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  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Any					     =			'cpmant'

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





"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

   UpperCAmelCase_   :   Any     	=       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"    ):
      UpperCAmelCase_   :   Optional[int]     	=       convert_classification(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForAudioFrameClassification"    ):
      UpperCAmelCase_   :   Dict     	=       convert_diarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForXVector"    ):
      UpperCAmelCase_   :   List[Any]     	=       convert_xvector(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   else:
      raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""    )

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

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


if __name__ == "__main__":
    _a									=				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.')
    _a									=				parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


