



"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase  			=		logging.get_logger("""transformers.models.speecht5""")
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      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__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=None      ,     ):
      if config_path is not None:
            UpperCAmelCase_     					=				SpeechTaHifiGanConfig.from_pretrained(_A )
      else:
            UpperCAmelCase_     					=				SpeechTaHifiGanConfig()

      UpperCAmelCase_     					=				SpeechTaHifiGan(_A )

      UpperCAmelCase_     					=				torch.load(_A )
      load_weights(orig_checkpoint["model"]["generator"]      ,     _A      ,     _A )

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

      model.save_pretrained(_A )

      if repo_id:
            print("Pushing to the hub..." )
            model.push_to_hub(_A )


if __name__ == "__main__":
  lowerCamelCase  			=		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."""
  )

  lowerCamelCase  			=		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__  :						str					=      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__  :						str					=      [
      'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GitForCausalLM',
      'GitModel',
      'GitPreTrainedModel',
      'GitVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

  UpperCamelCase__  :						Tuple					=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




