"""simple docstring"""



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_UpperCamelCase         =		["UtteranceLevel"]
@torch.no_grad()
def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    str														= torch.load(lowerCAmelCase ,     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__					:    Tuple														= checkpoint["""Downstream"""]

							A__					:    Dict														= HubertConfig.from_pretrained(lowerCAmelCase       )
							A__					:    Union[str, Any]														= HubertForSequenceClassification.from_pretrained(lowerCAmelCase ,     config=lowerCAmelCase       )
							A__					:    str														= WavaVecaFeatureExtractor.from_pretrained(
							    lowerCAmelCase ,     return_attention_mask=lowerCAmelCase ,     do_normalize=lowerCAmelCase       )

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

							A__					:    int														= downstream_dict["""projector.weight"""]
							A__					:    int														= downstream_dict["""projector.bias"""]
							A__					:    List[Any]														= downstream_dict["""model.post_net.linear.weight"""]
							A__					:    List[Any]														= downstream_dict["""model.post_net.linear.bias"""]

							hf_feature_extractor.save_pretrained(lowerCAmelCase       )
							hf_model.save_pretrained(lowerCAmelCase       )


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"""



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_UpperCamelCase         =		set()
_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase = None       ):

							def decorator(lowerCAmelCase       ):
														global _emitted_deprecation_warnings

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    List[Any]														= deprecated_class_or_function.__init__
																					A__					:    Any														= deprecated_class_or_function.__name__
														else:
																					A__					:    Dict														= deprecated_class_or_function
																					A__					:    Optional[int]														= deprecated_function.__name__
																					# Support deprecating __init__ class method: class name instead
																					A__					:    List[str]														= name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

														A__					:    Union[str, Any]														= (
														    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
														    if help_message
														    else """"""
														)

														@wraps(lowerCAmelCase       )
														def wrapper(*lowerCAmelCase ,     **lowerCAmelCase       ):
																					A__					:    Tuple														= hash(lowerCAmelCase       )
																					if func_hash not in _emitted_deprecation_warnings:
																												warnings.warn(lowerCAmelCase ,     category=lowerCAmelCase ,     stacklevel=2       )
																												_emitted_deprecation_warnings.add(lowerCAmelCase       )
																					return deprecated_function(*lowerCAmelCase ,     **lowerCAmelCase       )

														A__					:    int														= """deprecated"""

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    Union[str, Any]														= wrapper
																					return deprecated_class_or_function
														else:
																					return wrapper

							return decorator
class 	__UpperCAmelCase						(enum.EnumMeta    ):







			'''simple docstring'''





			def __getattribute__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= super().__getattribute__(snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





			def __getitem__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= super().__getitem__(snake_case_       )
										if member._on_access:
																	member._on_access()
										return member





			def __call__( cls      ,			snake_case_      ,			snake_case_=None      ,			*, snake_case_=None      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=1       ):







										'''simple docstring'''


										A__					:    List[Any]														= super().__call__(snake_case_      ,			snake_case_      ,			module=snake_case_      ,			qualname=snake_case_      ,			type=snake_case_      ,			start=snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





class 	__UpperCAmelCase						(enum.Enum       ,      metaclass=__A    ):







			'''simple docstring'''





			def __new__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= object.__new__(cls       )
										A__					:    int														= value
										A__					:    Optional[int]														= member.deprecate
										return member





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return ""





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= F''' {self.help_message}''' if self.help_message else """"""
										warnings.warn(
										    F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
										    + help_message      ,			snake_case_      ,			stacklevel=3      ,			)





