



'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase:					Union[str, Any]						       =						logging.get_logger('transformers.models.speecht5')
def       lowerCamelCase__       (						_A ,			_A ,			_A				):
  hf_model.apply_weight_norm()

  a						:					str        =     checkpoint['input_conv.weight_g']
  a						:					Union[str, Any]        =     checkpoint['input_conv.weight_v']
  a						:					Tuple        =     checkpoint['input_conv.bias']

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

      a						:					int        =     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
      a						:					Tuple        =     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
      a						:					Dict        =     checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

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

  hf_model.remove_weight_norm()
@torch.no_grad()
def       lowerCamelCase__       (						_A ,			_A ,			_A ,			_A=None ,			_A=None ,			):
  if config_path is not None:
    a						:					Any        =     SpeechTaHifiGanConfig.from_pretrained(A__				)
  else:
    a						:					Any        =     SpeechTaHifiGanConfig()

  a						:					List[str]        =     SpeechTaHifiGan(A__				)

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

  a						:					Optional[int]        =     np.load(A__				)
  a						:					List[str]        =     stats[0].reshape(-1				)
  a						:					Union[str, Any]        =     stats[1].reshape(-1				)
  a						:					Optional[Any]        =     torch.from_numpy(A__				).float()
  a						:					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__":
     lowerCAmelCase:					Optional[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.'
     )

     lowerCAmelCase:					Union[str, Any]						       =						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'''


def 				__lowerCamelCase				(   A__    )       ->	list:






   """simple docstring"""
   UpperCamelCase					=  len(A__    )
   for i in range(1    ,						A__    ):
      UpperCamelCase					=  collection[i]
      UpperCamelCase					=  0
      UpperCamelCase					=  i - 1

      while low <= high:
         UpperCamelCase					=  (low + high) // 2
         if val < collection[mid]:
            UpperCamelCase					=  mid - 1
         else:
            UpperCamelCase					=  mid + 1
      for j in range(A__    ,						A__    ,						-1    ):
         UpperCamelCase					=  collection[j - 1]
      UpperCamelCase					=  val
   return collection


if __name__ == "__main__":
   _lowerCamelCase	:       int      	=	input("Enter numbers separated by a comma:\n").strip()
   _lowerCamelCase	:       Union[str, Any]      	=	[int(item) for item in user_input.split(",")]
   print(binary_insertion_sort(unsorted))

