





import csv

import tweepy

# Twitter API credentials
_lowerCamelCase :    Dict  							= ''''''
_lowerCamelCase :    Optional[Any]  							= ''''''
_lowerCamelCase :    List[Any]  							= ''''''
_lowerCamelCase :    Optional[Any]  							= ''''''




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						tweepy.OAuthHandler(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							auth.set_access_token(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tweepy.API(SCREAMING_SNAKE_CASE__       )

							# initialize a list to hold all the tweepy Tweets
							SCREAMING_SNAKE_CASE__       :      Dict     =						[]

							# make initial request for most recent tweets (200 is the maximum allowed count)
							SCREAMING_SNAKE_CASE__       :      List[str]     =						api.user_timeline(screen_name=SCREAMING_SNAKE_CASE__			,				count=2_00       )

							# save most recent tweets
							alltweets.extend(SCREAMING_SNAKE_CASE__       )

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

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

														# all subsequent requests use the max_id param to prevent duplicates
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						api.user_timeline(
														    screen_name=SCREAMING_SNAKE_CASE__			,				count=2_00			,				max_id=SCREAMING_SNAKE_CASE__       )

														# save most recent tweets
														alltweets.extend(SCREAMING_SNAKE_CASE__       )

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

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

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


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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase :    Union[str, Any]  							= {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    int  							= [
		    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BigBirdPegasusForCausalLM''',
		    '''BigBirdPegasusForConditionalGeneration''',
		    '''BigBirdPegasusForQuestionAnswering''',
		    '''BigBirdPegasusForSequenceClassification''',
		    '''BigBirdPegasusModel''',
		    '''BigBirdPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bigbird_pegasus import (
		    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BigBirdPegasusConfig,
		    BigBirdPegasusOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdPegasusForCausalLM,
				    BigBirdPegasusForConditionalGeneration,
				    BigBirdPegasusForQuestionAnswering,
				    BigBirdPegasusForSequenceClassification,
				    BigBirdPegasusModel,
				    BigBirdPegasusPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






