



'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_A				:    Optional[int]     						=logging.get_logger('''transformers.models.speecht5''')







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Dict:
	hf_model.apply_weight_norm()

	_lowercase					:       int       =		checkpoint['input_conv.weight_g']
	_lowercase					:       Union[str, Any]       =		checkpoint['input_conv.weight_v']
	_lowercase					:       int       =		checkpoint['input_conv.bias']

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

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

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

	hf_model.remove_weight_norm()







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

	_lowercase					:       Dict       =		SpeechTaHifiGan(_lowercase					)

	_lowercase					:       Optional[Any]       =		torch.load(_lowercase					)
	load_weights(orig_checkpoint['model']['generator'],				_lowercase,				_lowercase					)

	_lowercase					:       Optional[int]       =		np.load(_lowercase					)
	_lowercase					:       Union[str, Any]       =		stats[0].reshape(-1					)
	_lowercase					:       Tuple       =		stats[1].reshape(-1					)
	_lowercase					:       Any       =		torch.from_numpy(_lowercase					).float()
	_lowercase					:       List[str]       =		torch.from_numpy(_lowercase					).float()

	model.save_pretrained(_lowercase					)

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


if __name__ == "__main__":
		_A				:    List[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.'''
		)

		_A				:    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'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return f'''{i * "  "}*''' if i else "\n##"







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[Any]       =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(_lowercase					) or old_parts[i] != new_part) and new_part:
			print(f'''{md_prefix(_lowercase					)} {new_part.replace("_",				" "					).title()}'''					)
	return new_path







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


if __name__ == "__main__":
		print_directory_md('''.''')
