

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCAmelCase      :    Dict						       =    logging.get_logger(__name__)

_lowerCAmelCase      :    Tuple						       =    ["UtteranceLevel"]






@torch.no_grad()
def  UpperCamelCase_(							_snake_case     :						Dict ,  _snake_case     :						Optional[int] ,  _snake_case     :						Optional[int] ,  _snake_case     :						List[Any]			):




			"""simple docstring"""






			__a						   =torch.load(_snake_case ,  map_location='cpu'			)
			if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
						raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'			)

			__a						   =checkpoint['Downstream']

			__a						   =HubertConfig.from_pretrained(_snake_case			)
			__a						   =HubertForSequenceClassification.from_pretrained(_snake_case ,  config=_snake_case			)
			__a						   =WavaVecaFeatureExtractor.from_pretrained(
			    _snake_case ,  return_attention_mask=_snake_case ,  do_normalize=_snake_case			)

			if hf_congfig.use_weighted_layer_sum:
						__a						   =checkpoint['Featurizer']['weights']

			__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']

			hf_feature_extractor.save_pretrained(_snake_case			)
			hf_model.save_pretrained(_snake_case			)


if __name__ == "__main__":
						_lowerCAmelCase      :    Dict						       =    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.")
						_lowerCAmelCase      :    Optional[int]						       =    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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCAmelCase      :    Union[str, Any]						       =    {
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    str						       =    ["ErnieMTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    Optional[Any]						       =    [
						    "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ErnieMForMultipleChoice",
						    "ErnieMForQuestionAnswering",
						    "ErnieMForSequenceClassification",
						    "ErnieMForTokenClassification",
						    "ErnieMModel",
						    "ErnieMPreTrainedModel",
						    "ErnieMForInformationExtraction",
						]


if TYPE_CHECKING:
						from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ernie_m import ErnieMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ernie_m import (
												    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ErnieMForInformationExtraction,
												    ErnieMForMultipleChoice,
												    ErnieMForQuestionAnswering,
												    ErnieMForSequenceClassification,
												    ErnieMForTokenClassification,
												    ErnieMModel,
												    ErnieMPreTrainedModel,
												)


else:
						import sys

						_lowerCAmelCase      :    Tuple						       =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






