






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()








import os
from pathlib import Path

def    UpperCamelCase						(	):






						"""simple docstring"""







						from torch.utils.cpp_extension import load

						__magic_name__      :       Dict       						= Path(_A			).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
						__magic_name__      :       Optional[int]       						= [
						    root / filename
						    for filename in [
						        """vision.cpp""",
						        os.path.join("""cpu""",   """ms_deform_attn_cpu.cpp"""			),
						        os.path.join("""cuda""",   """ms_deform_attn_cuda.cu"""			),
						    ]
						]

						load(
						    """MultiScaleDeformableAttention""",   _A,   with_cuda=_A,   extra_include_paths=[str(_A			)],   extra_cflags=["""-DWITH_CUDA=1"""],   extra_cuda_cflags=[
						        """-DCUDA_HAS_FP16=1""",
						        """-D__CUDA_NO_HALF_OPERATORS__""",
						        """-D__CUDA_NO_HALF_CONVERSIONS__""",
						        """-D__CUDA_NO_HALF2_OPERATORS__""",
						    ],   )

						import MultiScaleDeformableAttention as MSDA

						return MSDA
