'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__  :						List[Any]					=      logging.get_logger('transformers.models.speecht5')







def __UpperCamelCase( _A       :					Any	,      _A       :					int	,      _A       :					str     ):
   '''simple docstring'''
   hf_model.apply_weight_norm()

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

   for i in range(len(config.upsample_rates     )     ):
      UpperCAmelCase__				:					Union[str, Any] 					=	checkpoint[F'''upsamples.{i}.1.weight_g''']
      UpperCAmelCase__				:					int 					=	checkpoint[F'''upsamples.{i}.1.weight_v''']
      UpperCAmelCase__				:					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     )     ):
         UpperCAmelCase__				:					Union[str, 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__				:					List[Any] 					=	checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
         UpperCAmelCase__				:					Dict 					=	checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
         UpperCAmelCase__				:					str 					=	checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

   UpperCAmelCase__				:					Union[str, Any] 					=	checkpoint['''output_conv.1.weight_g''']
   UpperCAmelCase__				:					Optional[int] 					=	checkpoint['''output_conv.1.weight_v''']
   UpperCAmelCase__				:					List[str] 					=	checkpoint['''output_conv.1.bias''']

   hf_model.remove_weight_norm()







@torch.no_grad()
def __UpperCamelCase( _A       :					str	,      _A       :					Any	,      _A       :					Dict	,      _A       :					Dict=None	,      _A       :					str=None	,      ):
   '''simple docstring'''
   if config_path is not None:
      UpperCAmelCase__				:					Union[str, Any] 					=	SpeechTaHifiGanConfig.from_pretrained(_A     )
   else:
      UpperCAmelCase__				:					Union[str, Any] 					=	SpeechTaHifiGanConfig()

   UpperCAmelCase__				:					Tuple 					=	SpeechTaHifiGan(_A     )

   UpperCAmelCase__				:					Optional[int] 					=	torch.load(_A     )
   load_weights(orig_checkpoint['''model''']['''generator''']	,      _A	,      _A     )

   UpperCAmelCase__				:					Tuple 					=	np.load(_A     )
   UpperCAmelCase__				:					Dict 					=	stats[0].reshape(-1     )
   UpperCAmelCase__				:					str 					=	stats[1].reshape(-1     )
   UpperCAmelCase__				:					int 					=	torch.from_numpy(_A     ).float()
   UpperCAmelCase__				:					int 					=	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__":
  UpperCamelCase__  :						Any					=      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.'
  )

  UpperCamelCase__  :						str					=      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'''

from __future__ import annotations







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if (direction == 1 and array[indexa] > array[indexa]) or (
       direction == 0 and array[indexa] < array[indexa]
   ):
      UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	array[indexa], array[indexa]







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					List[Any] 					=	int(length / 2     )
      for i in range(_A	,      low + middle     ):
         comp_and_swap(_A	,      _A	,      i + middle	,      _A     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )
      bitonic_merge(_A	,      low + middle	,      _A	,      _A     )







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					Optional[int] 					=	int(length / 2     )
      bitonic_sort(_A	,      _A	,      _A	,      1     )
      bitonic_sort(_A	,      low + middle	,      _A	,      0     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )


if __name__ == "__main__":
  UpperCamelCase__  :						Dict					=      input('Enter numbers separated by a comma:\n').strip()
  UpperCamelCase__  :						Tuple					=      [int(item.strip()) for item in user_input.split(',')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('\nSorted array in ascending order is: ', end='')
  print(*unsorted, sep=', ')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('Sorted array in descending order is: ', end='')
  print(*unsorted, sep=', ')




