

'''simple docstring'''
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase	 =			["""UtteranceLevel"""]


@torch.no_grad()
def       a__    (  _SCREAMING_SNAKE_CASE					:    int     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    Dict     ,							_SCREAMING_SNAKE_CASE					:    Any							)			-> Any:


	"""simple docstring"""





	UpperCAmelCase_			:	List[str]      =   torch.load(_SCREAMING_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}'''							)

	UpperCAmelCase_			:	Optional[int]      =   checkpoint["Downstream"]

	UpperCAmelCase_			:	Union[str, Any]      =   HubertConfig.from_pretrained(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	str      =   HubertForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE     ,							config=_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Tuple      =   WavaVecaFeatureExtractor.from_pretrained(
	    _SCREAMING_SNAKE_CASE     ,							return_attention_mask=_SCREAMING_SNAKE_CASE     ,							do_normalize=_SCREAMING_SNAKE_CASE							)

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

	UpperCAmelCase_			:	List[Any]      =   downstream_dict["projector.weight"]
	UpperCAmelCase_			:	List[str]      =   downstream_dict["projector.bias"]
	UpperCAmelCase_			:	str      =   downstream_dict["model.post_net.linear.weight"]
	UpperCAmelCase_			:	Dict      =   downstream_dict["model.post_net.linear.bias"]

	hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE							)
	hf_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					_lowerCamelCase	 =			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	 =			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_sentencepiece_available, is_tokenizers_available


__snake_case   :      Optional[int]						    =  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Optional[Any]						    =  ['MBart50Tokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Dict						    =  ['MBart50TokenizerFast']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  __snake_case   :      Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






