

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase   :     Optional[Any]    							=     logging.get_logger(__name__)






def 			UpperCamelCase_(				_snake_case  :    List[str]			,	_snake_case  :    Optional[Any]			,	_snake_case  :    Optional[Any]      ):




			"""simple docstring"""






			__a     =WavaVecaForSequenceClassification.from_pretrained(lowerCAmelCase__			,	config=lowerCAmelCase__      )
			__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']
			return model






def 			UpperCamelCase_(				_snake_case  :    Optional[Any]			,	_snake_case  :    List[str]			,	_snake_case  :    List[str]      ):




			"""simple docstring"""






			__a     =WavaVecaForAudioFrameClassification.from_pretrained(lowerCAmelCase__			,	config=lowerCAmelCase__      )
			__a     =downstream_dict['model.linear.weight']
			__a     =downstream_dict['model.linear.bias']
			return model






def 			UpperCamelCase_(				_snake_case  :    Tuple			,	_snake_case  :    Optional[int]			,	_snake_case  :    Dict      ):




			"""simple docstring"""






			__a     =WavaVecaForXVector.from_pretrained(lowerCAmelCase__			,	config=lowerCAmelCase__      )
			__a     =downstream_dict['connector.weight']
			__a     =downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
						__a     =downstream_dict[
						    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
						]
						__a     =downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

			__a     =downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			__a     =downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			__a     =downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			__a     =downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			__a     =downstream_dict['objective.W']
			return model






@torch.no_grad()
def 			UpperCamelCase_(				_snake_case  :    List[str]			,	_snake_case  :    List[Any]			,	_snake_case  :    Optional[int]			,	_snake_case  :    str      ):




			"""simple docstring"""






			__a     =torch.load(lowerCAmelCase__			,	map_location='cpu'      )

			__a     =checkpoint['Downstream']

			__a     =WavaVecaConfig.from_pretrained(lowerCAmelCase__      )
			__a     =WavaVecaFeatureExtractor.from_pretrained(
			    lowerCAmelCase__			,	return_attention_mask=lowerCAmelCase__			,	do_normalize=lowerCAmelCase__      )

			__a     =hf_config.architectures[0]
			if arch.endswith('ForSequenceClassification'      ):
						__a     =convert_classification(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__      )
			elif arch.endswith('ForAudioFrameClassification'      ):
						__a     =convert_diarization(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__      )
			elif arch.endswith('ForXVector'      ):
						__a     =convert_xvector(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__      )
			else:
						raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'      )

			if hf_config.use_weighted_layer_sum:
						__a     =checkpoint['Featurizer']['weights']

			hf_feature_extractor.save_pretrained(lowerCAmelCase__      )
			hf_model.save_pretrained(lowerCAmelCase__      )


if __name__ == "__main__":
						_lowerCAmelCase   :     Tuple    							=     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   :     str    							=     parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











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


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

__snake_case     :int			       ={'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     'openai-gpt'
				A_   :     Any								=     {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  List[str]=40_478						, __UpperCamelCase      :  List[Any]=512						, __UpperCamelCase      :  List[Any]=768						, __UpperCamelCase      :  Optional[int]=12						, __UpperCamelCase      :  Dict=12						, __UpperCamelCase      :  List[str]="gelu"						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  List[str]=1e-5						, __UpperCamelCase      :  List[Any]=0.0_2						, __UpperCamelCase      :  str="cls_index"						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  int=None						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Tuple=0.1						, **__UpperCamelCase      :  List[Any]						, )		->     List[str]:
											A      						=    vocab_size
											A      						=    n_positions
											A      						=    n_embd
											A      						=    n_layer
											A      						=    n_head
											A      						=    afn
											A      						=    resid_pdrop
											A      						=    embd_pdrop
											A      						=    attn_pdrop
											A      						=    layer_norm_epsilon
											A      						=    initializer_range
											A      						=    summary_type
											A      						=    summary_use_proj
											A      						=    summary_activation
											A      						=    summary_first_dropout
											A      						=    summary_proj_to_labels
											super().__init__(**__UpperCamelCase       )