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


__lowerCAmelCase					:			str			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			Optional[int]			      =		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class 						__lowerCAmelCase	(	_UpperCAmelCase      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    """trocr"""
      A__	:			Union[str, Any]        =    ["""past_key_values"""]
      A__	:			str        =    {
          """num_attention_heads""": """decoder_attention_heads""",
          """hidden_size""": """d_model""",
          """num_hidden_layers""": """decoder_layers""",
      }




      def __init__(      self		:       Dict				,       _snake_case		:       Optional[Any]=5_0265				,       _snake_case		:       Dict=1024				,       _snake_case		:       List[str]=12				,       _snake_case		:       Dict=16				,       _snake_case		:       Optional[Any]=4096				,       _snake_case		:       Tuple="gelu"				,       _snake_case		:       Optional[Any]=512				,       _snake_case		:       Tuple=0.1				,       _snake_case		:       Tuple=0.0				,       _snake_case		:       Optional[int]=0.0				,       _snake_case		:       List[str]=2				,       _snake_case		:       Any=0.02				,       _snake_case		:       Dict=0.0				,       _snake_case		:       Any=True				,       _snake_case		:       Any=False				,       _snake_case		:       str=True				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       List[Any]=1				,       _snake_case		:       Tuple=0				,       _snake_case		:       Any=2				,       **_snake_case		:       int				,       ):
          __lowercase		:			Union[str, Any]	    =       vocab_size
          __lowercase		:			int	    =       d_model
          __lowercase		:			Any	    =       decoder_layers
          __lowercase		:			str	    =       decoder_attention_heads
          __lowercase		:			Optional[Any]	    =       decoder_ffn_dim
          __lowercase		:			str	    =       activation_function
          __lowercase		:			Dict	    =       max_position_embeddings
          __lowercase		:			Dict	    =       dropout
          __lowercase		:			Optional[int]	    =       attention_dropout
          __lowercase		:			Dict	    =       activation_dropout
          __lowercase		:			Tuple	    =       init_std
          __lowercase		:			Optional[int]	    =       decoder_layerdrop
          __lowercase		:			Optional[int]	    =       use_cache
          __lowercase		:			Union[str, Any]	    =       scale_embedding
          __lowercase		:			Optional[Any]	    =       use_learned_position_embeddings
          __lowercase		:			Dict	    =       layernorm_embedding

          super().__init__(
              pad_token_id=lowercase__				,       bos_token_id=lowercase__				,       eos_token_id=lowercase__				,       decoder_start_token_id=lowercase__				,       **lowercase__				,       )




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")


