


'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase				:Tuple        =      logging.get_logger('''transformers.models.speecht5''')





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	hf_model.apply_weight_norm()

	__magic_name__	:   List[str]					     =					checkpoint['input_conv.weight_g']
	__magic_name__	:   int					     =					checkpoint['input_conv.weight_v']
	__magic_name__	:   List[Any]					     =					checkpoint['input_conv.bias']

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

			__magic_name__	:   Union[str, Any]					     =					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
			__magic_name__	:   Dict					     =					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
			__magic_name__	:   Tuple					     =					checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

	__magic_name__	:   Tuple					     =					checkpoint['output_conv.1.weight_g']
	__magic_name__	:   int					     =					checkpoint['output_conv.1.weight_v']
	__magic_name__	:   Union[str, Any]					     =					checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     int=None		,		lowerCAmelCase    :     Tuple=None		,		):





	"""simple docstring"""







	if config_path is not None:
		__magic_name__	:   Dict					     =					SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase		)
	else:
		__magic_name__	:   int					     =					SpeechTaHifiGanConfig()

	__magic_name__	:   Optional[Any]					     =					SpeechTaHifiGan(lowerCAmelCase		)

	__magic_name__	:   str					     =					torch.load(lowerCAmelCase		)
	load_weights(orig_checkpoint['model']['generator']		,		lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   List[Any]					     =					np.load(lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					stats[0].reshape(-1		)
	__magic_name__	:   Dict					     =					stats[1].reshape(-1		)
	__magic_name__	:   Any					     =					torch.from_numpy(lowerCAmelCase		).float()
	__magic_name__	:   Any					     =					torch.from_numpy(lowerCAmelCase		).float()

	model.save_pretrained(lowerCAmelCase		)

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


if __name__ == "__main__":
							lowerCAmelCase				: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.'''
							)

							lowerCAmelCase				:Any        =      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'''

def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		lowerCAmelCase		) and isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   str					     =					len(set_a.intersection(lowerCAmelCase		)		)

		if alternative_union:
			__magic_name__	:   List[str]					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
		else:
			__magic_name__	:   Any					     =					len(set_a.union(lowerCAmelCase		)		)

		return intersection / union

	if isinstance(lowerCAmelCase		,		(list, tuple)		) and isinstance(lowerCAmelCase		,		(list, tuple)		):
		__magic_name__	:   str					     =					[element for element in set_a if element in set_b]

		if alternative_union:
			__magic_name__	:   Dict					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
			return len(lowerCAmelCase		) / union
		else:
			__magic_name__	:   Any					     =					set_a + [element for element in set_b if element not in set_a]
			return len(lowerCAmelCase		) / len(lowerCAmelCase		)

		return len(lowerCAmelCase		) / len(lowerCAmelCase		)
	return None


if __name__ == "__main__":
							lowerCAmelCase				:Dict        =      {'''a''', '''b''', '''c''', '''d''', '''e'''}
							lowerCAmelCase				:Tuple        =      {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
							print(jaccard_similarity(set_a, set_b))