





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]				)->		List[Any]:







		if "model" in orig_key:
				_snake_case   :				Union[str, Any]														=	orig_key.replace('model.'    ,							''				)
		if "norm1" in orig_key:
				_snake_case   :				List[str]														=	orig_key.replace('norm1'    ,							'attention.output.LayerNorm'				)
		if "norm2" in orig_key:
				_snake_case   :				Optional[Any]														=	orig_key.replace('norm2'    ,							'output.LayerNorm'				)
		if "norm" in orig_key:
				_snake_case   :				Union[str, Any]														=	orig_key.replace('norm'    ,							'LayerNorm'				)
		if "transformer" in orig_key:
				_snake_case   :				Optional[int]														=	orig_key.split('.'				)[0].split('_'				)[-1]
				_snake_case   :				Union[str, Any]														=	orig_key.replace(F"""transformer_{layer_num}"""    ,							F"""encoder.layer.{layer_num}"""				)
		if "mha.attn" in orig_key:
				_snake_case   :				Dict														=	orig_key.replace('mha.attn'    ,							'attention.self'				)
		if "mha" in orig_key:
				_snake_case   :				Union[str, Any]														=	orig_key.replace('mha'    ,							'attention'				)
		if "W_q" in orig_key:
				_snake_case   :				int														=	orig_key.replace('W_q'    ,							'self.query'				)
		if "W_k" in orig_key:
				_snake_case   :				str														=	orig_key.replace('W_k'    ,							'self.key'				)
		if "W_v" in orig_key:
				_snake_case   :				str														=	orig_key.replace('W_v'    ,							'self.value'				)
		if "ff1" in orig_key:
				_snake_case   :				Any														=	orig_key.replace('ff1'    ,							'intermediate.dense'				)
		if "ff2" in orig_key:
				_snake_case   :				str														=	orig_key.replace('ff2'    ,							'output.dense'				)
		if "ff" in orig_key:
				_snake_case   :				List[str]														=	orig_key.replace('ff'    ,							'output.dense'				)
		if "mlm_class" in orig_key:
				_snake_case   :				Dict														=	orig_key.replace('mlm.mlm_class'    ,							'cls.predictions.decoder'				)
		if "mlm" in orig_key:
				_snake_case   :				Tuple														=	orig_key.replace('mlm'    ,							'cls.predictions.transform'				)
		if "cls" not in orig_key:
				_snake_case   :				List[str]														=	'yoso.' + orig_key

		return orig_key


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Any				)->		List[Any]:







		for key in orig_state_dict.copy().keys():
				_snake_case   :				Any														=	orig_state_dict.pop(__snake_case				)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						_snake_case   :				str														=	val

		_snake_case   :				List[Any]														=	orig_state_dict['cls.predictions.decoder.bias']
		_snake_case   :				Dict														=	torch.arange(__snake_case				).expand((1, -1)				) + 2

		return orig_state_dict


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[Any]				)->		int:
		_snake_case   :				Union[str, Any]														=	torch.load(__snake_case    ,							map_location='cpu'				)['model_state_dict']
		_snake_case   :				Tuple														=	YosoConfig.from_json_file(__snake_case				)
		_snake_case   :				Optional[Any]														=	YosoForMaskedLM(__snake_case				)

		_snake_case   :				Optional[Any]														=	convert_checkpoint_helper(config.max_position_embeddings    ,							__snake_case				)

		print(model.load_state_dict(__snake_case				)				)
		model.eval()
		model.save_pretrained(__snake_case				)

		print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""				)


if __name__ == "__main__":
						lowerCAmelCase_						   =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help="""The json file for YOSO model config.""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						lowerCAmelCase_						   =    parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











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""")




