'''simple docstring'''




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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

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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE_  ,       config=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Optional[int]     				=					downstream_dict['connector.weight']
     lowercase_					:       Optional[Any]     				=					downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
          lowercase_					:       List[str]     				=					downstream_dict[
              f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_					:       Dict     				=					downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     lowercase_					:       Union[str, Any]     				=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     lowercase_					:       List[str]     				=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     lowercase_					:       str     				=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     lowercase_					:       Optional[int]     				=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     lowercase_					:       List[str]     				=					downstream_dict['objective.W']
     return model

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Any     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)

     lowercase_					:       str     				=					checkpoint['Downstream']

     lowercase_					:       Dict     				=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_  ,       return_attention_mask=SCREAMING_SNAKE_CASE_  ,       do_normalize=SCREAMING_SNAKE_CASE_							)

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

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

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_							)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_							)


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)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

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




