






"""simple docstring"""






import csv

import tweepy

# Twitter API credentials
UpperCAmelCase_   : Union[str, Any]   			= """"""
UpperCAmelCase_   : Any   			= """"""
UpperCAmelCase_   : List[Any]   			= """"""
UpperCAmelCase_   : Tuple   			= """"""



def 					_A			(__a							) ->       Tuple:
    """simple docstring"""






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

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

    # make initial request for most recent tweets (200 is the maximum allowed count)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		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
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		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
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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
        SCREAMING_SNAKE_CASE_      :  List[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
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[[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:
        SCREAMING_SNAKE_CASE_      :  str    						=		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""")




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

__lowerCamelCase							=       TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__lowerCamelCase							=       TaTokenizerFast

__lowerCamelCase							=       {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """MT5EncoderModel""",
       """MT5ForConditionalGeneration""",
       """MT5ForQuestionAnswering""",
       """MT5Model""",
       """MT5PreTrainedModel""",
       """MT5Stack""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   __lowerCamelCase							=       _LazyModule(
       __name__,
       globals()["""__file__"""],
       _import_structure,
       extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
       module_spec=__spec__,
   )





