


'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a_						:       Optional[Any]      			=				logging.get_logger("""transformers.models.speecht5""")





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		List[Any]	):
 hf_model.apply_weight_norm()

 lowerCamelCase_  				=						checkpoint["input_conv.weight_g"]
 lowerCamelCase_  				=						checkpoint["input_conv.weight_v"]
 lowerCamelCase_  				=						checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates	)	):
  lowerCamelCase_  				=						checkpoint[F'''upsamples.{i}.1.weight_g''']
  lowerCamelCase_  				=						checkpoint[F'''upsamples.{i}.1.weight_v''']
  lowerCamelCase_  				=						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	)	):
   lowerCamelCase_  				=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
   lowerCamelCase_  				=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
   lowerCamelCase_  				=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

   lowerCamelCase_  				=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
   lowerCamelCase_  				=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
   lowerCamelCase_  				=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

 lowerCamelCase_  				=						checkpoint["output_conv.1.weight_g"]
 lowerCamelCase_  				=						checkpoint["output_conv.1.weight_v"]
 lowerCamelCase_  				=						checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()





@torch.no_grad()
def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[Any]=None      ,       UpperCAmelCase_		:		Tuple=None      ,       ):
 if config_path is not None:
  lowerCamelCase_  				=						SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_	)
 else:
  lowerCamelCase_  				=						SpeechTaHifiGanConfig()

 lowerCamelCase_  				=						SpeechTaHifiGan(UpperCAmelCase_	)

 lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
 load_weights(orig_checkpoint["model"]["generator"]      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)

 lowerCamelCase_  				=						np.load(UpperCAmelCase_	)
 lowerCamelCase_  				=						stats[0].reshape(-1	)
 lowerCamelCase_  				=						stats[1].reshape(-1	)
 lowerCamelCase_  				=						torch.from_numpy(UpperCAmelCase_	).float()
 lowerCamelCase_  				=						torch.from_numpy(UpperCAmelCase_	).float()

 model.save_pretrained(UpperCAmelCase_	)

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


if __name__ == "__main__":
     a_						:       Dict      			=				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_						:       Dict      			=				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 collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

 lowerCamelCase_  				=						nums[0]
 for i in range(1      ,       len(UpperCAmelCase_	)	):
  lowerCamelCase_  				=						nums[i]
  lowerCamelCase_  				=						max(UpperCAmelCase_      ,       ans + num      ,       UpperCAmelCase_	)

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     a_						:       Any      			=				int(input("""Enter number of elements : """).strip())
     a_						:       Union[str, Any]      			=				list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
     print(max_subsequence_sum(array))



