

'''simple docstring'''



import csv

import tweepy

# Twitter API credentials
_UpperCamelCase						:  Union[str, Any]							       =				""
_UpperCamelCase						:  Optional[Any]							       =				""
_UpperCamelCase						:  Tuple							       =				""
_UpperCamelCase						:  Tuple							       =				""


def       snake_case   (	snake_case      : str			) ->  None:


    """simple docstring"""


    lowerCAmelCase 						=							tweepy.OAuthHandler(snake_case					,		snake_case			)
    auth.set_access_token(snake_case					,		snake_case			)
    lowerCAmelCase 						=							tweepy.API(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=snake_case					,		count=200			)

    # save most recent tweets
    alltweets.extend(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(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=snake_case					,		count=200					,		max_id=snake_case			)

        # save most recent tweets
        alltweets.extend(snake_case			)

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

        print(F'...{len(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(snake_case			)
        writer.writerow(['id', 'created_at', 'text']			)
        writer.writerows(snake_case			)


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








'''simple docstring'''



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


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      , bos_token_id=_SCREAMING_SNAKE_CASE      , eos_token_id=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





