






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase    							=      logging.get_logger('transformers.models.speecht5')


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					int							)     ->  Any:
   hf_model.apply_weight_norm()

   __UpperCamelCase	:			str					=					checkpoint["""input_conv.weight_g"""]
   __UpperCamelCase	:			List[Any]					=					checkpoint["""input_conv.weight_v"""]
   __UpperCamelCase	:			Dict					=					checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates							)							):
      __UpperCamelCase	:			Dict					=					checkpoint[f'upsamples.{i}.1.weight_g']
      __UpperCamelCase	:			str					=					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	:			Dict					=					checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
         __UpperCamelCase	:			Tuple					=					checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
         __UpperCamelCase	:			Union[str, Any]					=					checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

         __UpperCamelCase	:			Optional[Any]					=					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
         __UpperCamelCase	:			List[Any]					=					checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
         __UpperCamelCase	:			Union[str, Any]					=					checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

   __UpperCamelCase	:			List[str]					=					checkpoint["""output_conv.1.weight_g"""]
   __UpperCamelCase	:			Any					=					checkpoint["""output_conv.1.weight_v"""]
   __UpperCamelCase	:			Optional[int]					=					checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					int=None					,							__lowerCAmelCase							:					int=None					,							)     ->  List[Any]:
   if config_path is not None:
      __UpperCamelCase	:			Any					=					SpeechTaHifiGanConfig.from_pretrained(__lowerCAmelCase							)
   else:
      __UpperCamelCase	:			Optional[Any]					=					SpeechTaHifiGanConfig()

   __UpperCamelCase	:			Any					=					SpeechTaHifiGan(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					torch.load(__lowerCAmelCase							)
   load_weights(orig_checkpoint["""model"""]["""generator"""]					,							__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					np.load(__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					stats[0].reshape(-1							)
   __UpperCamelCase	:			Dict					=					stats[1].reshape(-1							)
   __UpperCamelCase	:			Dict					=					torch.from_numpy(__lowerCAmelCase							).float()
   __UpperCamelCase	:			Dict					=					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__":
       UpperCamelCase    							=      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    							=      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 re

from filelock import FileLock


try:
       import nltk

       UpperCamelCase    							=      True
except (ImportError, ModuleNotFoundError):
       UpperCamelCase    							=      False

if NLTK_AVAILABLE:
       with FileLock('.lock') as lock:
              nltk.download('punkt', quiet=True)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  str:
   re.sub("""<n>"""					,							""""""					,							__lowerCAmelCase							)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(__lowerCAmelCase							)							)



