

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 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Optional[int]	)						->				List[str]:




	'''simple docstring'''




	hf_model.apply_weight_norm()

	_UpperCamelCase   :		Any			=						checkpoint['input_conv.weight_g']
	_UpperCamelCase   :		Dict			=						checkpoint['input_conv.weight_v']
	_UpperCamelCase   :		List[Any]			=						checkpoint['input_conv.bias']

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

			_UpperCamelCase   :		List[Any]			=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
			_UpperCamelCase   :		List[str]			=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
			_UpperCamelCase   :		Optional[Any]			=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

	_UpperCamelCase   :		Optional[Any]			=						checkpoint['output_conv.1.weight_g']
	_UpperCamelCase   :		Optional[Any]			=						checkpoint['output_conv.1.weight_v']
	_UpperCamelCase   :		Dict			=						checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Any=None							,       UpperCAmelCase_							:     Optional[int]=None							,       )						->				Tuple:




	'''simple docstring'''




	if config_path is not None:
		_UpperCamelCase   :		Optional[Any]			=						SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_	)
	else:
		_UpperCamelCase   :		List[Any]			=						SpeechTaHifiGanConfig()

	_UpperCamelCase   :		int			=						SpeechTaHifiGan(UpperCAmelCase_	)

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

	_UpperCamelCase   :		Tuple			=						np.load(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[Any]			=						stats[0].reshape(-1	)
	_UpperCamelCase   :		List[Any]			=						stats[1].reshape(-1	)
	_UpperCamelCase   :		Optional[Any]			=						torch.from_numpy(UpperCAmelCase_	).float()
	_UpperCamelCase   :		str			=						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__":
							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,
							)







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

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




