






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCamelCase			:	Any								=logging.get_logger('transformers.models.speecht5')



def  a__ (__lowercase							:Optional[int]					,  __lowercase							:Optional[int]					,  __lowercase							:Optional[int]   )		-> Optional[int]:
				hf_model.apply_weight_norm()

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

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

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

				_A	:				Union[str, Any]   =      checkpoint['''output_conv.1.weight_g''']
				_A	:				List[str]   =      checkpoint['''output_conv.1.weight_v''']
				_A	:				Any   =      checkpoint['''output_conv.1.bias''']

				hf_model.remove_weight_norm()



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

				_A	:				Optional[Any]   =      SpeechTaHifiGan(__lowercase   )

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

				_A	:				int   =      np.load(__lowercase   )
				_A	:				List[str]   =      stats[0].reshape(-1   )
				_A	:				Any   =      stats[1].reshape(-1   )
				_A	:				Optional[int]   =      torch.from_numpy(__lowercase   ).float()
				_A	:				Any   =      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__":
	_UpperCamelCase			:	List[str]								=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.'
	)

	_UpperCamelCase			:	Tuple								=parser.parse_args()
	convert_hifigan_checkpoint(
	    args.checkpoint_path,
	    args.stats_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase			:	Any								={
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[Any]								=[
	    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MegatronBertForCausalLM',
	    'MegatronBertForMaskedLM',
	    'MegatronBertForMultipleChoice',
	    'MegatronBertForNextSentencePrediction',
	    'MegatronBertForPreTraining',
	    'MegatronBertForQuestionAnswering',
	    'MegatronBertForSequenceClassification',
	    'MegatronBertForTokenClassification',
	    'MegatronBertModel',
	    'MegatronBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	_UpperCamelCase			:	Optional[int]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



