






"""simple docstring"""



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
A__	:    int			 = logging.get_logger('transformers.models.speecht5')

def 			_snake_case    (					lowerCamelCase__     :   int    ,						lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   Optional[int]  )    ->     str:
							hf_model.apply_weight_norm()

							lowerCamelCase_	:							str									=checkpoint["input_conv.weight_g"]
							lowerCamelCase_	:							Union[str, Any]									=checkpoint["input_conv.weight_v"]
							lowerCamelCase_	:							str									=checkpoint["input_conv.bias"]

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

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

							lowerCamelCase_	:							List[str]									=checkpoint["output_conv.1.weight_g"]
							lowerCamelCase_	:							str									=checkpoint["output_conv.1.weight_v"]
							lowerCamelCase_	:							Dict									=checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()

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

							lowerCamelCase_	:							str									=SpeechTaHifiGan(lowerCamelCase__  )

							lowerCamelCase_	:							List[Any]									=torch.load(lowerCamelCase__  )
							load_weights(orig_checkpoint["model"]["generator"]    ,						lowerCamelCase__    ,						lowerCamelCase__  )

							lowerCamelCase_	:							Dict									=np.load(lowerCamelCase__  )
							lowerCamelCase_	:							List[Any]									=stats[0].reshape(-1  )
							lowerCamelCase_	:							List[str]									=stats[1].reshape(-1  )
							lowerCamelCase_	:							int									=torch.from_numpy(lowerCamelCase__  ).float()
							lowerCamelCase_	:							Union[str, 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__":
						A__	:    Union[str, 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"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


A__	:    List[str]			 = {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

						A__	:    List[str]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



