
import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 				__magic_name__				(     __lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     Optional[Any]:
				assert isinstance(__lowerCAmelCase	,						__lowerCAmelCase							)
				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 				__magic_name__				(     __lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		Optional[int]							)    ->     Optional[int]:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCamelCase					      =			CsvDatasetReader(__lowerCAmelCase	,						cache_dir=__lowerCAmelCase	,						keep_in_memory=__lowerCAmelCase							).read()
				_check_csv_dataset(__lowerCAmelCase	,						__lowerCAmelCase							)




@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 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		int							)    ->     int:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__lowerCamelCase					      =			features.copy() if features else default_expected_features
				__lowerCamelCase					      =			(
				    Features({feature: Value(__lowerCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
				)
				__lowerCamelCase					      =			CsvDatasetReader(__lowerCAmelCase	,						features=__lowerCAmelCase	,						cache_dir=__lowerCAmelCase							).read()
				_check_csv_dataset(__lowerCAmelCase	,						__lowerCAmelCase							)




@pytest.mark.parametrize('''split'''	,						[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Tuple							)    ->     Any:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__lowerCamelCase					      =			CsvDatasetReader(__lowerCAmelCase	,						cache_dir=__lowerCAmelCase	,						split=__lowerCAmelCase							).read()
				_check_csv_dataset(__lowerCAmelCase	,						__lowerCAmelCase							)
				assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''	,						[str, list]							)
def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		int							)    ->     Tuple:
				if issubclass(__lowerCAmelCase	,						__lowerCAmelCase							):
								__lowerCamelCase					      =			csv_path
				elif issubclass(__lowerCAmelCase	,						__lowerCAmelCase							):
								__lowerCamelCase					      =			[csv_path]
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__lowerCamelCase					      =			CsvDatasetReader(__lowerCAmelCase	,						cache_dir=__lowerCAmelCase							).read()
				_check_csv_dataset(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		str=("train",)							)    ->     Optional[int]:
				assert isinstance(__lowerCAmelCase	,						__lowerCAmelCase							)
				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 				__magic_name__				(     __lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Optional[Any]							)    ->     str:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCamelCase					      =			CsvDatasetReader({'''train''': csv_path}	,						cache_dir=__lowerCAmelCase	,						keep_in_memory=__lowerCAmelCase							).read()
				_check_csv_datasetdict(__lowerCAmelCase	,						__lowerCAmelCase							)




@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 				__magic_name__				(     __lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		Dict							)    ->     List[str]:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__lowerCamelCase					      =			features.copy() if features else default_expected_features
				__lowerCamelCase					      =			(
				    Features({feature: Value(__lowerCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
				)
				__lowerCamelCase					      =			CsvDatasetReader({'''train''': csv_path}	,						features=__lowerCAmelCase	,						cache_dir=__lowerCAmelCase							).read()
				_check_csv_datasetdict(__lowerCAmelCase	,						__lowerCAmelCase							)




@pytest.mark.parametrize('''split'''	,						[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		str							)    ->     int:
				if split:
								__lowerCamelCase					      =			{split: csv_path}
				else:
								__lowerCamelCase					      =			{'''train''': csv_path, '''test''': csv_path}
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__lowerCamelCase					      =			CsvDatasetReader(__lowerCAmelCase	,						cache_dir=__lowerCAmelCase							).read()
				_check_csv_datasetdict(__lowerCAmelCase	,						__lowerCAmelCase	,						splits=list(path.keys()							)							)
				assert all(dataset[split].split == split for split in path.keys()							)




def 				__magic_name__				(     __lowerCAmelCase			:		str							)    ->     Optional[int]:
				with open(__lowerCAmelCase	,						encoding='''utf-8'''							) as csvfile:
								yield from csv.reader(__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		Optional[Any]							)    ->     Any:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			os.path.join(__lowerCAmelCase	,						'''tmp.csv'''							)
				__lowerCamelCase					      =			CsvDatasetReader({'''train''': csv_path}	,						cache_dir=__lowerCAmelCase							).read()
				CsvDatasetWriter(dataset['''train''']	,						__lowerCAmelCase	,						num_proc=1							).write()

				__lowerCamelCase					      =			iter_csv_file(__lowerCAmelCase							)
				__lowerCamelCase					      =			iter_csv_file(__lowerCAmelCase							)

				for rowa, rowa in zip(__lowerCAmelCase	,						__lowerCAmelCase							):
								assert rowa == rowa




def 				__magic_name__				(     __lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		Tuple							)    ->     Any:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			os.path.join(__lowerCAmelCase	,						'''tmp.csv'''							)
				__lowerCamelCase					      =			CsvDatasetReader({'''train''': csv_path}	,						cache_dir=__lowerCAmelCase							).read()
				CsvDatasetWriter(dataset['''train''']	,						__lowerCAmelCase	,						num_proc=2							).write()

				__lowerCamelCase					      =			iter_csv_file(__lowerCAmelCase							)
				__lowerCamelCase					      =			iter_csv_file(__lowerCAmelCase							)

				for rowa, rowa in zip(__lowerCAmelCase	,						__lowerCAmelCase							):
								assert rowa == rowa







def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		List[str]							)    ->     str:
				__lowerCamelCase					      =			tmp_path / '''cache'''
				__lowerCamelCase					      =			os.path.join(__lowerCAmelCase	,						'''tmp.csv'''							)
				__lowerCamelCase					      =			CsvDatasetReader({'''train''': csv_path}	,						cache_dir=__lowerCAmelCase							).read()
				with pytest.raises(__lowerCAmelCase							):
								CsvDatasetWriter(dataset['''train''']	,						__lowerCAmelCase	,						num_proc=0							)





import bsa
import requests




def 				__magic_name__				(     __lowerCAmelCase			:		bsa.element.ResultSet							)    ->     dict[str, str]:
				return {
				    "name": soup.ha.a.text,
				    "genre": soup.find('''span'''	,						class_='''genre'''							).text.strip(),
				    "rating": soup.strong.text,
				    "page_link": f'''https://www.imdb.com{soup.a.get('href'							)}''',
				}




def 				__magic_name__				(     __lowerCAmelCase			:		int = 5							)    ->     tuple:
				__lowerCamelCase					      =			int(float(__lowerCAmelCase							)							)
				if num_movies < 1:
								return ()
				__lowerCamelCase					      =			(
				    '''https://www.imdb.com/search/title?title_type='''
				    f'''feature&sort=num_votes,desc&count={num_movies}'''
				)
				__lowerCamelCase					      =			bsa.BeautifulSoup(requests.get(__lowerCAmelCase							).content	,						'''html.parser'''							)
				return tuple(
				    get_movie_data_from_soup(__lowerCAmelCase							)
				    for movie in source.find_all('''div'''	,						class_='''lister-item mode-advanced'''							)							)


if __name__ == "__main__":
					import json

					SCREAMING_SNAKE_CASE__     :    str      							=					int(input("How many movies would you like to see? "))
					print(
					    ", ".join(
					        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
					    )
					)



