



import csv

import tweepy

# Twitter API credentials
UpperCAmelCase_									=							""
UpperCAmelCase_									=							""
UpperCAmelCase_									=							""
UpperCAmelCase_									=							""
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str				)->None:
      # authorize twitter, initialize tweepy
      _lowerCAmelCase 			=  tweepy.OAuthHandler(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      auth.set_access_token(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  tweepy.API(_SCREAMING_SNAKE_CASE				)

      # 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=_SCREAMING_SNAKE_CASE     ,				count=2_0_0				)

      # save most recent tweets
      alltweets.extend(_SCREAMING_SNAKE_CASE				)

      # 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(_SCREAMING_SNAKE_CASE				) > 0:
            print(f'''getting tweets before {oldest}'''				)

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

            # save most recent tweets
            alltweets.extend(_SCREAMING_SNAKE_CASE				)

            # update the id of the oldest tweet less one
            _lowerCAmelCase 			=  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
      _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(_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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)