


"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case__		:     Any    				=				logging.get_logger('''transformers.models.speecht5''')


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			int ,   _snake_case				:			List[Any]			):
      hf_model.apply_weight_norm()

      lowerCAmelCase					:	Optional[int]       =   checkpoint['''input_conv.weight_g''']
      lowerCAmelCase					:	Tuple       =   checkpoint['''input_conv.weight_v''']
      lowerCAmelCase					:	Tuple       =   checkpoint['''input_conv.bias''']

      for i in range(len(config.upsample_rates			)			):
            lowerCAmelCase					:	Dict       =   checkpoint[f'''upsamples.{i}.1.weight_g''']
            lowerCAmelCase					:	Any       =   checkpoint[f'''upsamples.{i}.1.weight_v''']
            lowerCAmelCase					:	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			)			):
                  lowerCAmelCase					:	int       =   checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
                  lowerCAmelCase					:	Union[str, Any]       =   checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
                  lowerCAmelCase					:	int       =   checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

                  lowerCAmelCase					:	List[Any]       =   checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
                  lowerCAmelCase					:	Tuple       =   checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
                  lowerCAmelCase					:	str       =   checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

      lowerCAmelCase					:	Any       =   checkpoint['''output_conv.1.weight_g''']
      lowerCAmelCase					:	int       =   checkpoint['''output_conv.1.weight_v''']
      lowerCAmelCase					:	List[str]       =   checkpoint['''output_conv.1.bias''']

      hf_model.remove_weight_norm()


@torch.no_grad()
def      _snake_case						(						_snake_case				:			Union[str, Any] ,   _snake_case				:			Union[str, Any] ,   _snake_case				:			Dict ,   _snake_case				:			Optional[Any]=None ,   _snake_case				:			List[Any]=None ,   ):
      if config_path is not None:
            lowerCAmelCase					:	Optional[int]       =   SpeechTaHifiGanConfig.from_pretrained(_snake_case			)
      else:
            lowerCAmelCase					:	Optional[Any]       =   SpeechTaHifiGanConfig()

      lowerCAmelCase					:	int       =   SpeechTaHifiGan(_snake_case			)

      lowerCAmelCase					:	List[str]       =   torch.load(_snake_case			)
      load_weights(orig_checkpoint['''model''']['''generator'''] ,   _snake_case ,   _snake_case			)

      lowerCAmelCase					:	List[Any]       =   np.load(_snake_case			)
      lowerCAmelCase					:	Optional[Any]       =   stats[0].reshape(-1			)
      lowerCAmelCase					:	Union[str, Any]       =   stats[1].reshape(-1			)
      lowerCAmelCase					:	Union[str, Any]       =   torch.from_numpy(_snake_case			).float()
      lowerCAmelCase					:	List[str]       =   torch.from_numpy(_snake_case			).float()

      model.save_pretrained(_snake_case			)

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


if __name__ == "__main__":
 snake_case__		:     int    				=				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.'''
 )

 snake_case__		:     Optional[int]    				=				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"""





snake_case__		:     List[Any]    				=				'''Tobias Carryer'''

from time import time



class 		snake_case_:






     def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Dict=int(time()				)				):  # noqa: B008
           lowerCAmelCase					:	str       =   multiplier
           lowerCAmelCase					:	Optional[int]       =   increment
           lowerCAmelCase					:	Optional[Any]       =   modulo
           lowerCAmelCase					:	Optional[Any]       =   seed






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Optional[int]       =   (self.multiplier * self.seed + self.increment) % self.modulo
           return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__		:     int    				=				LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
 while True:
  print(lcg.next_number())


