import csv

import tweepy

# Twitter API credentials
__snake_case						=			''''''
__snake_case						=			''''''
__snake_case						=			''''''
__snake_case						=			''''''





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     None:






      '''simple docstring'''





      UpperCAmelCase			:       Optional[Any]			     =tweepy.OAuthHandler(_UpperCamelCase						,    _UpperCamelCase  )
      auth.set_access_token(_UpperCamelCase						,    _UpperCamelCase  )
      UpperCAmelCase			:       Optional[int]			     =tweepy.API(_UpperCamelCase  )

      # initialize a list to hold all the tweepy Tweets
      UpperCAmelCase			:       Union[str, Any]			     =[]

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

      # save most recent tweets
      alltweets.extend(_UpperCamelCase  )

      # save the id of the oldest tweet less one
      UpperCAmelCase			:       str			     =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
            UpperCAmelCase			:       Dict			     =api.user_timeline(
                screen_name=_UpperCamelCase						,    count=2_00						,    max_id=_UpperCamelCase  )

            # save most recent tweets
            alltweets.extend(_UpperCamelCase  )

            # update the id of the oldest tweet less one
            UpperCAmelCase			:       Optional[Any]			     =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
      UpperCAmelCase			:       Tuple			     =[[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:
            UpperCAmelCase			:       Union[str, Any]			     =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''')







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
