

'''simple docstring'''





from typing import TYPE_CHECKING

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


A_	= {
    "configuration_groupvit": [
        "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "GroupViTConfig",
        "GroupViTOnnxConfig",
        "GroupViTTextConfig",
        "GroupViTVisionConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= [
				    "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GroupViTModel",
				    "GroupViTPreTrainedModel",
				    "GroupViTTextModel",
				    "GroupViTVisionModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= [
				    "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFGroupViTModel",
				    "TFGroupViTPreTrainedModel",
				    "TFGroupViTTextModel",
				    "TFGroupViTVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_groupvit import (
				    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    GroupViTConfig,
				    GroupViTOnnxConfig,
				    GroupViTTextConfig,
				    GroupViTVisionConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_groupvit import (
								    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GroupViTModel,
								    GroupViTPreTrainedModel,
								    GroupViTTextModel,
								    GroupViTVisionModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_groupvit import (
								    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFGroupViTModel,
								    TFGroupViTPreTrainedModel,
								    TFGroupViTTextModel,
								    TFGroupViTVisionModel,
								)

else:
				import sys

				A_	= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).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(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




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


if __name__ == "__main__":
					write_movies()



