'''simple docstring'''


import argparse

import torch

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


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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      WavLMForSequenceClassification.from_pretrained(lowerCamelCase_	,     config=lowerCamelCase_   )
     UpperCAmelCase_							:   int   =      downstream_dict['projector.weight']
     UpperCAmelCase_							:   Tuple   =      downstream_dict['projector.bias']
     UpperCAmelCase_							:   Optional[Any]   =      downstream_dict['model.post_net.linear.weight']
     UpperCAmelCase_							:   Tuple   =      downstream_dict['model.post_net.linear.bias']
     return model


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[Any]	,     lowerCamelCase_					:							Optional[Any]	,     lowerCamelCase_					:							Any   ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      WavLMForAudioFrameClassification.from_pretrained(lowerCamelCase_	,     config=lowerCamelCase_   )
     UpperCAmelCase_							:   str   =      downstream_dict['model.linear.weight']
     UpperCAmelCase_							:   str   =      downstream_dict['model.linear.bias']
     return model


def 					_lowerCamelCase    (      lowerCamelCase_					:							int	,     lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   Union[str, Any]   =      WavLMForXVector.from_pretrained(lowerCamelCase_	,     config=lowerCamelCase_   )
     UpperCAmelCase_							:   int   =      downstream_dict['connector.weight']
     UpperCAmelCase_							:   Dict   =      downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          UpperCAmelCase_							:   List[str]   =      downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          UpperCAmelCase_							:   str   =      downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     UpperCAmelCase_							:   Union[str, Any]   =      downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     UpperCAmelCase_							:   Any   =      downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     UpperCAmelCase_							:   Tuple   =      downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     UpperCAmelCase_							:   Dict   =      downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     UpperCAmelCase_							:   int   =      downstream_dict['objective.W']
     return model


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							Tuple   ):



     """simple docstring"""





     UpperCAmelCase_							:   Any   =      torch.load(lowerCamelCase_	,     map_location='cpu'   )

     UpperCAmelCase_							:   str   =      checkpoint['Downstream']

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

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

     if hf_config.use_weighted_layer_sum:
          UpperCAmelCase_							:   List[Any]   =      checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(lowerCamelCase_   )
     hf_model.save_pretrained(lowerCamelCase_   )


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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case__     :			int      			=  {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			List[str]      			=  ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Dict      			=  [
     '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MarkupLMForQuestionAnswering''',
     '''MarkupLMForSequenceClassification''',
     '''MarkupLMForTokenClassification''',
     '''MarkupLMModel''',
     '''MarkupLMPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 snake_case__     :			Optional[Any]      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)






