






'''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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any]     )	->      Tuple:

   """simple docstring"""
   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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						Optional[Any]=None,       SCREAMING_SNAKE_CASE__:						str=None,       )	->      int:

   """simple docstring"""
   if config_path is not None:
      __a					  =     SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE__     )
   else:
      __a					  =     SpeechTaHifiGanConfig()

   __a					  =     SpeechTaHifiGan(SCREAMING_SNAKE_CASE__     )

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

   __a					  =     np.load(SCREAMING_SNAKE_CASE__     )
   __a					  =     stats[0].reshape(-1     )
   __a					  =     stats[1].reshape(-1     )
   __a					  =     torch.from_numpy(SCREAMING_SNAKE_CASE__     ).float()
   __a					  =     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__":
 __UpperCamelCase   :							int         =   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   :							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 argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)