


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Tuple=None      ,					SCREAMING_SNAKE_CASE__      : List[Any]=None      ,					):
     if config_path is not None:
          __UpperCamelCase   							=SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )
     else:
          __UpperCamelCase   							=SpeechTaHifiGanConfig()

     __UpperCamelCase   							=SpeechTaHifiGan(SCREAMING_SNAKE_CASE__    )

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

     __UpperCamelCase   							=np.load(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=stats[0].reshape(-1    )
     __UpperCamelCase   							=stats[1].reshape(-1    )
     __UpperCamelCase   							=torch.from_numpy(SCREAMING_SNAKE_CASE__    ).float()
     __UpperCamelCase   							=torch.from_numpy(SCREAMING_SNAKE_CASE__    ).float()

     model.save_pretrained(SCREAMING_SNAKE_CASE__    )

     if repo_id:
          print('Pushing to the hub...'    )
          model.push_to_hub(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
   _A			=						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			=						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 warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





