





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 ={
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       '''cpmant'''






				def __init__(					self						:       List[str],					lowerCAmelCase__						:       int = 3_0_7_2_0,					lowerCAmelCase__						:       int = 4_0_9_6,					lowerCAmelCase__						:       int = 3_2,					lowerCAmelCase__						:       int = 1_2_8,					lowerCAmelCase__						:       int = 1_0_2_4_0,					lowerCAmelCase__						:       int = 4_8,					lowerCAmelCase__						:       int = 0.0,					lowerCAmelCase__						:       int = 5_1_2,					lowerCAmelCase__						:       int = 2_0_4_8,					lowerCAmelCase__						:       int = 1e-6,					lowerCAmelCase__						:       float = 1.0,					lowerCAmelCase__						:       int = 3_2,					lowerCAmelCase__						:       int = 3_2,					lowerCAmelCase__						:       int = 3_2,					lowerCAmelCase__						:       bool = True,					**lowerCAmelCase__						:       Optional[Any],					)      -> Tuple:




										'''simple docstring'''



										super().__init__(**lowerCAmelCase__				)
										_UpperCamelCase     :   Optional[Any]      =       prompt_types
										_UpperCamelCase     :   Union[str, Any]      =       prompt_length
										_UpperCamelCase     :   str      =       segment_types
										_UpperCamelCase     :   List[str]      =       hidden_size
										_UpperCamelCase     :   List[str]      =       num_attention_heads
										_UpperCamelCase     :   Dict      =       dim_head
										_UpperCamelCase     :   Any      =       dim_ff
										_UpperCamelCase     :   int      =       num_hidden_layers
										_UpperCamelCase     :   List[Any]      =       position_bias_num_buckets
										_UpperCamelCase     :   List[Any]      =       position_bias_max_distance
										_UpperCamelCase     :   Any      =       dropout_p
										_UpperCamelCase     :   Optional[int]      =       eps
										_UpperCamelCase     :   int      =       use_cache
										_UpperCamelCase     :   Dict      =       vocab_size
										_UpperCamelCase     :   Optional[Any]      =       init_std










'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      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(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	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(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						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.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

