


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase					:       int			    =     logging.get_logger("""transformers.models.speecht5""")



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   Tuple:
      hf_model.apply_weight_norm()

      a__	:  Dict     =   checkpoint["input_conv.weight_g"]
      a__	:  Optional[Any]     =   checkpoint["input_conv.weight_v"]
      a__	:  Union[str, Any]     =   checkpoint["input_conv.bias"]

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

                  a__	:  int     =   checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
                  a__	:  Any     =   checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
                  a__	:  Optional[int]     =   checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

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

      hf_model.remove_weight_norm()



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a=None			,				__a=None			,				)	->   List[str]:
      if config_path is not None:
            a__	:  Optional[Any]     =   SpeechTaHifiGanConfig.from_pretrained(__a      )
      else:
            a__	:  List[Any]     =   SpeechTaHifiGanConfig()

      a__	:  List[Any]     =   SpeechTaHifiGan(__a      )

      a__	:  Tuple     =   torch.load(__a      )
      load_weights(orig_checkpoint["model"]["generator"]			,				__a			,				__a      )

      a__	:  int     =   np.load(__a      )
      a__	:  Union[str, Any]     =   stats[0].reshape(-1      )
      a__	:  Optional[Any]     =   stats[1].reshape(-1      )
      a__	:  Optional[int]     =   torch.from_numpy(__a      ).float()
      a__	:  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__":
    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					:       Optional[int]			    =     parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )






from bisect import bisect
from itertools import accumulate







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  sorted(zip(snake_case__    ,			snake_case__					)    ,			key=lambda snake_case__					: x[0] / x[1]    ,			reverse=snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__		  =  list(accumulate(snake_case__					)					)
				lowerCAmelCase__		  =  bisect(snake_case__    ,			snake_case__					)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k]					)
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
