



'''simple docstring'''
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case_			:     List[Any]  	=  logging.get_logger('transformers.models.speecht5')






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

						_UpperCamelCase :		Any			    =       checkpoint['input_conv.weight_g']
						_UpperCamelCase :		Optional[int]			    =       checkpoint['input_conv.weight_v']
						_UpperCamelCase :		str			    =       checkpoint['input_conv.bias']

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

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

						_UpperCamelCase :		int			    =       checkpoint['output_conv.1.weight_g']
						_UpperCamelCase :		List[str]			    =       checkpoint['output_conv.1.weight_v']
						_UpperCamelCase :		List[Any]			    =       checkpoint['output_conv.1.bias']

						hf_model.remove_weight_norm()






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

						_UpperCamelCase :		int			    =       SpeechTaHifiGan(UpperCAmelCase_							)

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

						_UpperCamelCase :		int			    =       np.load(UpperCAmelCase_							)
						_UpperCamelCase :		Any			    =       stats[0].reshape(-1							)
						_UpperCamelCase :		Dict			    =       stats[1].reshape(-1							)
						_UpperCamelCase :		Tuple			    =       torch.from_numpy(UpperCAmelCase_							).float()
						_UpperCamelCase :		int			    =       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__":
					snake_case_			:     Optional[int]  	=  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.'
					)

					snake_case_			:     List[str]  	=  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 __future__ import annotations






def A__   (					UpperCAmelCase_							):
						if not nums:
												return 0
						_UpperCamelCase :		Any			    =       nums[0]
						_UpperCamelCase :		Optional[int]			    =       0
						for num in nums[1:]:
												_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       (
												    max_excluding + num,
												    max(UpperCAmelCase_  ,  UpperCAmelCase_							),
												)
						return max(UpperCAmelCase_  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


