






import argparse

import torch

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


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

   lowercase__						:	Union[str, Any]         = hf_config.architectures[0]
   if arch.endswith("""ForSequenceClassification"""					):
      lowercase__						:	Union[str, Any]         = convert_classification(A		,				A		,				A					)
   elif arch.endswith("""ForAudioFrameClassification"""					):
      lowercase__						:	Dict         = convert_diarization(A		,				A		,				A					)
   elif arch.endswith("""ForXVector"""					):
      lowercase__						:	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:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


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










from collections import OrderedDict
from typing import Mapping

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


a    =       logging.get_logger(__name__)

a    =       {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}
class        __snake_case(					__UpperCamelCase      ):
 lowercase :       Dict		      =		'''ernie'''



 def __init__(	self   :   Tuple ,		UpperCAmelCase__   :   int=30_522 ,		UpperCAmelCase__   :   List[str]=768 ,		UpperCAmelCase__   :   Tuple=12 ,		UpperCAmelCase__   :   Optional[int]=12 ,		UpperCAmelCase__   :   Any=3_072 ,		UpperCAmelCase__   :   Tuple="gelu" ,		UpperCAmelCase__   :   Union[str, Any]=0.1 ,		UpperCAmelCase__   :   str=0.1 ,		UpperCAmelCase__   :   List[str]=512 ,		UpperCAmelCase__   :   str=2 ,		UpperCAmelCase__   :   Optional[int]=3 ,		UpperCAmelCase__   :   Dict=False ,		UpperCAmelCase__   :   Any=0.02 ,		UpperCAmelCase__   :   Any=1e-12 ,		UpperCAmelCase__   :   str=0 ,		UpperCAmelCase__   :   Any="absolute" ,		UpperCAmelCase__   :   List[str]=True ,		UpperCAmelCase__   :   Tuple=None ,		**UpperCAmelCase__   :   Dict ,		):

    """simple docstring"""






    super().__init__(pad_token_id=UpperCAmelCase__ ,		**UpperCAmelCase__	)

    lowercase__						:	List[str]         = vocab_size
    lowercase__						:	Optional[int]         = hidden_size
    lowercase__						:	Union[str, Any]         = num_hidden_layers
    lowercase__						:	Any         = num_attention_heads
    lowercase__						:	Optional[int]         = hidden_act
    lowercase__						:	str         = intermediate_size
    lowercase__						:	Tuple         = hidden_dropout_prob
    lowercase__						:	Tuple         = attention_probs_dropout_prob
    lowercase__						:	str         = max_position_embeddings
    lowercase__						:	List[Any]         = type_vocab_size
    lowercase__						:	Any         = task_type_vocab_size
    lowercase__						:	Optional[Any]         = use_task_id
    lowercase__						:	Optional[Any]         = initializer_range
    lowercase__						:	Union[str, Any]         = layer_norm_eps
    lowercase__						:	Dict         = position_embedding_type
    lowercase__						:	Union[str, Any]         = use_cache
    lowercase__						:	int         = classifier_dropout
class        __snake_case(					__UpperCamelCase      ):



 @property
 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






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


