






"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase_	:				str										= logging.get_logger(__name__)



def     __lowercase					(    a :				Optional[int]       ,		a :				str       ,		a :				Optional[int] )					->					Union[str, Any]:
      __snake_case      :    str  			=WavaVecaForSequenceClassification.from_pretrained(a       ,		config=a )
      __snake_case      :    Union[str, Any]  			=downstream_dict['''projector.weight''']
      __snake_case      :    str  			=downstream_dict['''projector.bias''']
      __snake_case      :    int  			=downstream_dict['''model.post_net.linear.weight''']
      __snake_case      :    Union[str, Any]  			=downstream_dict['''model.post_net.linear.bias''']
      return model



def     __lowercase					(    a :				Optional[int]       ,		a :				Any       ,		a :				Dict )					->					Optional[int]:
      __snake_case      :    List[Any]  			=WavaVecaForAudioFrameClassification.from_pretrained(a       ,		config=a )
      __snake_case      :    Tuple  			=downstream_dict['''model.linear.weight''']
      __snake_case      :    List[str]  			=downstream_dict['''model.linear.bias''']
      return model



def     __lowercase					(    a :				Union[str, Any]       ,		a :				Optional[Any]       ,		a :				Optional[Any] )					->					Union[str, Any]:
      __snake_case      :    Union[str, Any]  			=WavaVecaForXVector.from_pretrained(a       ,		config=a )
      __snake_case      :    int  			=downstream_dict['''connector.weight''']
      __snake_case      :    Union[str, Any]  			=downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
            __snake_case      :    Any  			=downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            __snake_case      :    Union[str, Any]  			=downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      __snake_case      :    List[Any]  			=downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      __snake_case      :    Optional[int]  			=downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      __snake_case      :    List[str]  			=downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      __snake_case      :    Dict  			=downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      __snake_case      :    List[str]  			=downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def     __lowercase					(    a :				Union[str, Any]       ,		a :				Tuple       ,		a :				str       ,		a :				Dict )					->					Tuple:
      __snake_case      :    int  			=torch.load(a       ,		map_location='''cpu''' )

      __snake_case      :    str  			=checkpoint['''Downstream''']

      __snake_case      :    Any  			=WavaVecaConfig.from_pretrained(a )
      __snake_case      :    str  			=WavaVecaFeatureExtractor.from_pretrained(
          a       ,		return_attention_mask=a       ,		do_normalize=a )

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

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

      hf_feature_extractor.save_pretrained(a )
      hf_model.save_pretrained(a )


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







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_	:				Dict										= logging.get_logger(__name__)

UpperCamelCase_	:				Dict										= {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}


class      _lowercase (     lowerCAmelCase						):
      _a   :     Union[str, Any]								=   '''xmod'''


      def __init__(    self				:	Optional[int]							,   a				:	Dict=3_0_5_2_2							,   a				:	Any=7_6_8							,   a				:	str=1_2							,   a				:	Optional[int]=1_2							,   a				:	Any=3_0_7_2							,   a				:	int="gelu"							,   a				:	List[Any]=0.1							,   a				:	Union[str, Any]=0.1							,   a				:	Tuple=5_1_2							,   a				:	str=2							,   a				:	Any=0.0_2							,   a				:	Tuple=1e-12							,   a				:	Optional[int]=1							,   a				:	Any=0							,   a				:	str=2							,   a				:	str="absolute"							,   a				:	List[Any]=True							,   a				:	Optional[Any]=None							,   a				:	Tuple=False							,   a				:	Union[str, Any]=2							,   a				:	Any=False							,   a				:	Dict=True							,   a				:	int=True							,   a				:	str=("en_XX",)							,   a				:	Dict=None							,   **a				:	Tuple							,   ):





            """simple docstring"""







            super().__init__(pad_token_id=a							,   bos_token_id=a							,   eos_token_id=a							,   **a						)

            __snake_case      :    Union[str, Any]  			=vocab_size
            __snake_case      :    Union[str, Any]  			=hidden_size
            __snake_case      :    int  			=num_hidden_layers
            __snake_case      :    Union[str, Any]  			=num_attention_heads
            __snake_case      :    Any  			=hidden_act
            __snake_case      :    Tuple  			=intermediate_size
            __snake_case      :    int  			=hidden_dropout_prob
            __snake_case      :    str  			=attention_probs_dropout_prob
            __snake_case      :    int  			=max_position_embeddings
            __snake_case      :    Tuple  			=type_vocab_size
            __snake_case      :    Union[str, Any]  			=initializer_range
            __snake_case      :    Dict  			=layer_norm_eps
            __snake_case      :    Optional[int]  			=position_embedding_type
            __snake_case      :    int  			=use_cache
            __snake_case      :    int  			=classifier_dropout
            __snake_case      :    int  			=pre_norm
            __snake_case      :    Any  			=adapter_reduction_factor
            __snake_case      :    str  			=adapter_layer_norm
            __snake_case      :    Union[str, Any]  			=adapter_reuse_layer_norm
            __snake_case      :    List[str]  			=ln_before_adapter
            __snake_case      :    Optional[int]  			=list(a						)
            __snake_case      :    int  			=default_language



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            if self.task == "multiple-choice":
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
            else:
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''sequence'''}
            return OrderedDict(
                [
                    ('''input_ids''', dynamic_axis),
                    ('''attention_mask''', dynamic_axis),
                ]						)


