

'''simple docstring'''

from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowercase__         =      logging.get_logger(__name__)




class 	A_			:






     '''simple docstring'''



     UpperCAmelCase_						:     str
     UpperCAmelCase_						:     str  		=  None


     @staticmethod
     def        UpperCAmelCase_	(		)						->     str:
      raise NotImplementedError


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					str					,      **lowercase_			:					Dict	)						->     Union[str, Any]:
      raise NotImplementedError


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Any	)						->     Optional[Any]:
      raise NotImplementedError


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[int]:
      if not self.is_available():
       raise RuntimeError(
           f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."""	)




     @classmethod
     def        UpperCAmelCase_	(		cls			:					Any	)						->     int:
      return f"""`pip install {cls.pip_package or cls.name}`"""




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[Any]  		=  """optuna"""


     @staticmethod
     def        UpperCAmelCase_	(		)						->     List[str]:
      return is_optuna_available()


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					int					,      lowercase_			:					str					,      **lowercase_			:					int	)						->     Tuple:
      return run_hp_search_optuna(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Union[str, Any]	)						->     Optional[Any]:
      return default_hp_space_optuna(lowercase_	)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  """ray"""
     UpperCAmelCase_						:     Dict  		=  """'ray[tune]'"""


     @staticmethod
     def        UpperCAmelCase_	(		)						->     Union[str, Any]:
      return is_ray_available()


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					str					,      **lowercase_			:					Tuple	)						->     Tuple:
      return run_hp_search_ray(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Dict	)						->     str:
      return default_hp_space_ray(lowercase_	)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  """sigopt"""


     @staticmethod
     def        UpperCAmelCase_	(		)						->     Union[str, Any]:
      return is_sigopt_available()


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					int					,      lowercase_			:					str					,      **lowercase_			:					int	)						->     Any:
      return run_hp_search_sigopt(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					int	)						->     List[Any]:
      return default_hp_space_sigopt(lowercase_	)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Union[str, Any]  		=  """wandb"""


     @staticmethod
     def        UpperCAmelCase_	(		)						->     str:
      return is_wandb_available()


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					str					,      **lowercase_			:					Union[str, Any]	)						->     Any:
      return run_hp_search_wandb(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int	)						->     Any:
      return default_hp_space_wandb(lowercase_	)



lowercase__         =      {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Optional[int]						 =			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
 if len(UpperCAmelCase_     ) > 0:
  UpperCAmelCase	:     int						 =			available_backends[0].name
  if len(UpperCAmelCase_     ) > 1:
   logger.info(
       F"""{len(UpperCAmelCase_     )} hyperparameter search backends available. Using {name} as the default."""     )
  return name
 raise RuntimeError(
     'No hyperparameter search backend available.\n'
     + '\n'.join(
         F""" - To install {backend.name} run {backend.pip_install()}"""
         for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()     )     )







'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     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     )     ):
   UpperCAmelCase	:     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	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			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__":
 lowercase__         =      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."
 )

 lowercase__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )




