



"""simple docstring"""






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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


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

			__lowerCAmelCase   :						Optional[Any]				= checkpoint["input_conv.weight_g"]
			__lowerCAmelCase   :						Optional[int]				= checkpoint["input_conv.weight_v"]
			__lowerCAmelCase   :						int				= checkpoint["input_conv.bias"]

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

									__lowerCAmelCase   :						Optional[int]				= checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
									__lowerCAmelCase   :						str				= checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
									__lowerCAmelCase   :						int				= checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

			__lowerCAmelCase   :						Union[str, Any]				= checkpoint["output_conv.1.weight_g"]
			__lowerCAmelCase   :						Optional[Any]				= checkpoint["output_conv.1.weight_v"]
			__lowerCAmelCase   :						List[str]				= checkpoint["output_conv.1.bias"]

			hf_model.remove_weight_norm()


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

			__lowerCAmelCase   :						Optional[int]				= SpeechTaHifiGan(__snake_case			)

			__lowerCAmelCase   :						str				= torch.load(__snake_case			)
			load_weights(orig_checkpoint["model"]["generator"]      ,__snake_case      ,__snake_case			)

			__lowerCAmelCase   :						List[Any]				= np.load(__snake_case			)
			__lowerCAmelCase   :						List[str]				= stats[0].reshape(-1			)
			__lowerCAmelCase   :						Dict				= stats[1].reshape(-1			)
			__lowerCAmelCase   :						Tuple				= torch.from_numpy(__snake_case			).float()
			__lowerCAmelCase   :						Any				= 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__":
	__snake_case							:     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.'
	)

	__snake_case							:     List[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"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale