






import argparse

import torch

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


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







def       lowercase__	(					A_:						List[Any]  ,      A_:						List[Any]  ,      A_:						Dict		)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				=WavaVecaForSequenceClassification.from_pretrained(A_  ,      config=A_		)
       __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       lowercase__	(					A_:						Any  ,      A_:						List[str]  ,      A_:						Any		)	->				Tuple:







       """simple docstring"""






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







def       lowercase__	(					A_:						List[str]  ,      A_:						Optional[int]  ,      A_:						List[Any]		)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=WavaVecaForXVector.from_pretrained(A_  ,      config=A_		)
       __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       lowercase__	(					A_:						List[str]  ,      A_:						List[str]  ,      A_:						Any  ,      A_:						List[str]		)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=torch.load(A_  ,      map_location="""cpu"""		)

       __UpperCAmelCase     				=checkpoint["""Downstream"""]

       __UpperCAmelCase     				=WavaVecaConfig.from_pretrained(A_		)
       __UpperCAmelCase     				=WavaVecaFeatureExtractor.from_pretrained(
           A_  ,      return_attention_mask=A_  ,      do_normalize=A_		)

       __UpperCAmelCase     				=hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""		):
              __UpperCAmelCase     				=convert_classification(A_  ,      A_  ,      A_		)
       elif arch.endswith("""ForAudioFrameClassification"""		):
              __UpperCAmelCase     				=convert_diarization(A_  ,      A_  ,      A_		)
       elif arch.endswith("""ForXVector"""		):
              __UpperCAmelCase     				=convert_xvector(A_  ,      A_  ,      A_		)
       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(A_		)
       hf_model.save_pretrained(A_		)


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)








"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




