


import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
A   :					Dict	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Any					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						UniSpeechSatForSequenceClassification.from_pretrained(_A					,      config=_A		)
      lowerCamelCase__	:       Optional[int]					=						downstream_dict["projector.weight"]
      lowerCamelCase__	:       Optional[int]					=						downstream_dict["projector.bias"]
      lowerCamelCase__	:       int					=						downstream_dict["model.post_net.linear.weight"]
      lowerCamelCase__	:       Optional[Any]					=						downstream_dict["model.post_net.linear.bias"]
      return model

def    lowercase_	(	_A    :					str					,      _A    :					List[Any]					,      _A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						UniSpeechSatForAudioFrameClassification.from_pretrained(_A					,      config=_A		)
      lowerCamelCase__	:       List[str]					=						downstream_dict["model.linear.weight"]
      lowerCamelCase__	:       Optional[int]					=						downstream_dict["model.linear.bias"]
      return model

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					str					,      _A    :					Optional[Any]		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						UniSpeechSatForXVector.from_pretrained(_A					,      config=_A		)
      lowerCamelCase__	:       List[Any]					=						downstream_dict["connector.weight"]
      lowerCamelCase__	:       int					=						downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
            lowerCamelCase__	:       Optional[Any]					=						downstream_dict[
                F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
            ]
            lowerCamelCase__	:       Dict					=						downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

      lowerCamelCase__	:       str					=						downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      lowerCamelCase__	:       List[Any]					=						downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      lowerCamelCase__	:       Dict					=						downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      lowerCamelCase__	:       Optional[Any]					=						downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      lowerCamelCase__	:       Dict					=						downstream_dict["objective.W"]
      return model

@torch.no_grad()
def    lowercase_	(	_A    :					str					,      _A    :					Tuple					,      _A    :					str					,      _A    :					Tuple		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						torch.load(_A					,      map_location="cpu"		)

      lowerCamelCase__	:       Dict					=						checkpoint["Downstream"]

      lowerCamelCase__	:       List[Any]					=						UniSpeechSatConfig.from_pretrained(_A		)
      lowerCamelCase__	:       Dict					=						WavaVecaFeatureExtractor.from_pretrained(
          _A					,      return_attention_mask=_A					,      do_normalize=_A		)

      lowerCamelCase__	:       int					=						hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"		):
            lowerCamelCase__	:       str					=						convert_classification(_A					,      _A					,      _A		)
      elif arch.endswith("ForAudioFrameClassification"		):
            lowerCamelCase__	:       Optional[Any]					=						convert_diarization(_A					,      _A					,      _A		)
      elif arch.endswith("ForXVector"		):
            lowerCamelCase__	:       Optional[Any]					=						convert_xvector(_A					,      _A					,      _A		)
      else:
            raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"		)

      if hf_config.use_weighted_layer_sum:
            lowerCamelCase__	:       Optional[Any]					=						checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_A		)
      hf_model.save_pretrained(_A		)


if __name__ == "__main__":
    A   :					Any	=      argparse.ArgumentParser()
    parser.add_argument(
        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
    )
    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
    A   :					List[Any]	=      parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A   :					Optional[int]	=      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SpeechT5ForSpeechToText",
        "SpeechT5ForSpeechToSpeech",
        "SpeechT5ForTextToSpeech",
        "SpeechT5Model",
        "SpeechT5PreTrainedModel",
        "SpeechT5HifiGan",
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    A   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


