



"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__snake_case							:     int   =					get_logger(__name__)



class     A__     (						enum.Enum							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'all_checks'
	SCREAMING_SNAKE_CASE					   =			'basic_checks'
	SCREAMING_SNAKE_CASE					   =			'no_checks'






class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=None			)		->     List[Any]:
			if expected_checksums is None:
						logger.info("Unable to verify checksums."			)
						return
			if len(set(__snake_case			) - set(__snake_case			)			) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(__snake_case			) - set(__snake_case			)			)			)
			if len(set(__snake_case			) - set(__snake_case			)			) > 0:
						raise UnexpectedDownloadedFile(str(set(__snake_case			) - set(__snake_case			)			)			)
			__lowerCAmelCase   :						Any				= [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__lowerCAmelCase   :						str				= " for " + verification_name if verification_name is not None else ""
			if len(__snake_case			) > 0:
						raise NonMatchingChecksumError(
						    F"""Checksums didn't match{for_verification_name}:\n"""
						    F"""{bad_urls}\n"""
						    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"			)
			logger.info("All the checksums matched successfully" + for_verification_name			)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''











def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			if expected_splits is None:
						logger.info("Unable to verify splits sizes."			)
						return
			if len(set(__snake_case			) - set(__snake_case			)			) > 0:
						raise ExpectedMoreSplits(str(set(__snake_case			) - set(__snake_case			)			)			)
			if len(set(__snake_case			) - set(__snake_case			)			) > 0:
						raise UnexpectedSplits(str(set(__snake_case			) - set(__snake_case			)			)			)
			__lowerCAmelCase   :						int				= [
			    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(__snake_case			) > 0:
						raise NonMatchingSplitsSizesError(str(__snake_case			)			)
			logger.info("All the splits matched successfully."			)


def     _lowercase       (						__snake_case      ,__snake_case = True			)		->     dict:
			if record_checksum:
						__lowerCAmelCase   :						int				= shaaaa()
						with open(__snake_case      ,"rb"			) as f:
									for chunk in iter(lambda: f.read(1 << 20			)      ,B""			):
												m.update(__snake_case			)
									__lowerCAmelCase   :						Optional[Any]				= m.hexdigest()
			else:
						__lowerCAmelCase   :						Optional[int]				= None
			return {"num_bytes": os.path.getsize(__snake_case			), "checksum": checksum}





def     _lowercase       (						__snake_case			)		->     Dict:
			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False




"""simple docstring"""






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     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			assert isinstance(__snake_case      ,__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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Dict				= tmp_path / "cache"
			__lowerCAmelCase   :						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():
						__lowerCAmelCase   :						Optional[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Tuple				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						Dict				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						Optional[int]				= ParquetDatasetReader(__snake_case      ,features=__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,split=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)
			assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"      ,[str, list]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			if issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= parquet_path
			elif issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= [parquet_path]
			__lowerCAmelCase   :						str				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						Tuple				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=("train",)			)		->     int:
			assert isinstance(__snake_case      ,__snake_case			)
			for split in splits:
						__lowerCAmelCase   :						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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Any				= tmp_path / "cache"
			__lowerCAmelCase   :						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():
						__lowerCAmelCase   :						str				= ParquetDatasetReader(
						    {"train": parquet_path}      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						List[Any]				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						str				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						str				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						str				= ParquetDatasetReader({"train": parquet_path}      ,features=__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__snake_case			)


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


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						int				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
			assert writer.write() > 0
			__lowerCAmelCase   :						str				= pq.ParquetFile(tmp_path / "foo.parquet"			)
			__lowerCAmelCase   :						int				= pf.read()
			assert dataset.data.table == output_table


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

			__lowerCAmelCase   :						List[Any]				= Dataset.from_parquet(str(tmp_path / "foo.parquet"			)			)
			assert dataset.features == reloaded_dataset.features

			__lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(str(tmp_path / "foo.parquet"			)      ,streaming=__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     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
			assert get_writer_batch_size(__snake_case			) == expected