
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 . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)




