






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'''







					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_A								= 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'''







					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A								= features.copy() if features else default_expected_features
					_A								= (
					    Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
					)
					_A								= 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'''







					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A								= 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 ):
										_A								= parquet_path
					elif issubclass(_lowercase					,			_lowercase ):
										_A								= [parquet_path]
					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A								= 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:
										_A								= 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'''







					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_A								= 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'''







					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A								= features.copy() if features else default_expected_features
					_A								= (
					    Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
					)
					_A								= 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:
										_A								= {split: parquet_path}
					else:
										_A								= '''train'''
										_A								= {'''train''': parquet_path, '''test''': parquet_path}
					_A								= tmp_path / '''cache'''
					_A								= {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A								= 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'''







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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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

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

					_A								= 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











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).text					,			'''html.parser''' )
					_A								= soup.find_all('''td'''					,			attrs='''titleColumn''' )
					_A								= soup.find_all('''td'''					,			class_='''ratingColumn imdbRating''' )
					return {
					    title.a.text: float(rating.strong.text )
					    for title, rating in zip(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



