




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase				:		Optional[int]     =   logging.get_logger(__name__)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       List[str]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    UniSpeechSatForSequenceClassification.from_pretrained(lowercase	,							config=lowercase     )
      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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Any	,							lowercase	:       List[Any]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    UniSpeechSatForAudioFrameClassification.from_pretrained(lowercase	,							config=lowercase     )
      lowerCamelCase_		     =    downstream_dict['model.linear.weight']
      lowerCamelCase_		     =    downstream_dict['model.linear.bias']
      return model

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       List[str]	,							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    UniSpeechSatForXVector.from_pretrained(lowercase	,							config=lowercase     )
      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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       Tuple	,							lowercase	:       List[Any]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    torch.load(lowercase	,							map_location='cpu'     )

      lowerCamelCase_		     =    checkpoint['Downstream']

      lowerCamelCase_		     =    UniSpeechSatConfig.from_pretrained(lowercase     )
      lowerCamelCase_		     =    WavaVecaFeatureExtractor.from_pretrained(
          lowercase	,							return_attention_mask=lowercase	,							do_normalize=lowercase     )

      lowerCamelCase_		     =    hf_config.architectures[0]
      if arch.endswith('ForSequenceClassification'     ):
            lowerCamelCase_		     =    convert_classification(lowercase	,							lowercase	,							lowercase     )
      elif arch.endswith('ForAudioFrameClassification'     ):
            lowerCamelCase_		     =    convert_diarization(lowercase	,							lowercase	,							lowercase     )
      elif arch.endswith('ForXVector'     ):
            lowerCamelCase_		     =    convert_xvector(lowercase	,							lowercase	,							lowercase     )
      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(lowercase     )
      hf_model.save_pretrained(lowercase     )


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Union[str, Any]     =   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Optional[Any]     =   ["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Dict     =   [
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   [
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		Optional[int]     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



