






"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase_	:				str										= logging.get_logger(__name__)



def     __lowercase					(    a :				Optional[int]       ,		a :				str       ,		a :				Optional[int] )					->					Union[str, Any]:
      __snake_case      :    str  			=WavaVecaForSequenceClassification.from_pretrained(a       ,		config=a )
      __snake_case      :    Union[str, Any]  			=downstream_dict['''projector.weight''']
      __snake_case      :    str  			=downstream_dict['''projector.bias''']
      __snake_case      :    int  			=downstream_dict['''model.post_net.linear.weight''']
      __snake_case      :    Union[str, Any]  			=downstream_dict['''model.post_net.linear.bias''']
      return model



def     __lowercase					(    a :				Optional[int]       ,		a :				Any       ,		a :				Dict )					->					Optional[int]:
      __snake_case      :    List[Any]  			=WavaVecaForAudioFrameClassification.from_pretrained(a       ,		config=a )
      __snake_case      :    Tuple  			=downstream_dict['''model.linear.weight''']
      __snake_case      :    List[str]  			=downstream_dict['''model.linear.bias''']
      return model



def     __lowercase					(    a :				Union[str, Any]       ,		a :				Optional[Any]       ,		a :				Optional[Any] )					->					Union[str, Any]:
      __snake_case      :    Union[str, Any]  			=WavaVecaForXVector.from_pretrained(a       ,		config=a )
      __snake_case      :    int  			=downstream_dict['''connector.weight''']
      __snake_case      :    Union[str, Any]  			=downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
            __snake_case      :    Any  			=downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            __snake_case      :    Union[str, Any]  			=downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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



@torch.no_grad()
def     __lowercase					(    a :				Union[str, Any]       ,		a :				Tuple       ,		a :				str       ,		a :				Dict )					->					Tuple:
      __snake_case      :    int  			=torch.load(a       ,		map_location='''cpu''' )

      __snake_case      :    str  			=checkpoint['''Downstream''']

      __snake_case      :    Any  			=WavaVecaConfig.from_pretrained(a )
      __snake_case      :    str  			=WavaVecaFeatureExtractor.from_pretrained(
          a       ,		return_attention_mask=a       ,		do_normalize=a )

      __snake_case      :    Dict  			=hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification''' ):
            __snake_case      :    Optional[int]  			=convert_classification(a       ,		a       ,		a )
      elif arch.endswith('''ForAudioFrameClassification''' ):
            __snake_case      :    Union[str, Any]  			=convert_diarization(a       ,		a       ,		a )
      elif arch.endswith('''ForXVector''' ):
            __snake_case      :    Dict  			=convert_xvector(a       ,		a       ,		a )
      else:
            raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )

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

      hf_feature_extractor.save_pretrained(a )
      hf_model.save_pretrained(a )


if __name__ == "__main__":
    UpperCamelCase_	:				Dict										= 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.""")
    UpperCamelCase_	:				str										= parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_	:				str										= {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Union[str, Any]										= ["""LayoutXLMTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

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


