




'''simple docstring'''






import argparse

import torch

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


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

def      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     int										=  WavLMForSequenceClassification.from_pretrained(snake_case__   , config=snake_case__		)
			__UpperCamelCase :     Union[str, Any]										=  downstream_dict["projector.weight"]
			__UpperCamelCase :     List[str]										=  downstream_dict["projector.bias"]
			__UpperCamelCase :     List[Any]										=  downstream_dict["model.post_net.linear.weight"]
			__UpperCamelCase :     Tuple										=  downstream_dict["model.post_net.linear.bias"]
			return model

def      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     Dict										=  WavLMForAudioFrameClassification.from_pretrained(snake_case__   , config=snake_case__		)
			__UpperCamelCase :     Dict										=  downstream_dict["model.linear.weight"]
			__UpperCamelCase :     Tuple										=  downstream_dict["model.linear.bias"]
			return model

def      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     List[Any]										=  WavLMForXVector.from_pretrained(snake_case__   , config=snake_case__		)
			__UpperCamelCase :     List[str]										=  downstream_dict["connector.weight"]
			__UpperCamelCase :     Union[str, Any]										=  downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
						__UpperCamelCase :     Optional[int]										=  downstream_dict[
						    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						__UpperCamelCase :     List[str]										=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			__UpperCamelCase :     str										=  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
			__UpperCamelCase :     Tuple										=  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
			__UpperCamelCase :     List[Any]										=  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
			__UpperCamelCase :     int										=  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
			__UpperCamelCase :     Optional[int]										=  downstream_dict["objective.W"]
			return model

@torch.no_grad()
def      __lowerCAmelCase   (       snake_case__   , snake_case__   , snake_case__   , snake_case__		):
			__UpperCamelCase :     Optional[Any]										=  torch.load(snake_case__   , map_location="cpu"		)

			__UpperCamelCase :     int										=  checkpoint["Downstream"]

			__UpperCamelCase :     Optional[Any]										=  WavLMConfig.from_pretrained(snake_case__		)
			__UpperCamelCase :     Any										=  WavaVecaFeatureExtractor.from_pretrained(
			    snake_case__   , return_attention_mask=snake_case__   , do_normalize=snake_case__		)

			__UpperCamelCase :     int										=  hf_config.architectures[0]
			if arch.endswith("ForSequenceClassification"		):
						__UpperCamelCase :     Tuple										=  convert_classification(snake_case__   , snake_case__   , snake_case__		)
			elif arch.endswith("ForAudioFrameClassification"		):
						__UpperCamelCase :     List[Any]										=  convert_diarization(snake_case__   , snake_case__   , snake_case__		)
			elif arch.endswith("ForXVector"		):
						__UpperCamelCase :     List[str]										=  convert_xvector(snake_case__   , snake_case__   , snake_case__		)
			else:
						raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"		)

			if hf_config.use_weighted_layer_sum:
						__UpperCamelCase :     List[str]										=  checkpoint["Featurizer"]["weights"]

			hf_feature_extractor.save_pretrained(snake_case__		)
			hf_model.save_pretrained(snake_case__		)


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









'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



