import argparse

import torch

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


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


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[Any]						,  lowercase_:		str						,  lowercase_:		Optional[Any]			):
       _UpperCamelCase       :    Dict					  =					WavLMForSequenceClassification.from_pretrained(lowercase_						,  config=lowercase_			)
       _UpperCamelCase       :    List[str]					  =					downstream_dict['projector.weight']
       _UpperCamelCase       :    Optional[int]					  =					downstream_dict['projector.bias']
       _UpperCamelCase       :    Optional[int]					  =					downstream_dict['model.post_net.linear.weight']
       _UpperCamelCase       :    Optional[Any]					  =					downstream_dict['model.post_net.linear.bias']
       return model


def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Union[str, Any]						,  lowercase_:		str			):
       _UpperCamelCase       :    List[Any]					  =					WavLMForAudioFrameClassification.from_pretrained(lowercase_						,  config=lowercase_			)
       _UpperCamelCase       :    str					  =					downstream_dict['model.linear.weight']
       _UpperCamelCase       :    Dict					  =					downstream_dict['model.linear.bias']
       return model


def SCREAMING_SNAKE_CASE			(       lowercase_:		Union[str, Any]						,  lowercase_:		Optional[int]						,  lowercase_:		Union[str, Any]			):
       _UpperCamelCase       :    List[str]					  =					WavLMForXVector.from_pretrained(lowercase_						,  config=lowercase_			)
       _UpperCamelCase       :    Union[str, Any]					  =					downstream_dict['connector.weight']
       _UpperCamelCase       :    Tuple					  =					downstream_dict['connector.bias']
       for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
              _UpperCamelCase       :    int					  =					downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              _UpperCamelCase       :    Tuple					  =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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


@torch.no_grad()
def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		List[str]						,  lowercase_:		Union[str, Any]						,  lowercase_:		Dict			):
       _UpperCamelCase       :    int					  =					torch.load(lowercase_						,  map_location='cpu'			)

       _UpperCamelCase       :    str					  =					checkpoint['Downstream']

       _UpperCamelCase       :    List[str]					  =					WavLMConfig.from_pretrained(lowercase_			)
       _UpperCamelCase       :    Tuple					  =					WavaVecaFeatureExtractor.from_pretrained(
           lowercase_						,  return_attention_mask=lowercase_						,  do_normalize=lowercase_			)

       _UpperCamelCase       :    Tuple					  =					hf_config.architectures[0]
       if arch.endswith('ForSequenceClassification'			):
              _UpperCamelCase       :    Optional[Any]					  =					convert_classification(lowercase_						,  lowercase_						,  lowercase_			)
       elif arch.endswith('ForAudioFrameClassification'			):
              _UpperCamelCase       :    Tuple					  =					convert_diarization(lowercase_						,  lowercase_						,  lowercase_			)
       elif arch.endswith('ForXVector'			):
              _UpperCamelCase       :    Any					  =					convert_xvector(lowercase_						,  lowercase_						,  lowercase_			)
       else:
              raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"			)

       if hf_config.use_weighted_layer_sum:
              _UpperCamelCase       :    Tuple					  =					checkpoint['Featurizer']['weights']

       hf_feature_extractor.save_pretrained(lowercase_			)
       hf_model.save_pretrained(lowercase_			)


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)




from typing import TYPE_CHECKING

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


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



