






from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup

def    UpperCamelCase						(	_A = ""			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A = "IMDb_Top_250_Movies.csv"			):






						"""simple docstring"""







						__magic_name__      :       Union[str, Any]       						= get_imdb_top_aaa_movies()
						with open(_A,   """w""",   newline=""""""			) as out_file:
												__magic_name__      :       Optional[Any]       						= csv.writer(_A			)
												writer.writerow(["""Movie title""", """IMDb rating"""]			)
												for title, rating in movies.items():
																		writer.writerow([title, rating]			)


if __name__ == "__main__":
		write_movies()








from typing import TYPE_CHECKING

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


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
