





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase_						   =    {
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =    [
						    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLMForMultipleChoice""",
						    """XLMForQuestionAnswering""",
						    """XLMForQuestionAnsweringSimple""",
						    """XLMForSequenceClassification""",
						    """XLMForTokenClassification""",
						    """XLMModel""",
						    """XLMPreTrainedModel""",
						    """XLMWithLMHeadModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =    [
						    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLMForMultipleChoice""",
						    """TFXLMForQuestionAnsweringSimple""",
						    """TFXLMForSequenceClassification""",
						    """TFXLMForTokenClassification""",
						    """TFXLMMainLayer""",
						    """TFXLMModel""",
						    """TFXLMPreTrainedModel""",
						    """TFXLMWithLMHeadModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
						from .tokenization_xlm import XLMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm import (
												    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMForMultipleChoice,
												    XLMForQuestionAnswering,
												    XLMForQuestionAnsweringSimple,
												    XLMForSequenceClassification,
												    XLMForTokenClassification,
												    XLMModel,
												    XLMPreTrainedModel,
												    XLMWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm import (
												    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMForMultipleChoice,
												    TFXLMForQuestionAnsweringSimple,
												    TFXLMForSequenceClassification,
												    TFXLMForTokenClassification,
												    TFXLMMainLayer,
												    TFXLMModel,
												    TFXLMPreTrainedModel,
												    TFXLMWithLMHeadModel,
												)

else:
						import sys

						lowerCAmelCase_						   =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











import csv

import tweepy

# Twitter API credentials
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		None:
  # authorize twitter, initialize tweepy
  _snake_case   :				Optional[Any]														=	tweepy.OAuthHandler(lowerCAmelCase    ,							lowerCAmelCase				)
  auth.set_access_token(lowerCAmelCase    ,							lowerCAmelCase				)
  _snake_case   :				List[Any]														=	tweepy.API(lowerCAmelCase				)

  # initialize a list to hold all the tweepy Tweets
  _snake_case   :				Any														=	[]

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




