



'''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       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Any:
 hf_model.apply_weight_norm()

 _snake_case    :  List[Any]					=					checkpoint["""input_conv.weight_g"""]
 _snake_case    :  Dict					=					checkpoint["""input_conv.weight_v"""]
 _snake_case    :  List[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    :  int					=					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    :  str					=					checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
   _snake_case    :  List[Any]					=					checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
   _snake_case    :  Optional[int]					=					checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

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

 _snake_case    :  Union[str, Any]					=					checkpoint["""output_conv.1.weight_g"""]
 _snake_case    :  int					=					checkpoint["""output_conv.1.weight_v"""]
 _snake_case    :  Optional[Any]					=					checkpoint["""output_conv.1.bias"""]

 hf_model.remove_weight_norm()



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	int=None				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=None				,       )  ->     int:
 if config_path is not None:
  _snake_case    :  Tuple					=					SpeechTaHifiGanConfig.from_pretrained(_A				)
 else:
  _snake_case    :  Optional[Any]					=					SpeechTaHifiGanConfig()

 _snake_case    :  List[Any]					=					SpeechTaHifiGan(_A				)

 _snake_case    :  str					=					torch.load(_A				)
 load_weights(orig_checkpoint["""model"""]["""generator"""]				,       _A				,       _A				)

 _snake_case    :  str					=					np.load(_A				)
 _snake_case    :  int					=					stats[0].reshape(-1				)
 _snake_case    :  Optional[Any]					=					stats[1].reshape(-1				)
 _snake_case    :  Dict					=					torch.from_numpy(_A				).float()
 _snake_case    :  Tuple					=					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,
  )





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
