





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup
def 							a_	(    __magic_name__ = ""   )				->			dict[str, float]:




				"""simple docstring"""






				snake_case   :					Optional[Any]    						=				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
				snake_case   :					Any    						=				BeautifulSoup(requests.get(__a   ).text		,     '''html.parser'''   )
				snake_case   :					List[Any]    						=				soup.find_all('''td'''		,     attrs='''titleColumn'''   )
				snake_case   :					int    						=				soup.find_all('''td'''		,     class_='''ratingColumn imdbRating'''   )
				return {
				    title.a.text: float(rating.strong.text   )
				    for title, rating in zip(__a		,     __a   )
				}
def 							a_	(    __magic_name__ = "IMDb_Top_250_Movies.csv"   )				->			None:




				"""simple docstring"""






				snake_case   :					Union[str, Any]    						=				get_imdb_top_aaa_movies()
				with open(__a		,     '''w'''		,     newline=''''''   ) as out_file:
								snake_case   :					Tuple    						=				csv.writer(__a   )
								writer.writerow(['''Movie title''', '''IMDb rating''']   )
								for title, rating in movies.items():
												writer.writerow([title, rating]   )


if __name__ == "__main__":
			write_movies()




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__snake_case				 =	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoForCausalLM''',
		    '''GPTNeoForQuestionAnswering''',
		    '''GPTNeoForSequenceClassification''',
		    '''GPTNeoForTokenClassification''',
		    '''GPTNeoModel''',
		    '''GPTNeoPreTrainedModel''',
		    '''load_tf_weights_in_gpt_neo''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxGPTNeoForCausalLM''',
		    '''FlaxGPTNeoModel''',
		    '''FlaxGPTNeoPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neo import (
				    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoForCausalLM,
				    GPTNeoForQuestionAnswering,
				    GPTNeoForSequenceClassification,
				    GPTNeoForTokenClassification,
				    GPTNeoModel,
				    GPTNeoPreTrainedModel,
				    load_tf_weights_in_gpt_neo,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)