"""simple docstring"""






import csv

import tweepy

# Twitter API credentials
_A   					=    ""
_A   					=    ""
_A   					=    ""
_A   					=    ""




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Tuple:
				# authorize twitter, initialize tweepy
				SCREAMING_SNAKE_CASE__		  =				tweepy.OAuthHandler(__UpperCAmelCase  ,					__UpperCAmelCase       )
				auth.set_access_token(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				tweepy.API(__UpperCAmelCase       )

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

				# make initial request for most recent tweets (200 is the maximum allowed count)
				SCREAMING_SNAKE_CASE__		  =				api.user_timeline(screen_name=__UpperCAmelCase  ,					count=200       )

				# save most recent tweets
				alltweets.extend(__UpperCAmelCase       )

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

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

								# all subsequent requests use the max_id param to prevent duplicates
								SCREAMING_SNAKE_CASE__		  =				api.user_timeline(
								    screen_name=__UpperCAmelCase  ,					count=200  ,					max_id=__UpperCAmelCase       )

								# save most recent tweets
								alltweets.extend(__UpperCAmelCase       )

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

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

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


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



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




