

"""simple docstring"""



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput






@dataclass
class 				snake_case__     (   _a   ):
 _snake_case   :			List[Any]           =		None
 _snake_case   :			List[str]           =		None
 _snake_case   :			Optional[Any]           =		None
 _snake_case   :			Tuple           =		None






class 				snake_case__     (   _a   ):


 def __init__(				self  ,						lowerCamelCase=1  ,						lowerCamelCase=0  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase="cls"  ,						lowerCamelCase=False  ,						lowerCamelCase=True  ,						**lowerCamelCase  ,						):
        super().__init__(pad_token_id=_a  ,						bos_token_id=_a  ,						eos_token_id=_a  ,						**_a		)
        __a     				=  project_dim
        __a     				=  pooler_fn
        __a     				=  learn_encoder
        __a     				=  use_attention_mask






class 				snake_case__     (   _a   ):
 _snake_case   :			Union[str, Any]           =		[r"""pooler""", r"""logit_scale"""]
 _snake_case   :			Dict           =		[r"""position_ids""", r"""predictions.decoder.bias"""]
 _snake_case   :			str           =		"""roberta"""
 _snake_case   :			List[Any]           =		RobertaSeriesConfig


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(_a		)
        __a     				=  XLMRobertaModel(_a		)
        __a     				=  nn.Linear(config.hidden_size  ,						config.project_dim		)
        __a     				=  getattr(_a  ,						"has_pre_transformation"  ,						_a		)
        if self.has_pre_transformation:
               __a     				=  nn.Linear(config.hidden_size  ,						config.project_dim		)
               __a     				=  nn.LayerNorm(config.hidden_size  ,						eps=config.layer_norm_eps		)
        self.post_init()






 def 		a__ (				self  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						):
        __a     				=  return_dict if return_dict is not None else self.config.use_return_dict

        __a     				=  self.base_model(
            input_ids=_a  ,						attention_mask=_a  ,						token_type_ids=_a  ,						position_ids=_a  ,						head_mask=_a  ,						inputs_embeds=_a  ,						encoder_hidden_states=_a  ,						encoder_attention_mask=_a  ,						output_attentions=_a  ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states  ,						return_dict=_a  ,						)

        if self.has_pre_transformation:
               __a     				=  outputs["hidden_states"][-2]
               __a     				=  self.pre_LN(_a		)
               __a     				=  self.transformation_pre(_a		)

               return TransformationModelOutput(
                   projection_state=_a  ,						last_hidden_state=outputs.last_hidden_state  ,						hidden_states=outputs.hidden_states  ,						attentions=outputs.attentions  ,						)
        else:
               __a     				=  self.transformation(outputs.last_hidden_state		)
               return TransformationModelOutput(
                   projection_state=_a  ,						last_hidden_state=outputs.last_hidden_state  ,						hidden_states=outputs.hidden_states  ,						attentions=outputs.attentions  ,						)

"""simple docstring"""



import argparse

import torch

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


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



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __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 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __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 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]			=	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__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
