


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     hf_model.apply_weight_norm()

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

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

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

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

     hf_model.remove_weight_norm()






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None					,  )					-> Tuple:


     """simple docstring"""


     if config_path is not None:
          __UpperCamelCase =			SpeechTaHifiGanConfig.from_pretrained(_lowercase )
     else:
          __UpperCamelCase =			SpeechTaHifiGanConfig()

     __UpperCamelCase =			SpeechTaHifiGan(_lowercase )

     __UpperCamelCase =			torch.load(_lowercase )
     load_weights(orig_checkpoint['model']['generator']					,  _lowercase					,  _lowercase )

     __UpperCamelCase =			np.load(_lowercase )
     __UpperCamelCase =			stats[0].reshape(-1 )
     __UpperCamelCase =			stats[1].reshape(-1 )
     __UpperCamelCase =			torch.from_numpy(_lowercase ).float()
     __UpperCamelCase =			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__":
    __snake_case   							=						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.'''
    )

    __snake_case   							=						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 Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = None						,   _A = None						,   _A = True						,   _A = None						,   _A = False						,   _A = None						,   _A = True						,   _A = "arrow"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split    )
									UpperCamelCase				:		Dict				   =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_A						,   file_format=self._file_format						,   )
									return self.builder.as_dataset(split=self.split    )



