



"""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    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						hf_model.apply_weight_norm()

						snake_case_ :Optional[int]						=			checkpoint["""input_conv.weight_g"""]
						snake_case_ :Optional[int]						=			checkpoint["""input_conv.weight_v"""]
						snake_case_ :int						=			checkpoint["""input_conv.bias"""]

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

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

						snake_case_ :Tuple						=			checkpoint["""output_conv.1.weight_g"""]
						snake_case_ :Optional[Any]						=			checkpoint["""output_conv.1.weight_v"""]
						snake_case_ :int						=			checkpoint["""output_conv.1.bias"""]

						hf_model.remove_weight_norm()
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase=None,					_lowercase=None,					):







						'''simple docstring'''


						if config_path is not None:
												snake_case_ :Union[str, Any]						=			SpeechTaHifiGanConfig.from_pretrained(_lowercase	)
						else:
												snake_case_ :Tuple						=			SpeechTaHifiGanConfig()

						snake_case_ :Any						=			SpeechTaHifiGan(_lowercase	)

						snake_case_ :Any						=			torch.load(_lowercase	)
						load_weights(orig_checkpoint["""model"""]["""generator"""],					_lowercase,					_lowercase	)

						snake_case_ :Tuple						=			np.load(_lowercase	)
						snake_case_ :Optional[int]						=			stats[0].reshape(-1	)
						snake_case_ :Optional[int]						=			stats[1].reshape(-1	)
						snake_case_ :Any						=			torch.from_numpy(_lowercase	).float()
						snake_case_ :str						=			torch.from_numpy(_lowercase	).float()

						model.save_pretrained(_lowercase	)

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


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,
						)







def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]						):





  """simple docstring"""



  a__	:					Optional[Any]										=len(SCREAMING_SNAKE_CASE						)
  while cur > 1:
    # Find the maximum number in arr
    a__	:					List[Any]										=arr.index(max(arr[0:cur]						)						)
    # Reverse from 0 to mi
    a__	:					int										=arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE						)]
    # Reverse whole list
    a__	:					List[str]										=arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE						)]
    cur -= 1
  return arr


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       input("""Enter numbers separated by a comma:\n""").strip()
     UpperCAmelCase	:	Optional[int]		     =       [int(item) for item in user_input.split(""",""")]
     print(pancake_sort(unsorted))






