




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase     :		Union[str, Any]								=       logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   WavaVecaForSequenceClassification.from_pretrained(snake_case__			,    config=snake_case__						)
       A__							=   downstream_dict['''projector.weight''']
       A__							=   downstream_dict['''projector.bias''']
       A__							=   downstream_dict['''model.post_net.linear.weight''']
       A__							=   downstream_dict['''model.post_net.linear.bias''']
       return model







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Union[str, Any]:



       """simple docstring"""

       A__							=   WavaVecaForAudioFrameClassification.from_pretrained(snake_case__			,    config=snake_case__						)
       A__							=   downstream_dict['''model.linear.weight''']
       A__							=   downstream_dict['''model.linear.bias''']
       return model







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Any:



       """simple docstring"""

       A__							=   WavaVecaForXVector.from_pretrained(snake_case__			,    config=snake_case__						)
       A__							=   downstream_dict['''connector.weight''']
       A__							=   downstream_dict['''connector.bias''']
       for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
              A__							=   downstream_dict[
                  f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
              ]
              A__							=   downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

       A__							=   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
       A__							=   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
       A__							=   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
       A__							=   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
       A__							=   downstream_dict['''objective.W''']
       return model







@torch.no_grad()
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   torch.load(snake_case__			,    map_location='''cpu'''						)

       A__							=   checkpoint['''Downstream''']

       A__							=   WavaVecaConfig.from_pretrained(snake_case__						)
       A__							=   WavaVecaFeatureExtractor.from_pretrained(
           snake_case__			,    return_attention_mask=snake_case__			,    do_normalize=snake_case__						)

       A__							=   hf_config.architectures[0]
       if arch.endswith('''ForSequenceClassification'''						):
              A__							=   convert_classification(snake_case__			,    snake_case__			,    snake_case__						)
       elif arch.endswith('''ForAudioFrameClassification'''						):
              A__							=   convert_diarization(snake_case__			,    snake_case__			,    snake_case__						)
       elif arch.endswith('''ForXVector'''						):
              A__							=   convert_xvector(snake_case__			,    snake_case__			,    snake_case__						)
       else:
              raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""						)

       if hf_config.use_weighted_layer_sum:
              A__							=   checkpoint['''Featurizer''']['''weights''']

       hf_feature_extractor.save_pretrained(snake_case__						)
       hf_model.save_pretrained(snake_case__						)


if __name__ == "__main__":
     _lowerCamelCase     :		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.""")
     _lowerCamelCase     :		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_speech_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase     :		Tuple								=       {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		int								=       ["""Speech2TextTokenizer"""]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[int]								=       ["""Speech2TextFeatureExtractor"""]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[Any]								=       [
      """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFSpeech2TextForConditionalGeneration""",
      """TFSpeech2TextModel""",
      """TFSpeech2TextPreTrainedModel""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		List[Any]								=       [
      """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2TextForConditionalGeneration""",
      """Speech2TextModel""",
      """Speech2TextPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
  from .processing_speech_to_text import SpeechaTextProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_speech_to_text import SpeechaTextTokenizer

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_speech_to_text import (
        TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSpeechaTextForConditionalGeneration,
        TFSpeechaTextModel,
        TFSpeechaTextPreTrainedModel,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text import (
        SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextForConditionalGeneration,
        SpeechaTextModel,
        SpeechaTextPreTrainedModel,
    )

else:
  import sys

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


