





'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCAmelCase					:		List[Any]		=							logging.get_logger('transformers.models.speecht5')




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      hf_model.apply_weight_norm()

      _snake_case		:	int					     =     checkpoint['''input_conv.weight_g''']
      _snake_case		:	str					     =     checkpoint['''input_conv.weight_v''']
      _snake_case		:	List[Any]					     =     checkpoint['''input_conv.bias''']

      for i in range(len(config.upsample_rates	)	):
            _snake_case		:	int					     =     checkpoint[f'''upsamples.{i}.1.weight_g''']
            _snake_case		:	List[Any]					     =     checkpoint[f'''upsamples.{i}.1.weight_v''']
            _snake_case		:	List[Any]					     =     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	)	):
                  _snake_case		:	str					     =     checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
                  _snake_case		:	Optional[Any]					     =     checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
                  _snake_case		:	Tuple					     =     checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

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

      _snake_case		:	str					     =     checkpoint['''output_conv.1.weight_g''']
      _snake_case		:	str					     =     checkpoint['''output_conv.1.weight_v''']
      _snake_case		:	Any					     =     checkpoint['''output_conv.1.bias''']

      hf_model.remove_weight_norm()




@torch.no_grad()
def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      ):





      """simple docstring"""



      if config_path is not None:
            _snake_case		:	Any					     =     SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase_	)
      else:
            _snake_case		:	Dict					     =     SpeechTaHifiGanConfig()

      _snake_case		:	str					     =     SpeechTaHifiGan(lowerCAmelCase_	)

      _snake_case		:	Optional[Any]					     =     torch.load(lowerCAmelCase_	)
      load_weights(orig_checkpoint['''model''']['''generator''']			,      lowerCAmelCase_			,      lowerCAmelCase_	)

      _snake_case		:	Tuple					     =     np.load(lowerCAmelCase_	)
      _snake_case		:	List[Any]					     =     stats[0].reshape(-1	)
      _snake_case		:	Tuple					     =     stats[1].reshape(-1	)
      _snake_case		:	Any					     =     torch.from_numpy(lowerCAmelCase_	).float()
      _snake_case		:	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					:		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					:		Optional[int]		=							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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		int		=							{
    'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							[
       'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PegasusXForConditionalGeneration',
       'PegasusXModel',
       'PegasusXPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pegasus_x import (
          PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
          PegasusXForConditionalGeneration,
          PegasusXModel,
          PegasusXPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


