAKE_CASE			).read()
						_check_parquet_dataset(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				List[str]=("train",)			):
						assert isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						for split in splits:
												UpperCamelCase_		:							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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				List[Any]			):
						UpperCamelCase_		:							Dict  =		tmp_path / """cache"""
						UpperCamelCase_		:							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():
												UpperCamelCase_		:							Optional[int]  =		ParquetDatasetReader(
												    {"""train""": parquet_path}		, cache_dir=_SCREAMING_SNAKE_CASE		, keep_in_memory=_SCREAMING_SNAKE_CASE			).read()
						_check_parquet_datasetdict(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)






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






@pytest.mark.parametrize("""split"""		, [None, NamedSplit("""train"""			), """train""", """test"""]			)
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Dict			):
						if split:
												UpperCamelCase_		:							List[str]  =		{split: parquet_path}
						else:
												UpperCamelCase_		:							int  =		"""train"""
												UpperCamelCase_		:							Any  =		{"""train""": parquet_path, """test""": parquet_path}
						UpperCamelCase_		:							str  =		tmp_path / """cache"""
						UpperCamelCase_		:							Any  =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase_		:							List[Any]  =		ParquetDatasetReader(_SCREAMING_SNAKE_CASE		, cache_dir=_SCREAMING_SNAKE_CASE			).read()
						_check_parquet_datasetdict(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, splits=list(path.keys()			)			)
						assert all(dataset[split].split == split for split in path.keys()			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							List[Any]  =		ParquetDatasetWriter(_SCREAMING_SNAKE_CASE		, tmp_path / """foo.parquet"""			)
						assert writer.write() > 0
						UpperCamelCase_		:							Any  =		pq.ParquetFile(tmp_path / """foo.parquet"""			)
						UpperCamelCase_		:							Union[str, Any]  =		pf.read()
						assert dataset.data.table == output_table






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Dict			):
						UpperCamelCase_		:							str  =		str(shared_datadir / """test_image_rgb.jpg"""			)
						UpperCamelCase_		:							int  =		{"""image""": [image_path]}
						UpperCamelCase_		:							Tuple  =		Features({"""image""": Image()}			)
						UpperCamelCase_		:							List[str]  =		Dataset.from_dict(_SCREAMING_SNAKE_CASE		, features=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Union[str, Any]  =		ParquetDatasetWriter(_SCREAMING_SNAKE_CASE		, tmp_path / """foo.parquet"""			)
						assert writer.write() > 0

						UpperCamelCase_		:							Tuple  =		Dataset.from_parquet(str(tmp_path / """foo.parquet"""			)			)
						assert dataset.features == reloaded_dataset.features

						UpperCamelCase_		:							Union[str, Any]  =		ParquetDatasetReader(str(tmp_path / """foo.parquet"""			)		, streaming=_SCREAMING_SNAKE_CASE			).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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				int			):
						assert get_writer_batch_size(_SCREAMING_SNAKE_CASE			) == expected







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






