
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a									=				{
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				['FlavaFeatureExtractor']
    _a									=				['FlavaImageProcessor']
    _a									=				['FlavaProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'FlavaForPreTraining',
        'FlavaImageCodebook',
        'FlavaImageModel',
        'FlavaModel',
        'FlavaMultimodalModel',
        'FlavaPreTrainedModel',
        'FlavaTextModel',
    ]

if TYPE_CHECKING:
    from .configuration_flava import (
        FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
        FlavaConfig,
        FlavaImageCodebookConfig,
        FlavaImageConfig,
        FlavaMultimodalConfig,
        FlavaTextConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_flava import FlavaFeatureExtractor
        from .image_processing_flava import FlavaImageProcessor
        from .processing_flava import FlavaProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flava import (
            FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
            FlavaForPreTraining,
            FlavaImageCodebook,
            FlavaImageModel,
            FlavaModel,
            FlavaMultimodalModel,
            FlavaPreTrainedModel,
            FlavaTextModel,
        )

else:
    import sys

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




"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

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

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__lowerCamelCase    )
   hf_model.save_pretrained(__lowerCamelCase    )


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


