




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase						:   int		=   logging.get_logger("transformers.models.speecht5")



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 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 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Tuple=None						,							_lowerCAmelCase							:   Union[str, Any]=None						,							):




 """simple docstring"""







 if config_path is not None:
  UpperCAmelCase__							 =						SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						SpeechTaHifiGanConfig()

 UpperCAmelCase__							 =						SpeechTaHifiGan(_lowerCAmelCase	)

 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 load_weights(orig_checkpoint["model"]["generator"]						,							_lowerCAmelCase						,							_lowerCAmelCase	)

 UpperCAmelCase__							 =						np.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						stats[0].reshape(-1	)
 UpperCAmelCase__							 =						stats[1].reshape(-1	)
 UpperCAmelCase__							 =						torch.from_numpy(_lowerCAmelCase	).float()
 UpperCAmelCase__							 =						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__":
  _lowerCAmelCase						:   Dict		=   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						:   Optional[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_torch_available, is_vision_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





