




"""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 					a__				(       snake_case__ ,				snake_case__       )		->      str:
	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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      str:
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""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       =      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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Tuple:
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	lowerCamelCase       =      features.copy() if features else default_expected_features
	lowerCamelCase       =      (
	    Features({feature: Value(snake_case__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	lowerCamelCase       =      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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Tuple:
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	lowerCamelCase       =      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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      str:
	if issubclass(snake_case__ ,				snake_case__       ):
		lowerCamelCase       =      parquet_path
	elif issubclass(snake_case__ ,				snake_case__       ):
		lowerCamelCase       =      [parquet_path]
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	lowerCamelCase       =      ParquetDatasetReader(snake_case__ ,				cache_dir=snake_case__       ).read()
	_check_parquet_dataset(snake_case__ ,				snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__=("train",)       )		->      Optional[Any]:
	assert isinstance(snake_case__ ,				snake_case__       )
	for split in splits:
		lowerCamelCase       =      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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      Any:
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""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       =      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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[str]:
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	lowerCamelCase       =      features.copy() if features else default_expected_features
	lowerCamelCase       =      (
	    Features({feature: Value(snake_case__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	lowerCamelCase       =      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 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[Any]:
	if split:
		lowerCamelCase       =      {split: parquet_path}
	else:
		lowerCamelCase       =      """train"""
		lowerCamelCase       =      {"""train""": parquet_path, """test""": parquet_path}
	lowerCamelCase       =      tmp_path / """cache"""
	lowerCamelCase       =      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	lowerCamelCase       =      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 					a__				(       snake_case__ ,				snake_case__       )		->      List[str]:
	lowerCamelCase       =      ParquetDatasetWriter(snake_case__ ,				tmp_path / """foo.parquet"""       )
	assert writer.write() > 0
	lowerCamelCase       =      pq.ParquetFile(tmp_path / """foo.parquet"""       )
	lowerCamelCase       =      pf.read()
	assert dataset.data.table == output_table



def 					a__				(       snake_case__ ,				snake_case__       )		->      str:
	lowerCamelCase       =      str(shared_datadir / """test_image_rgb.jpg"""       )
	lowerCamelCase       =      {"""image""": [image_path]}
	lowerCamelCase       =      Features({"""image""": Image()}       )
	lowerCamelCase       =      Dataset.from_dict(snake_case__ ,				features=snake_case__       )
	lowerCamelCase       =      ParquetDatasetWriter(snake_case__ ,				tmp_path / """foo.parquet"""       )
	assert writer.write() > 0

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

	lowerCamelCase       =      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 					a__				(       snake_case__ ,				snake_case__       )		->      Tuple:
	assert get_writer_batch_size(snake_case__       ) == expected









"""simple docstring"""




import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase							:    List[str]  							=							4
lowerCAmelCase							:    List[str]  							=							3






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				pass




def 					a__				(       snake_case__       )		->      Dict:
			for shard in shards:
						for i in range(snake_case__       ):
									yield {"i": i, "shard": shard}



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      int(os.environ["""RANK"""]       )
			lowerCamelCase       =      int(os.environ["""WORLD_SIZE"""]       )

			lowerCamelCase       =      ArgumentParser()
			parser.add_argument("""--streaming""" ,				type=snake_case__       )
			parser.add_argument("""--local_rank""" ,				type=snake_case__       )
			parser.add_argument("""--num_workers""" ,				type=snake_case__ ,				default=0       )
			lowerCamelCase       =      parser.parse_args()
			lowerCamelCase       =      args.streaming
			lowerCamelCase       =      args.num_workers

			lowerCamelCase       =      {"""shards""": [F'shard_{shard_idx}' for shard_idx in range(snake_case__       )]}
			lowerCamelCase       =      IterableDataset.from_generator(snake_case__ ,				gen_kwargs=snake_case__       )
			if not streaming:
						lowerCamelCase       =      Dataset.from_list(list(snake_case__       )       )

			lowerCamelCase       =      split_dataset_by_node(snake_case__ ,				rank=snake_case__ ,				world_size=snake_case__       )
			lowerCamelCase       =      torch.utils.data.DataLoader(snake_case__ ,				num_workers=snake_case__       )

			lowerCamelCase       =      NUM_SHARDS * NUM_ITEMS_PER_SHARD
			lowerCamelCase       =      full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)       )

			lowerCamelCase       =      sum(1 for _ in dataloader       )
			if local_size != expected_local_size:
						raise FailedTestError(F'local_size {local_size} != expected_local_size {expected_local_size}'       )


if __name__ == "__main__":
					main()



