


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE :					int			=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :					List[Any]			=	["""UtteranceLevel"""]







@torch.no_grad()
def 						_lowercase       (     __lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Union[str, Any]			)  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Dict								=     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}'			)

     UpperCamelCase__							:  Optional[Any]								=     checkpoint['''Downstream''']

     UpperCamelCase__							:  Any								=     HubertConfig.from_pretrained(__lowerCamelCase			)
     UpperCamelCase__							:  Tuple								=     HubertForSequenceClassification.from_pretrained(__lowerCamelCase		,config=__lowerCamelCase			)
     UpperCamelCase__							:  Tuple								=     WavaVecaFeatureExtractor.from_pretrained(
         __lowerCamelCase		,return_attention_mask=__lowerCamelCase		,do_normalize=__lowerCamelCase			)

     if hf_congfig.use_weighted_layer_sum:
          UpperCamelCase__							:  Union[str, Any]								=     checkpoint['''Featurizer''']['''weights''']

     UpperCamelCase__							:  int								=     downstream_dict['''projector.weight''']
     UpperCamelCase__							:  str								=     downstream_dict['''projector.bias''']
     UpperCamelCase__							:  List[Any]								=     downstream_dict['''model.post_net.linear.weight''']
     UpperCamelCase__							:  List[str]								=     downstream_dict['''model.post_net.linear.bias''']

     hf_feature_extractor.save_pretrained(__lowerCamelCase			)
     hf_model.save_pretrained(__lowerCamelCase			)


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








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		*__lowerCamelCase					:   Optional[int],		**__lowerCamelCase					:   Dict   )  -> None:
           warnings.warn(
               '''The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
               ''' Please use MobileNetV1ImageProcessor instead.''',		__lowerCamelCase,		)
           super().__init__(*__lowerCamelCase,		**__lowerCamelCase   )




