




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase	:	Optional[Any]				=   logging.get_logger('transformers.models.speecht5')




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Optional[Any]:
   hf_model.apply_weight_norm()

   snake_case				:    Union[str, Any]           =	checkpoint["""input_conv.weight_g"""]
   snake_case				:    Tuple           =	checkpoint["""input_conv.weight_v"""]
   snake_case				:    Tuple           =	checkpoint["""input_conv.bias"""]

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

         snake_case				:    Optional[int]           =	checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
         snake_case				:    Optional[Any]           =	checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
         snake_case				:    Any           =	checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

   snake_case				:    str           =	checkpoint["""output_conv.1.weight_g"""]
   snake_case				:    Dict           =	checkpoint["""output_conv.1.weight_v"""]
   snake_case				:    Union[str, Any]           =	checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=None						,lowercase=None						,)  ->       Tuple:
   if config_path is not None:
      snake_case				:    Optional[Any]           =	SpeechTaHifiGanConfig.from_pretrained(lowercase					)
   else:
      snake_case				:    List[Any]           =	SpeechTaHifiGanConfig()

   snake_case				:    Union[str, Any]           =	SpeechTaHifiGan(lowercase					)

   snake_case				:    Tuple           =	torch.load(lowercase					)
   load_weights(orig_checkpoint["""model"""]["""generator"""]						,lowercase						,lowercase					)

   snake_case				:    Any           =	np.load(lowercase					)
   snake_case				:    Optional[Any]           =	stats[0].reshape(-1					)
   snake_case				:    Any           =	stats[1].reshape(-1					)
   snake_case				:    Optional[int]           =	torch.from_numpy(lowercase					).float()
   snake_case				:    str           =	torch.from_numpy(lowercase					).float()

   model.save_pretrained(lowercase					)

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


if __name__ == "__main__":
       lowerCamelCase	:	Union[str, 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.'
       )

       lowerCamelCase	:	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,
       )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:	List[str]				=   {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[Any]				=   [
           'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
           'Swinv2ForImageClassification',
           'Swinv2ForMaskedImageModeling',
           'Swinv2Model',
           'Swinv2PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swinva import (
                  SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinvaForImageClassification,
                  SwinvaForMaskedImageModeling,
                  SwinvaModel,
                  SwinvaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





