

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(lowercase__			, lowercase__	):





	'''simple docstring'''
	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 a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case_      		= ParquetDatasetReader(lowercase__			, cache_dir=lowercase__			, keep_in_memory=lowercase__	).read()
	_check_parquet_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 a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case_      		= features.copy() if features else default_expected_features
	snake_case_      		= (
	    Features({feature: Value(lowercase__	) for feature, dtype in features.items()}	) if features is not None else None
	)
	snake_case_      		= ParquetDatasetReader(lowercase__			, features=lowercase__			, cache_dir=lowercase__	).read()
	_check_parquet_dataset(lowercase__			, lowercase__	)





@pytest.mark.parametrize('split'			, [None, NamedSplit('train'	), 'train', 'test']	)
def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case_      		= ParquetDatasetReader(lowercase__			, cache_dir=lowercase__			, split=lowercase__	).read()
	_check_parquet_dataset(lowercase__			, lowercase__	)
	assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'			, [str, list]	)
def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	if issubclass(lowercase__			, lowercase__	):
		snake_case_      		= parquet_path
	elif issubclass(lowercase__			, lowercase__	):
		snake_case_      		= [parquet_path]
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case_      		= ParquetDatasetReader(lowercase__			, cache_dir=lowercase__	).read()
	_check_parquet_dataset(lowercase__			, lowercase__	)





def a(lowercase__			, lowercase__			, lowercase__=("train",)	):





	'''simple docstring'''
	assert isinstance(lowercase__			, lowercase__	)
	for split in splits:
		snake_case_      		= 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(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case_      		= ParquetDatasetReader(
		    {'train': parquet_path}			, cache_dir=lowercase__			, keep_in_memory=lowercase__	).read()
	_check_parquet_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 a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case_      		= features.copy() if features else default_expected_features
	snake_case_      		= (
	    Features({feature: Value(lowercase__	) for feature, dtype in features.items()}	) if features is not None else None
	)
	snake_case_      		= ParquetDatasetReader({'train': parquet_path}			, features=lowercase__			, cache_dir=lowercase__	).read()
	_check_parquet_datasetdict(lowercase__			, lowercase__	)





@pytest.mark.parametrize('split'			, [None, NamedSplit('train'	), 'train', 'test']	)
def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	if split:
		snake_case_      		= {split: parquet_path}
	else:
		snake_case_      		= 'train'
		snake_case_      		= {'train': parquet_path, 'test': parquet_path}
	snake_case_      		= tmp_path / 'cache'
	snake_case_      		= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case_      		= ParquetDatasetReader(lowercase__			, cache_dir=lowercase__	).read()
	_check_parquet_datasetdict(lowercase__			, lowercase__			, splits=list(path.keys()	)	)
	assert all(dataset[split].split == split for split in path.keys()	)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= ParquetDatasetWriter(lowercase__			, tmp_path / 'foo.parquet'	)
	assert writer.write() > 0
	snake_case_      		= pq.ParquetFile(tmp_path / 'foo.parquet'	)
	snake_case_      		= pf.read()
	assert dataset.data.table == output_table





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= str(shared_datadir / 'test_image_rgb.jpg'	)
	snake_case_      		= {'image': [image_path]}
	snake_case_      		= Features({'image': Image()}	)
	snake_case_      		= Dataset.from_dict(lowercase__			, features=lowercase__	)
	snake_case_      		= ParquetDatasetWriter(lowercase__			, tmp_path / 'foo.parquet'	)
	assert writer.write() > 0

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

	snake_case_      		= ParquetDatasetReader(str(tmp_path / 'foo.parquet'	)			, streaming=lowercase__	).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(lowercase__			, lowercase__	):





	'''simple docstring'''
	assert get_writer_batch_size(lowercase__	) == expected




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= ClipClient(
	    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1	)

	os.makedirs(f"""{class_data_dir}/images"""			, exist_ok=lowercase__	)
	if len(list(Path(f"""{class_data_dir}/images"""	).iterdir()	)	) >= num_class_images:
		return

	while True:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= tqdm(desc='downloading real regularization images'			, total=lowercase__	)

	with open(f"""{class_data_dir}/caption.txt"""			, 'w'	) as fa, open(f"""{class_data_dir}/urls.txt"""			, 'w'	) as fa, open(
	    f"""{class_data_dir}/images.txt"""			, 'w'	) as fa:
		while total < num_class_images:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= Image.open(BytesIO(img.content	)	)
					with open(f"""{class_data_dir}/images/{total}.jpg"""			, 'wb'	) as f:
						f.write(img.content	)
					fa.write(images['caption'] + '\n'	)
					fa.write(images['url'] + '\n'	)
					fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'	)
					total += 1
					pbar.update(1	)
				else:
					continue
			except Exception:
				continue
	return





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser(''			, add_help=lowercase__	)
	parser.add_argument('--class_prompt'			, help='text prompt to retrieve images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--class_data_dir'			, help='path to save images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--num_class_images'			, help='number of images to download'			, default=200			, type=lowercase__	)
	return parser.parse_args()


if __name__ == "__main__":
		A											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
