import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase       =		logging.get_logger("transformers.models.speecht5")
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					hf_model.apply_weight_norm()

					_UpperCamelCase			     =     checkpoint['''input_conv.weight_g''']
					_UpperCamelCase			     =     checkpoint['''input_conv.weight_v''']
					_UpperCamelCase			     =     checkpoint['''input_conv.bias''']

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

															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

					_UpperCamelCase			     =     checkpoint['''output_conv.1.weight_g''']
					_UpperCamelCase			     =     checkpoint['''output_conv.1.weight_v''']
					_UpperCamelCase			     =     checkpoint['''output_conv.1.bias''']

					hf_model.remove_weight_norm()
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     ):
					if config_path is not None:
										_UpperCamelCase			     =     SpeechTaHifiGanConfig.from_pretrained(__snake_case						)
					else:
										_UpperCamelCase			     =     SpeechTaHifiGanConfig()

					_UpperCamelCase			     =     SpeechTaHifiGan(__snake_case						)

					_UpperCamelCase			     =     torch.load(__snake_case						)
					load_weights(orig_checkpoint['''model''']['''generator''']  ,     __snake_case  ,     __snake_case						)

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

					model.save_pretrained(__snake_case						)

					if repo_id:
										print('''Pushing to the hub...'''						)
										model.push_to_hub(__snake_case						)


if __name__ == "__main__":
					_lowerCAmelCase       =		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       =		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 typing import List

from .keymap import KEYMAP, get_character
def 					_snake_case	(  __snake_case						):

					def decorator(__snake_case						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     '''handle_key'''  ,     []						)
										handle += [key]
										setattr(__snake_case  ,     '''handle_key'''  ,     __snake_case						)
										return func

					return decorator
def 					_snake_case	(  *__snake_case						):

					def decorator(__snake_case						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     '''handle_key'''  ,     []						)
										handle += keys
										setattr(__snake_case  ,     '''handle_key'''  ,     __snake_case						)
										return func

					return decorator
class     lowerCAmelCase_     (       __lowercase ):







							def __new__(   cls      :     Optional[Any] ,			_A      :     Optional[Any] ,			_A      :     Optional[int] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     super().__new__(cls ,			_A ,			_A ,			_A		)
												if not hasattr(_A ,			'''key_handler'''		):
																	setattr(_A ,			'''key_handler''' ,			{}		)
												setattr(_A ,			'''handle_input''' ,			KeyHandler.handle_input		)

												for value in attrs.values():
																	_UpperCamelCase			     =     getattr(_A ,			'''handle_key''' ,			[]		)
																	for key in handled_keys:
																						_UpperCamelCase			     =     value
												return new_cls







							@staticmethod
							def  UpperCamelCase_						(   cls      :     str		):
												_UpperCamelCase			     =     get_character()
												if char != KEYMAP["undefined"]:
																	_UpperCamelCase			     =     ord(_A		)
												_UpperCamelCase			     =     cls.key_handler.get(_A		)
												if handler:
																	_UpperCamelCase			     =     char
																	return handler(cls		)
												else:
																	return None




def 					_snake_case	(  cls						):
					return KeyHandler(cls.__name__  ,     cls.__bases__  ,     cls.__dict__.copy()						)
