
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  :					Optional[Any]						    =							logging.get_logger(__name__)

lowercase  :					Optional[int]						    =							{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Any=				"camembert"
    def __init__(  self		,snake_case=30522		,snake_case=768		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=2		,snake_case=0.02		,snake_case=1e-12		,snake_case=1		,snake_case=0		,snake_case=2		,snake_case="absolute"		,snake_case=True		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:     List[Any]       	=      vocab_size
           lowercase							:     Tuple       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_hidden_layers
           lowercase							:     List[str]       	=      num_attention_heads
           lowercase							:     Optional[Any]       	=      hidden_act
           lowercase							:     Tuple       	=      intermediate_size
           lowercase							:     Any       	=      hidden_dropout_prob
           lowercase							:     List[str]       	=      attention_probs_dropout_prob
           lowercase							:     Dict       	=      max_position_embeddings
           lowercase							:     Tuple       	=      type_vocab_size
           lowercase							:     Union[str, Any]       	=      initializer_range
           lowercase							:     Tuple       	=      layer_norm_eps
           lowercase							:     Dict       	=      position_embedding_type
           lowercase							:     Union[str, Any]       	=      use_cache
           lowercase							:     Optional[int]       	=      classifier_dropout






class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.task == "multiple-choice":
                  lowercase							:     Union[str, Any]       	=      {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                  lowercase							:     Union[str, Any]       	=      {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
               ]      )

import csv

import tweepy

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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							tweepy.OAuthHandler(lowercase							,			lowercase							)
				auth.set_access_token(lowercase							,			lowercase							)
				__lowercase 			=							tweepy.API(lowercase							)

				# initialize a list to hold all the tweepy Tweets
				__lowercase 			=							[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				__lowercase 			=							api.user_timeline(screen_name=lowercase							,			count=200							)

				# save most recent tweets
				alltweets.extend(lowercase							)

				# save the id of the oldest tweet less one
				__lowercase 			=							alltweets[-1].id - 1

				# keep grabbing tweets until there are no tweets left to grab
				while len(lowercase							) > 0:
								print(F"getting tweets before {oldest}"							)

								# all subsequent requests use the max_id param to prevent duplicates
								__lowercase 			=							api.user_timeline(
								    screen_name=lowercase							,			count=200							,			max_id=lowercase							)

								# save most recent tweets
								alltweets.extend(lowercase							)

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

								print(F"...{len(lowercase							)} tweets downloaded so far"							)

				# transform the tweepy tweets into a 2D array that will populate the csv
				__lowercase 			=							[[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 			=							csv.writer(lowercase							)
								writer.writerow(['''id''', '''created_at''', '''text''']							)
								writer.writerows(lowercase							)


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