




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_   							=      logging.get_logger(__name__)

UpperCAmelCase_   							=      {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class        lowerCamelCase__(   __lowerCamelCase):
      UpperCAmelCase__ :      List[str]       						=  'flaubert'
      UpperCAmelCase__ :      Dict       						=  {
          'hidden_size': 'emb_dim',
          'num_attention_heads': 'n_heads',
          'num_hidden_layers': 'n_layers',
          'n_words': 'vocab_size',  # For backward compatibility
      }
      def __init__(							self:					Union[str, Any]       ,			UpperCamelCase_:					int=False       ,			UpperCamelCase_:					Dict=0.0       ,			UpperCamelCase_:					str=3_01_45       ,			UpperCamelCase_:					List[str]=20_48       ,			UpperCamelCase_:					List[Any]=12       ,			UpperCamelCase_:					Any=16       ,			UpperCamelCase_:					Dict=0.1       ,			UpperCamelCase_:					List[str]=0.1       ,			UpperCamelCase_:					List[str]=True       ,			UpperCamelCase_:					str=False       ,			UpperCamelCase_:					Optional[int]=False       ,			UpperCamelCase_:					str=False       ,			UpperCamelCase_:					List[str]=1       ,			UpperCamelCase_:					List[Any]=True       ,			UpperCamelCase_:					List[Any]=5_12       ,			UpperCamelCase_:					str=20_48**-0.5       ,			UpperCamelCase_:					Optional[int]=1E-12       ,			UpperCamelCase_:					List[str]=0.02       ,			UpperCamelCase_:					List[str]=0       ,			UpperCamelCase_:					Any=1       ,			UpperCamelCase_:					List[str]=2       ,			UpperCamelCase_:					List[str]=3       ,			UpperCamelCase_:					str=5       ,			UpperCamelCase_:					Optional[int]=True       ,			UpperCamelCase_:					Dict="first"       ,			UpperCamelCase_:					int=True       ,			UpperCamelCase_:					str=None       ,			UpperCamelCase_:					Optional[Any]=True       ,			UpperCamelCase_:					List[str]=0.1       ,			UpperCamelCase_:					List[str]=5       ,			UpperCamelCase_:					List[Any]=5       ,			UpperCamelCase_:					Dict=0       ,			UpperCamelCase_:					List[str]=0       ,			UpperCamelCase_:					Tuple=2       ,			UpperCamelCase_:					Optional[Any]=0       ,			**UpperCamelCase_:					Dict       ,			):
             __lowerCamelCase  =		pre_norm
             __lowerCamelCase  =		layerdrop
             __lowerCamelCase  =		vocab_size
             __lowerCamelCase  =		emb_dim
             __lowerCamelCase  =		n_layers
             __lowerCamelCase  =		n_heads
             __lowerCamelCase  =		dropout
             __lowerCamelCase  =		attention_dropout
             __lowerCamelCase  =		gelu_activation
             __lowerCamelCase  =		sinusoidal_embeddings
             __lowerCamelCase  =		causal
             __lowerCamelCase  =		asm
             __lowerCamelCase  =		n_langs
             __lowerCamelCase  =		use_lang_emb
             __lowerCamelCase  =		layer_norm_eps
             __lowerCamelCase  =		bos_index
             __lowerCamelCase  =		eos_index
             __lowerCamelCase  =		pad_index
             __lowerCamelCase  =		unk_index
             __lowerCamelCase  =		mask_index
             __lowerCamelCase  =		is_encoder
             __lowerCamelCase  =		max_position_embeddings
             __lowerCamelCase  =		embed_init_std
             __lowerCamelCase  =		init_std
             __lowerCamelCase  =		summary_type
             __lowerCamelCase  =		summary_use_proj
             __lowerCamelCase  =		summary_activation
             __lowerCamelCase  =		summary_proj_to_labels
             __lowerCamelCase  =		summary_first_dropout
             __lowerCamelCase  =		start_n_top
             __lowerCamelCase  =		end_n_top
             __lowerCamelCase  =		mask_token_id
             __lowerCamelCase  =		lang_id

             if "n_words" in kwargs:
                    __lowerCamelCase  =		kwargs["""n_words"""]

             super().__init__(pad_token_id=UpperCamelCase_       ,			bos_token_id=UpperCamelCase_       ,			**UpperCamelCase_  )





class        lowerCamelCase__(   __lowerCamelCase):
      @property
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             if self.task == "multiple-choice":
                    __lowerCamelCase  =		{0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    __lowerCamelCase  =		{0: """batch""", 1: """sequence"""}
             return OrderedDict(
                 [
                     ("""input_ids""", dynamic_axis),
                     ("""attention_mask""", dynamic_axis),
                 ]  )







import argparse

import torch

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


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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForXVector.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""connector.weight"""]
       __lowerCamelCase  =		downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
              __lowerCamelCase  =		downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase  =		downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase  =		downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

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

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase  =		checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


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

