






import csv

import tweepy

# Twitter API credentials
__a      :       str				  =   ""
__a      :       Tuple				  =   ""
__a      :       List[str]				  =   ""
__a      :       Tuple				  =   ""



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str    )						->      None:





    """simple docstring"""

    __A         =	tweepy.OAuthHandler(__lowercase			,				__lowercase    )
    auth.set_access_token(__lowercase			,				__lowercase    )
    __A         =	tweepy.API(__lowercase    )

    # initialize a list to hold all the tweepy Tweets
    __A         =	[]

    # make initial request for most recent tweets (200 is the maximum allowed count)
    __A         =	api.user_timeline(screen_name=__lowercase			,				count=2_0_0    )

    # save most recent tweets
    alltweets.extend(__lowercase    )

    # save the id of the oldest tweet less one
    __A         =	alltweets[-1].id - 1

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

        # all subsequent requests use the max_id param to prevent duplicates
        __A         =	api.user_timeline(
            screen_name=__lowercase			,				count=2_0_0			,				max_id=__lowercase    )

        # save most recent tweets
        alltweets.extend(__lowercase    )

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

        print(f"...{len(__lowercase    )} tweets downloaded so far"    )

    # transform the tweepy tweets into a 2D array that will populate the csv
    __A         =	[[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:
        __A         =	csv.writer(__lowercase    )
        writer.writerow(["""id""", """created_at""", """text"""]    )
        writer.writerows(__lowercase    )


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












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




