





from typing import List

import numpy as np


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    SCREAMING_SNAKE_CASE__											=			{key: len(lowerCAmelCase_	) for key, value in gen_kwargs.items() if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	)}
    if len(set(lists_lengths.values()	)	) > 1:
        raise RuntimeError(
            (
                '''Sharding is ambiguous for this dataset: '''
                + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                + '''\n'''.join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()	)
                + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
            )	)
    SCREAMING_SNAKE_CASE__											=			max(lists_lengths.values() ,							default=0	)
    return max(1 ,							lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[range]:
    SCREAMING_SNAKE_CASE__											=			[]
    for group_idx in range(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE__											=			shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE__											=			range(lowerCAmelCase_ ,							start + num_shards_to_add	)
        shards_indices_per_group.append(lowerCAmelCase_	)
    return shards_indices_per_group


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[dict]:
    SCREAMING_SNAKE_CASE__											=			_number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
    if num_shards == 1:
        return [dict(lowerCAmelCase_	)]
    else:
        SCREAMING_SNAKE_CASE__											=			_distribute_shards(num_shards=lowerCAmelCase_ ,							max_num_jobs=lowerCAmelCase_	)
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	)
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowerCAmelCase_	)	)
        ]


def 							__snake_case							(    lowerCAmelCase_	)				->						dict:
    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,							lowerCAmelCase_	)
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }



def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						dict:
    SCREAMING_SNAKE_CASE__											=			{len(lowerCAmelCase_	) for value in gen_kwargs.values() if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	)}
    SCREAMING_SNAKE_CASE__											=			{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE__											=			list(range(lowerCAmelCase_	)	)
        rng.shuffle(indices_per_size[size]	)
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE__											=			dict(lowerCAmelCase_	)
    for key, value in shuffled_kwargs.items():
        if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			[value[i] for i in indices_per_size[len(lowerCAmelCase_	)]]
    return shuffled_kwargs

from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()