

"""simple docstring"""
import csv

import tweepy

# Twitter API credentials
_A							=  """"""
_A							=  """"""
_A							=  """"""
_A							=  """"""




def 					a__						(						lowerCAmelCase    )	-> None:
      # authorize twitter, initialize tweepy
      UpperCAmelCase__			:							List[Any]							=    tweepy.OAuthHandler(lowerCAmelCase			,	lowerCAmelCase    )
      auth.set_access_token(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__			:							Dict							=    tweepy.API(lowerCAmelCase    )

      # 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__			:							Tuple							=    api.user_timeline(screen_name=lowerCAmelCase			,	count=2_00    )

      # save most recent tweets
      alltweets.extend(lowerCAmelCase    )

      # save the id of the oldest tweet less one
      UpperCAmelCase__			:							int							=    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
            UpperCAmelCase__			:							Optional[int]							=    api.user_timeline(
                screen_name=lowerCAmelCase			,	count=2_00			,	max_id=lowerCAmelCase    )

            # save most recent tweets
            alltweets.extend(lowerCAmelCase    )

            # update the id of the oldest tweet less one
            UpperCAmelCase__			:							List[str]							=    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
      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__			:							Any							=    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""")




"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

