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


__lowerCAmelCase					:			Optional[Any]			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			Dict			      =		{
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Any        =    '''transfo-xl'''
      A__	:			List[str]        =    ['''mems''']
      A__	:			Optional[int]        =    {
          '''n_token''': '''vocab_size''',
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(      self		:       Union[str, Any]				,       _snake_case		:       List[str]=26_7735				,       _snake_case		:       str=[2_0000, 4_0000, 20_0000]				,       _snake_case		:       List[str]=1024				,       _snake_case		:       str=1024				,       _snake_case		:       Optional[int]=16				,       _snake_case		:       Union[str, Any]=64				,       _snake_case		:       Tuple=4096				,       _snake_case		:       Tuple=4				,       _snake_case		:       Union[str, Any]=False				,       _snake_case		:       List[Any]=18				,       _snake_case		:       int=1600				,       _snake_case		:       List[str]=1000				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       List[Any]=True				,       _snake_case		:       Dict=0				,       _snake_case		:       Any=-1				,       _snake_case		:       Optional[int]=True				,       _snake_case		:       Dict=0.1				,       _snake_case		:       Any=0.0				,       _snake_case		:       Tuple=True				,       _snake_case		:       List[Any]="normal"				,       _snake_case		:       Optional[int]=0.01				,       _snake_case		:       str=0.01				,       _snake_case		:       Optional[Any]=0.02				,       _snake_case		:       Optional[Any]=1E-5				,       _snake_case		:       Dict=0				,       **_snake_case		:       Optional[Any]				,       ):
          __lowercase		:			Optional[int]	    =       vocab_size
          __lowercase		:			int	    =       []
          self.cutoffs.extend(_snake_case				)
          if proj_share_all_but_first:
              __lowercase		:			Dict	    =       [False] + [True] * len(self.cutoffs				)
          else:
              __lowercase		:			Tuple	    =       [False] + [False] * len(self.cutoffs				)
          __lowercase		:			int	    =       d_model
          __lowercase		:			Optional[int]	    =       d_embed
          __lowercase		:			Dict	    =       d_head
          __lowercase		:			List[Any]	    =       d_inner
          __lowercase		:			Tuple	    =       div_val
          __lowercase		:			Optional[Any]	    =       pre_lnorm
          __lowercase		:			List[Any]	    =       n_layer
          __lowercase		:			int	    =       n_head
          __lowercase		:			Any	    =       mem_len
          __lowercase		:			Any	    =       same_length
          __lowercase		:			Dict	    =       attn_type
          __lowercase		:			Dict	    =       clamp_len
          __lowercase		:			Tuple	    =       sample_softmax
          __lowercase		:			List[str]	    =       adaptive
          __lowercase		:			Any	    =       dropout
          __lowercase		:			Dict	    =       dropatt
          __lowercase		:			Optional[int]	    =       untie_r
          __lowercase		:			Dict	    =       init
          __lowercase		:			Optional[int]	    =       init_range
          __lowercase		:			Optional[int]	    =       proj_init_std
          __lowercase		:			Tuple	    =       init_std
          __lowercase		:			Optional[int]	    =       layer_norm_epsilon
          super().__init__(eos_token_id=_snake_case				,       **_snake_case				)




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          # Message copied from Transformer-XL documentation
          logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.'				)
          return -1




      @max_position_embeddings.setter
      def 		snake_case_			(      self		:       str				,       _snake_case		:       Optional[int]				):
          # Message copied from Transformer-XL documentation
          raise NotImplementedError(
              F'The model {self.model_type} is one of the few models that has no sequence length limit.'				)




import csv

import tweepy

# Twitter API credentials
__lowerCAmelCase					:			str			      =		""
__lowerCAmelCase					:			Any			      =		""
__lowerCAmelCase					:			Any			      =		""
__lowerCAmelCase					:			Optional[int]			      =		""


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						None:
    # authorize twitter, initialize tweepy
    __lowercase		:			List[Any]	    =       tweepy.OAuthHandler(__lowerCAmelCase	,					__lowerCAmelCase	)
    auth.set_access_token(__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			str	    =       tweepy.API(__lowerCAmelCase	)

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

    # make initial request for most recent tweets (200 is the maximum allowed count)
    __lowercase		:			List[Any]	    =       api.user_timeline(screen_name=__lowerCAmelCase	,					count=200	)

    # save most recent tweets
    alltweets.extend(__lowerCAmelCase	)

    # save the id of the oldest tweet less one
    __lowercase		:			Any	    =       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
        __lowercase		:			Tuple	    =       api.user_timeline(
            screen_name=__lowerCAmelCase	,					count=200	,					max_id=__lowerCAmelCase	)

        # save most recent tweets
        alltweets.extend(__lowerCAmelCase	)

        # update the id of the oldest tweet less one
        __lowercase		:			Any	    =       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
    __lowercase		:			Optional[int]	    =       [[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:
        __lowercase		:			int	    =       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")


