



import argparse

import torch

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


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

def       A_   (						__a  :					Tuple			,		__a  :					Optional[Any]			,		__a  :					Union[str, Any]		):

				"""simple docstring"""
				a__					   =		WavLMForSequenceClassification.from_pretrained(__a			,		config=__a		)
				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       A_   (						__a  :					str			,		__a  :					List[str]			,		__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		WavLMForAudioFrameClassification.from_pretrained(__a			,		config=__a		)
				a__					   =		downstream_dict["""model.linear.weight"""]
				a__					   =		downstream_dict["""model.linear.bias"""]
				return model

def       A_   (						__a  :					Dict			,		__a  :					int			,		__a  :					Optional[Any]		):

				"""simple docstring"""
				a__					   =		WavLMForXVector.from_pretrained(__a			,		config=__a		)
				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       A_   (						__a  :					int			,		__a  :					str			,		__a  :					List[Any]			,		__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		torch.load(__a			,		map_location="""cpu"""		)

				a__					   =		checkpoint["""Downstream"""]

				a__					   =		WavLMConfig.from_pretrained(__a		)
				a__					   =		WavaVecaFeatureExtractor.from_pretrained(
				    __a			,		return_attention_mask=__a			,		do_normalize=__a		)

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


if __name__ == "__main__":
	UpperCAmelCase							=       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							=       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


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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




