




"""simple docstring"""


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Tuple		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  WavaVecaForSequenceClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		List[Any]								=  downstream_dict["""projector.weight"""]
  _snake_case							:		Optional[Any]								=  downstream_dict["""projector.bias"""]
  _snake_case							:		int								=  downstream_dict["""model.post_net.linear.weight"""]
  _snake_case							:		List[str]								=  downstream_dict["""model.post_net.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  WavaVecaForAudioFrameClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[int]								=  downstream_dict["""model.linear.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  WavaVecaForXVector.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[Any]								=  downstream_dict["""connector.weight"""]
  _snake_case							:		Optional[int]								=  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
    _snake_case							:		Tuple								=  downstream_dict[
        F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
    ]
    _snake_case							:		str								=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

  _snake_case							:		Any								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  _snake_case							:		Union[str, Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  _snake_case							:		List[Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  _snake_case							:		List[str]								=  downstream_dict["""objective.W"""]
  return model


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  torch.load(snake_case__		,   map_location="""cpu"""    )

  _snake_case							:		Union[str, Any]								=  checkpoint["""Downstream"""]

  _snake_case							:		Optional[Any]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		Optional[int]								=  WavaVecaFeatureExtractor.from_pretrained(
      snake_case__		,   return_attention_mask=snake_case__		,   do_normalize=snake_case__    )

  _snake_case							:		Optional[int]								=  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""    ):
    _snake_case							:		Optional[Any]								=  convert_classification(snake_case__		,   snake_case__		,   snake_case__    )
  elif arch.endswith("""ForAudioFrameClassification"""    ):
    _snake_case							:		Dict								=  convert_diarization(snake_case__		,   snake_case__		,   snake_case__    )
  elif arch.endswith("""ForXVector"""    ):
    _snake_case							:		List[str]								=  convert_xvector(snake_case__		,   snake_case__		,   snake_case__    )
  else:
    raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"    )

  if hf_config.use_weighted_layer_sum:
    _snake_case							:		Optional[int]								=  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(snake_case__    )
  hf_model.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     A_						=				parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



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""")