


"""simple docstring"""



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    int		,				lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    hf_model.apply_weight_norm()

    snake_case       : Dict    =       checkpoint['input_conv.weight_g']
    snake_case       : Any    =       checkpoint['input_conv.weight_v']
    snake_case       : Union[str, Any]    =       checkpoint['input_conv.bias']

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

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

    snake_case       : Tuple    =       checkpoint['output_conv.1.weight_g']
    snake_case       : int    =       checkpoint['output_conv.1.weight_v']
    snake_case       : Optional[int]    =       checkpoint['output_conv.1.bias']

    hf_model.remove_weight_norm()







@torch.no_grad()
def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    Optional[Any]		,				lowercase    :    List[str]		,				lowercase    :    Tuple=None		,				lowercase    :    Optional[int]=None		,				)      ->		List[str]:


    """simple docstring"""





    if config_path is not None:
        snake_case       : Tuple    =       SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__					)
    else:
        snake_case       : List[Any]    =       SpeechTaHifiGanConfig()

    snake_case       : List[str]    =       SpeechTaHifiGan(lowerCAmelCase__					)

    snake_case       : Optional[int]    =       torch.load(lowerCAmelCase__					)
    load_weights(orig_checkpoint["model"]["generator"]		,				lowerCAmelCase__		,				lowerCAmelCase__					)

    snake_case       : Optional[Any]    =       np.load(lowerCAmelCase__					)
    snake_case       : Union[str, Any]    =       stats[0].reshape(-1					)
    snake_case       : Dict    =       stats[1].reshape(-1					)
    snake_case       : Any    =       torch.from_numpy(lowerCAmelCase__					).float()
    snake_case       : str    =       torch.from_numpy(lowerCAmelCase__					).float()

    model.save_pretrained(lowerCAmelCase__					)

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


if __name__ == "__main__":
  __snake_case       =       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       =       parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )












_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
