





'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase_					:    Dict      	=			logging.get_logger('transformers.models.speecht5')






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      List[str]    ,							lowercase						:      Optional[Any]						)	->	Tuple:
				hf_model.apply_weight_norm()

				_a								= checkpoint["input_conv.weight_g"]
				_a								= checkpoint["input_conv.weight_v"]
				_a								= checkpoint["input_conv.bias"]

				for i in range(len(config.upsample_rates						)						):
								_a								= checkpoint[F'upsamples.{i}.1.weight_g']
								_a								= checkpoint[F'upsamples.{i}.1.weight_v']
								_a								= 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								= checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
												_a								= checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
												_a								= checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

												_a								= checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
												_a								= checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
												_a								= checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

				_a								= checkpoint["output_conv.1.weight_g"]
				_a								= checkpoint["output_conv.1.weight_v"]
				_a								= checkpoint["output_conv.1.bias"]

				hf_model.remove_weight_norm()






@torch.no_grad()
def  _lowerCamelCase				( lowercase						:      Dict    ,							lowercase						:      List[str]    ,							lowercase						:      Any    ,							lowercase						:      int=None    ,							lowercase						:      List[str]=None    ,							)	->	Any:
				if config_path is not None:
								_a								= SpeechTaHifiGanConfig.from_pretrained(lowercase						)
				else:
								_a								= SpeechTaHifiGanConfig()

				_a								= SpeechTaHifiGan(lowercase						)

				_a								= torch.load(lowercase						)
				load_weights(orig_checkpoint["model"]["generator"]    ,							lowercase    ,							lowercase						)

				_a								= np.load(lowercase						)
				_a								= stats[0].reshape(-1						)
				_a								= stats[1].reshape(-1						)
				_a								= torch.from_numpy(lowercase						).float()
				_a								= 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__":
					lowerCAmelCase_					:    Tuple      	=			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_					:    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,
					)







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
