
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 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : str				,					__lowerCamelCase     : List[str]    ):
       hf_model.apply_weight_norm()

       snake_case       :   Optional[Any]							=      checkpoint["input_conv.weight_g"]
       snake_case       :   Union[str, Any]							=      checkpoint["input_conv.weight_v"]
       snake_case       :   List[Any]							=      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       :   Dict							=      checkpoint[f"""upsamples.{i}.1.weight_v"""]
              snake_case       :   Dict							=      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       :   Union[str, Any]							=      checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     snake_case       :   List[str]							=      checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     snake_case       :   Any							=      checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

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

       snake_case       :   List[str]							=      checkpoint["output_conv.1.weight_g"]
       snake_case       :   Optional[int]							=      checkpoint["output_conv.1.weight_v"]
       snake_case       :   int							=      checkpoint["output_conv.1.bias"]

       hf_model.remove_weight_norm()


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]=None				,					__lowerCamelCase     : Dict=None				,					):
       if config_path is not None:
              snake_case       :   Any							=      SpeechTaHifiGanConfig.from_pretrained(__lowerCamelCase    )
       else:
              snake_case       :   List[Any]							=      SpeechTaHifiGanConfig()

       snake_case       :   Tuple							=      SpeechTaHifiGan(__lowerCamelCase    )

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

       snake_case       :   int							=      np.load(__lowerCamelCase    )
       snake_case       :   List[str]							=      stats[0].reshape(-1    )
       snake_case       :   Dict							=      stats[1].reshape(-1    )
       snake_case       :   Optional[Any]							=      torch.from_numpy(__lowerCamelCase    ).float()
       snake_case       :   Union[str, Any]							=      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							=       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,
   )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase  							=						{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''ElectraTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ElectraForCausalLM''',
          '''ElectraForMaskedLM''',
          '''ElectraForMultipleChoice''',
          '''ElectraForPreTraining''',
          '''ElectraForQuestionAnswering''',
          '''ElectraForSequenceClassification''',
          '''ElectraForTokenClassification''',
          '''ElectraModel''',
          '''ElectraPreTrainedModel''',
          '''load_tf_weights_in_electra''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFElectraForMaskedLM''',
          '''TFElectraForMultipleChoice''',
          '''TFElectraForPreTraining''',
          '''TFElectraForQuestionAnswering''',
          '''TFElectraForSequenceClassification''',
          '''TFElectraForTokenClassification''',
          '''TFElectraModel''',
          '''TFElectraPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''FlaxElectraForCausalLM''',
          '''FlaxElectraForMaskedLM''',
          '''FlaxElectraForMultipleChoice''',
          '''FlaxElectraForPreTraining''',
          '''FlaxElectraForQuestionAnswering''',
          '''FlaxElectraForSequenceClassification''',
          '''FlaxElectraForTokenClassification''',
          '''FlaxElectraModel''',
          '''FlaxElectraPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
      from .tokenization_electra import ElectraTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_electra_fast import ElectraTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_electra import (
                ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                ElectraForCausalLM,
                ElectraForMaskedLM,
                ElectraForMultipleChoice,
                ElectraForPreTraining,
                ElectraForQuestionAnswering,
                ElectraForSequenceClassification,
                ElectraForTokenClassification,
                ElectraModel,
                ElectraPreTrainedModel,
                load_tf_weights_in_electra,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_electra import (
                TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFElectraForMaskedLM,
                TFElectraForMultipleChoice,
                TFElectraForPreTraining,
                TFElectraForQuestionAnswering,
                TFElectraForSequenceClassification,
                TFElectraForTokenClassification,
                TFElectraModel,
                TFElectraPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_electra import (
                FlaxElectraForCausalLM,
                FlaxElectraForMaskedLM,
                FlaxElectraForMultipleChoice,
                FlaxElectraForPreTraining,
                FlaxElectraForQuestionAnswering,
                FlaxElectraForSequenceClassification,
                FlaxElectraForTokenClassification,
                FlaxElectraModel,
                FlaxElectraPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



