

'''simple docstring'''

import csv

import tweepy

# Twitter API credentials
lowercase__         =      ""
lowercase__         =      ""
lowercase__         =      ""
lowercase__         =      ""




def 				UpperCamelCase(					UpperCAmelCase_     ):
 # authorize twitter, initialize tweepy
 UpperCAmelCase	:     Optional[int]						 =			tweepy.OAuthHandler(UpperCAmelCase_  ,	UpperCAmelCase_     )
 auth.set_access_token(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			tweepy.API(UpperCAmelCase_     )

 # initialize a list to hold all the tweepy Tweets
 UpperCAmelCase	:     str						 =			[]

 # make initial request for most recent tweets (200 is the maximum allowed count)
 UpperCAmelCase	:     str						 =			api.user_timeline(screen_name=UpperCAmelCase_  ,	count=2_00     )

 # save most recent tweets
 alltweets.extend(UpperCAmelCase_     )

 # save the id of the oldest tweet less one
 UpperCAmelCase	:     Optional[int]						 =			alltweets[-1].id - 1

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

  # all subsequent requests use the max_id param to prevent duplicates
  UpperCAmelCase	:     Union[str, Any]						 =			api.user_timeline(
      screen_name=UpperCAmelCase_  ,	count=2_00  ,	max_id=UpperCAmelCase_     )

  # save most recent tweets
  alltweets.extend(UpperCAmelCase_     )

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

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

 # transform the tweepy tweets into a 2D array that will populate the csv
 UpperCAmelCase	:     Union[str, 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:
  UpperCAmelCase	:     Dict						 =			csv.writer(UpperCAmelCase_     )
  writer.writerow(['id', 'created_at', 'text']     )
  writer.writerows(UpperCAmelCase_     )


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







'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates     ) * len(config.resblock_kernel_sizes     )     ):
  for j in range(len(config.resblock_dilation_sizes     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

 if repo_id:
  print('Pushing to the hub...'     )
  model.push_to_hub(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
 parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
 )

 lowercase__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )




