


'''simple docstring'''
import argparse

import torch

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


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase_						,  config=UpperCamelCase_ )
							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       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase_						,  config=UpperCamelCase_ )
							A_			    =		downstream_dict["model.linear.weight"]
							A_			    =		downstream_dict["model.linear.bias"]
							return model







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		WavaVecaForXVector.from_pretrained(UpperCamelCase_						,  config=UpperCamelCase_ )
							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       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )

							A_			    =		checkpoint["Downstream"]

							A_			    =		WavaVecaConfig.from_pretrained(UpperCamelCase_ )
							A_			    =		WavaVecaFeatureExtractor.from_pretrained(
							    UpperCamelCase_						,  return_attention_mask=UpperCamelCase_						,  do_normalize=UpperCamelCase_ )

							A_			    =		hf_config.architectures[0]
							if arch.endswith("ForSequenceClassification" ):
														A_			    =		convert_classification(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
							elif arch.endswith("ForAudioFrameClassification" ):
														A_			    =		convert_diarization(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
							elif arch.endswith("ForXVector" ):
														A_			    =		convert_xvector(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
							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(UpperCamelCase_ )
							hf_model.save_pretrained(UpperCamelCase_ )


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









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		{
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[Any]   						="transfo-xl"
						_UpperCAmelCase		:       str   						=["mems"]
						_UpperCAmelCase		:       Optional[int]   						={
						    "n_token": "vocab_size",
						    "hidden_size": "d_model",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}






						def __init__(  self							:      str ,  lowerCAmelCase							:      Tuple=26_77_35 ,  lowerCAmelCase							:      List[Any]=[2_00_00, 4_00_00, 20_00_00] ,  lowerCAmelCase							:      Dict=10_24 ,  lowerCAmelCase							:      List[str]=10_24 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      Optional[Any]=64 ,  lowerCAmelCase							:      str=40_96 ,  lowerCAmelCase							:      Optional[int]=4 ,  lowerCAmelCase							:      Any=False ,  lowerCAmelCase							:      List[str]=18 ,  lowerCAmelCase							:      Any=16_00 ,  lowerCAmelCase							:      List[str]=10_00 ,  lowerCAmelCase							:      int=True ,  lowerCAmelCase							:      List[str]=True ,  lowerCAmelCase							:      List[str]=0 ,  lowerCAmelCase							:      Tuple=-1 ,  lowerCAmelCase							:      Optional[int]=True ,  lowerCAmelCase							:      List[str]=0.1 ,  lowerCAmelCase							:      int=0.0 ,  lowerCAmelCase							:      Tuple=True ,  lowerCAmelCase							:      List[str]="normal" ,  lowerCAmelCase							:      Tuple=0.0_1 ,  lowerCAmelCase							:      Optional[int]=0.0_1 ,  lowerCAmelCase							:      List[Any]=0.0_2 ,  lowerCAmelCase							:      Dict=1e-5 ,  lowerCAmelCase							:      Optional[int]=0 ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		vocab_size
													A_			    =		[]
													self.cutoffs.extend(lowerCAmelCase	)
													if proj_share_all_but_first:
																				A_			    =		[False] + [True] * len(self.cutoffs	)
													else:
																				A_			    =		[False] + [False] * len(self.cutoffs	)
													A_			    =		d_model
													A_			    =		d_embed
													A_			    =		d_head
													A_			    =		d_inner
													A_			    =		div_val
													A_			    =		pre_lnorm
													A_			    =		n_layer
													A_			    =		n_head
													A_			    =		mem_len
													A_			    =		same_length
													A_			    =		attn_type
													A_			    =		clamp_len
													A_			    =		sample_softmax
													A_			    =		adaptive
													A_			    =		dropout
													A_			    =		dropatt
													A_			    =		untie_r
													A_			    =		init
													A_			    =		init_range
													A_			    =		proj_init_std
													A_			    =		init_std
													A_			    =		layer_norm_epsilon
													super().__init__(eos_token_id=lowerCAmelCase ,  **lowerCAmelCase	)






						@property
						def 				_UpperCAmelCase       (  self							:      str	):
													# Message copied from Transformer-XL documentation
													logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."	)
													return -1






						@max_position_embeddings.setter
						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Dict	):
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    F"The model {self.model_type} is one of the few models that has no sequence length limit."	)





