

'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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





def       A_     (       snake_case					,				snake_case					,				snake_case       ):
  hf_model.apply_weight_norm()

  SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint['''input_conv.weight_g''']
  SCREAMING_SNAKE_CASE:Any  					=    checkpoint['''input_conv.weight_v''']
  SCREAMING_SNAKE_CASE:Dict  					=    checkpoint['''input_conv.bias''']

  for i in range(len(config.upsample_rates       )       ):
    SCREAMING_SNAKE_CASE:Optional[Any]  					=    checkpoint[F'''upsamples.{i}.1.weight_g''']
    SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint[F'''upsamples.{i}.1.weight_v''']
    SCREAMING_SNAKE_CASE:Dict  					=    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       )       ):
      SCREAMING_SNAKE_CASE:int  					=    checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
      SCREAMING_SNAKE_CASE:Dict  					=    checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
      SCREAMING_SNAKE_CASE:Any  					=    checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

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

  SCREAMING_SNAKE_CASE:Tuple  					=    checkpoint['''output_conv.1.weight_g''']
  SCREAMING_SNAKE_CASE:Dict  					=    checkpoint['''output_conv.1.weight_v''']
  SCREAMING_SNAKE_CASE:List[str]  					=    checkpoint['''output_conv.1.bias''']

  hf_model.remove_weight_norm()





@torch.no_grad()
def       A_     (       snake_case					,				snake_case					,				snake_case					,				snake_case=None					,				snake_case=None					,				):






  if config_path is not None:
    SCREAMING_SNAKE_CASE:int  					=    SpeechTaHifiGanConfig.from_pretrained(__a       )
  else:
    SCREAMING_SNAKE_CASE:int  					=    SpeechTaHifiGanConfig()

  SCREAMING_SNAKE_CASE:Any  					=    SpeechTaHifiGan(__a       )

  SCREAMING_SNAKE_CASE:Tuple  					=    torch.load(__a       )
  load_weights(orig_checkpoint["model"]["generator"]					,				__a					,				__a       )

  SCREAMING_SNAKE_CASE:Optional[int]  					=    np.load(__a       )
  SCREAMING_SNAKE_CASE:int  					=    stats[0].reshape(-1       )
  SCREAMING_SNAKE_CASE:Optional[int]  					=    stats[1].reshape(-1       )
  SCREAMING_SNAKE_CASE:str  					=    torch.from_numpy(__a       ).float()
  SCREAMING_SNAKE_CASE:List[Any]  					=    torch.from_numpy(__a       ).float()

  model.save_pretrained(__a       )

  if repo_id:
    print("Pushing to the hub..."       )
    model.push_to_hub(__a       )


if __name__ == "__main__":
    A_				  =							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."
    )

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










'''simple docstring'''
import csv

import tweepy

# Twitter API credentials
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''


def 			a      (			__a   )  ->	None:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= tweepy.OAuthHandler(__a ,					__a   )
					auth.set_access_token(__a ,					__a   )
					UpperCamelCase__   :List[str]												= tweepy.API(__a   )

					# initialize a list to hold all the tweepy Tweets
					UpperCamelCase__   :Dict												= []

					# make initial request for most recent tweets (200 is the maximum allowed count)
					UpperCamelCase__   :Tuple												= api.user_timeline(screen_name=__a ,					count=200   )

					# save most recent tweets
					alltweets.extend(__a   )

					# save the id of the oldest tweet less one
					UpperCamelCase__   :Union[str, Any]												= alltweets[-1].id - 1

					# keep grabbing tweets until there are no tweets left to grab
					while len(__a   ) > 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=__a ,					count=200 ,					max_id=__a   )

										# save most recent tweets
										alltweets.extend(__a   )

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

										print(f'''...{len(__a   )} tweets downloaded so far'''   )

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


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