



'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger('''transformers.models.speecht5''')


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     Union[str, Any]      ):
		hf_model.apply_weight_norm()

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	checkpoint["""input_conv.weight_g"""]
		__SCREAMING_SNAKE_CASE :	str					 =	checkpoint["""input_conv.weight_v"""]
		__SCREAMING_SNAKE_CASE :	int					 =	checkpoint["""input_conv.bias"""]

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

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

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	checkpoint["""output_conv.1.weight_g"""]
		__SCREAMING_SNAKE_CASE :	str					 =	checkpoint["""output_conv.1.weight_v"""]
		__SCREAMING_SNAKE_CASE :	Tuple					 =	checkpoint["""output_conv.1.bias"""]

		hf_model.remove_weight_norm()


@torch.no_grad()
def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     List[Any] ,   _lowerCamelCase:     List[str]=None ,   _lowerCamelCase:     Dict=None ,   ):






		if config_path is not None:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	SpeechTaHifiGanConfig.from_pretrained(_snake_case      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	SpeechTaHifiGanConfig()

		__SCREAMING_SNAKE_CASE :	List[str]					 =	SpeechTaHifiGan(_snake_case      )

		__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.load(_snake_case      )
		load_weights(orig_checkpoint["""model"""]["""generator"""] ,   _snake_case ,   _snake_case      )

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




UpperCamelCase__ :			List[str]												=				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich