




import csv

import tweepy

# Twitter API credentials
UpperCamelCase__    			=			""""""
UpperCamelCase__    			=			""""""
UpperCamelCase__    			=			""""""
UpperCamelCase__    			=			""""""







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str     ):
					# authorize twitter, initialize tweepy
					__lowerCAmelCase				   =       tweepy.OAuthHandler(lowerCAmelCase__      ,      lowerCAmelCase__     )
					auth.set_access_token(lowerCAmelCase__      ,      lowerCAmelCase__     )
					__lowerCAmelCase				   =       tweepy.API(lowerCAmelCase__     )

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

					# make initial request for most recent tweets (200 is the maximum allowed count)
					__lowerCAmelCase				   =       api.user_timeline(screen_name=lowerCAmelCase__      ,      count=2_00     )

					# save most recent tweets
					alltweets.extend(lowerCAmelCase__     )

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

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

										# all subsequent requests use the max_id param to prevent duplicates
										__lowerCAmelCase				   =       api.user_timeline(
										    screen_name=lowerCAmelCase__      ,      count=2_00      ,      max_id=lowerCAmelCase__     )

										# save most recent tweets
										alltweets.extend(lowerCAmelCase__     )

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

										print(F"""...{len(lowerCAmelCase__     )} tweets downloaded so far"""     )

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


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








'''simple docstring'''
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
  # setup instructions, if using on-demand hardware

  # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
  # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
  # Throw an error if user passes both BYO and on-demand cluster args
  # Otherwise, use default values
  UpperCamelCase__    					=      argparse.ArgumentParser()
  parser.add_argument('''--user''', type=str, default='''ubuntu''')
  parser.add_argument('''--host''', type=str, default='''localhost''')
  parser.add_argument('''--key_path''', type=str, default=None)
  parser.add_argument('''--instance''', type=str, default='''V100:1''')
  parser.add_argument('''--provider''', type=str, default='''cheapest''')
  parser.add_argument('''--use_spot''', type=bool, default=False)
  parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
  UpperCamelCase__					,   UpperCamelCase__    					=      parser.parse_known_args()
  if args.host != "localhost":
    if args.instance != "V100:1" or args.provider != "cheapest":
      raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
    )
  else:
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  UpperCamelCase__    					=      args.example.rsplit('''/''', 1)[0]

  # Set up remote environment
  cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
  # Note transformers is copied into the home directory on the remote machine, so we can install from there
  cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
  cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

  # Run example. You can bypass the CLI wrapper and paste your own code here.
  cluster.run([F"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

  # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
  # from my_script... import train
  # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
  # launch_train_gpu = rh.function(fn=train,
  #                                system=gpu,
  #                                reqs=reqs,
  #                                name='train_bert_glue')
  #
  # We can pass in arguments just like we would to a function:
  # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
  #                  stream_logs=True)
