




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__magic_name__  :  Any					=       logging.get_logger("""transformers.models.speecht5""")
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  hf_model.apply_weight_norm()

  lowerCAmelCase__										=      checkpoint['''input_conv.weight_g''']
  lowerCAmelCase__										=      checkpoint['''input_conv.weight_v''']
  lowerCAmelCase__										=      checkpoint['''input_conv.bias''']

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

      lowerCAmelCase__										=      checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
      lowerCAmelCase__										=      checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
      lowerCAmelCase__										=      checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

  lowerCAmelCase__										=      checkpoint['''output_conv.1.weight_g''']
  lowerCAmelCase__										=      checkpoint['''output_conv.1.weight_v''']
  lowerCAmelCase__										=      checkpoint['''output_conv.1.bias''']

  hf_model.remove_weight_norm()
@torch.no_grad()
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase=None   , __lowerCAmelCase=None   , ):
  if config_path is not None:
    lowerCAmelCase__										=      SpeechTaHifiGanConfig.from_pretrained(__lowerCAmelCase				)
  else:
    lowerCAmelCase__										=      SpeechTaHifiGanConfig()

  lowerCAmelCase__										=      SpeechTaHifiGan(__lowerCAmelCase				)

  lowerCAmelCase__										=      torch.load(__lowerCAmelCase				)
  load_weights(orig_checkpoint['''model''']['''generator''']   , __lowerCAmelCase   , __lowerCAmelCase				)

  lowerCAmelCase__										=      np.load(__lowerCAmelCase				)
  lowerCAmelCase__										=      stats[0].reshape(-1				)
  lowerCAmelCase__										=      stats[1].reshape(-1				)
  lowerCAmelCase__										=      torch.from_numpy(__lowerCAmelCase				).float()
  lowerCAmelCase__										=      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__":
      __magic_name__  :  List[Any]					=       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."""
      )

      __magic_name__  :  Any					=       parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  Union[str, Any]					=       {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  str					=       [
          """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AltCLIPPreTrainedModel""",
          """AltCLIPModel""",
          """AltCLIPTextModel""",
          """AltCLIPVisionModel""",
      ]


if TYPE_CHECKING:
      from .configuration_altclip import (
          ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AltCLIPConfig,
          AltCLIPTextConfig,
          AltCLIPVisionConfig,
      )
      from .processing_altclip import AltCLIPProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_altclip import (
                ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                AltCLIPModel,
                AltCLIPPreTrainedModel,
                AltCLIPTextModel,
                AltCLIPVisionModel,
            )


else:
      import sys

      __magic_name__  :  str					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

