

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a:      Optional[int]								=     logging.get_logger(__name__)

_a:      int								=     ["""UtteranceLevel"""]



@torch.no_grad()
def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     torch.load(A    ,       map_location="cpu"      )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"      )

	UpperCAmelCase_    			=     checkpoint["Downstream"]

	UpperCAmelCase_    			=     HubertConfig.from_pretrained(A      )
	UpperCAmelCase_    			=     HubertForSequenceClassification.from_pretrained(A    ,       config=A      )
	UpperCAmelCase_    			=     WavaVecaFeatureExtractor.from_pretrained(
	    A    ,       return_attention_mask=A    ,       do_normalize=A      )

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

	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"]

	hf_feature_extractor.save_pretrained(A      )
	hf_model.save_pretrained(A      )


if __name__ == "__main__":
				_a:      int								=     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:      Optional[Any]								=     parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


from typing import TYPE_CHECKING

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


_a:      Dict								=     {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

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