'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					Any  =logging.get_logger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			int ,   snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       WavaVecaForSequenceClassification.from_pretrained(lowercase__ ,   config=lowercase__      )
     UpperCAmelCase_	   =       downstream_dict["""projector.weight"""]
     UpperCAmelCase_	   =       downstream_dict["""projector.bias"""]
     UpperCAmelCase_	   =       downstream_dict["""model.post_net.linear.weight"""]
     UpperCAmelCase_	   =       downstream_dict["""model.post_net.linear.bias"""]
     return model





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			Dict ,   snake_case_	:			Dict      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       WavaVecaForAudioFrameClassification.from_pretrained(lowercase__ ,   config=lowercase__      )
     UpperCAmelCase_	   =       downstream_dict["""model.linear.weight"""]
     UpperCAmelCase_	   =       downstream_dict["""model.linear.bias"""]
     return model





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			Optional[Any] ,   snake_case_	:			str      )   ->				Tuple:


     '''simple docstring'''




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

     UpperCAmelCase_	   =       downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
     UpperCAmelCase_	   =       downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
     UpperCAmelCase_	   =       downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
     UpperCAmelCase_	   =       downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
     UpperCAmelCase_	   =       downstream_dict["""objective.W"""]
     return model





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			Any ,   snake_case_	:			List[str] ,   snake_case_	:			List[Any]      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       torch.load(lowercase__ ,   map_location="cpu"      )

     UpperCAmelCase_	   =       checkpoint["""Downstream"""]

     UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(lowercase__      )
     UpperCAmelCase_	   =       WavaVecaFeatureExtractor.from_pretrained(
         lowercase__ ,   return_attention_mask=lowercase__ ,   do_normalize=lowercase__      )

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

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

     hf_feature_extractor.save_pretrained(lowercase__      )
     hf_model.save_pretrained(lowercase__      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[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.')
    SCREAMING_SNAKE_CASE_:					List[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_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




