






import csv

import tweepy

# Twitter API credentials
__magic_name__:					Optional[Any]							=     ""
__magic_name__:					List[Any]							=     ""
__magic_name__:					List[Any]							=     ""
__magic_name__:					List[Any]							=     ""

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= tweepy.OAuthHandler(_A,   _A			)
						auth.set_access_token(_A,   _A			)
						__magic_name__      :       Optional[int]       						= tweepy.API(_A			)

						# initialize a list to hold all the tweepy Tweets
						__magic_name__      :       List[str]       						= []

						# make initial request for most recent tweets (200 is the maximum allowed count)
						__magic_name__      :       List[Any]       						= api.user_timeline(screen_name=_A,   count=200			)

						# save most recent tweets
						alltweets.extend(_A			)

						# save the id of the oldest tweet less one
						__magic_name__      :       List[str]       						= alltweets[-1].id - 1

						# keep grabbing tweets until there are no tweets left to grab
						while len(_A			) > 0:
												print(f'getting tweets before {oldest}'			)

												# all subsequent requests use the max_id param to prevent duplicates
												__magic_name__      :       Optional[int]       						= api.user_timeline(
												    screen_name=_A,   count=200,   max_id=_A			)

												# save most recent tweets
												alltweets.extend(_A			)

												# update the id of the oldest tweet less one
												__magic_name__      :       Dict       						= alltweets[-1].id - 1

												print(f'...{len(_A			)} tweets downloaded so far'			)

						# transform the tweepy tweets into a 2D array that will populate the csv
						__magic_name__      :       Any       						= [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

						# write the csv
						with open(f'new_{screen_name}_tweets.csv',   """w"""			) as f:
												__magic_name__      :       str       						= csv.writer(_A			)
												writer.writerow(["""id""", """created_at""", """text"""]			)
												writer.writerows(_A			)


if __name__ == "__main__":
		# pass in the username of the account you want to download
		get_all_tweets("FirePing32")








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
