

"""simple docstring"""



import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")



def 					_lowerCamelCase(					a							):
       __a     				=  test_file.split(os.path.sep							)
       if components[0:2] != ["tests", "models"]:
              raise ValueError(
                  "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
                  F"{test_file} instead."							)
       __a     				=  components[-1]
       if not test_fn.endswith("py"							):
              raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."							)
       if not test_fn.startswith("test_modeling_"							):
              raise ValueError(
                  F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."							)

       __a     				=  components[:-1] + [test_fn.replace(".py" ,  ""							)]
       __a     				=  ".".join(a							)

       return test_module_path



def 					_lowerCamelCase(					a							):
       __a     				=  get_module_path(a							)
       __a     				=  importlib.import_module(a							)

       return test_module



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  get_test_module(a							)
       for attr in dir(a							):
              if attr.endswith("ModelTester"							):
                     tester_classes.append(getattr(a ,  a							)							)

    # sort with class names
       return sorted(a ,  key=lambda a							: x.__name__							)



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  get_test_module(a							)
       for attr in dir(a							):
              __a     				=  getattr(a ,  a							)
              # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
              # `all_model_classes` is not empty (which also excludes other special classes).
              __a     				=  getattr(a ,  "all_model_classes" ,  []							)
              if len(a							) > 0:
                     test_classes.append(a							)

    # sort with class names
       return sorted(a ,  key=lambda a							: x.__name__							)



def 					_lowerCamelCase(					a							):
       __a     				=  get_test_classes(a							)
       __a     				=  set()
       for test_class in test_classes:
              model_classes.update(test_class.all_model_classes							)

       # sort with class names
       return sorted(a ,  key=lambda a							: x.__name__							)



def 					_lowerCamelCase(					a							):
       __a     				=  test_class()
       if hasattr(a ,  "setUp"							):
              test.setUp()

       __a     				=  None
       if hasattr(a ,  "model_tester"							):
              # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
              if test.model_tester is not None:
                     __a     				=  test.model_tester.__class__

       return model_tester



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  get_test_classes(a							)

       __a     				=  []
       for test_class in test_classes:
              if model_class in test_class.all_model_classes:
                     target_test_classes.append(a							)

    # sort with class names
       return sorted(a ,  key=lambda a							: x.__name__							)



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  get_test_classes_for_model(a ,  a							)

       __a     				=  []
       for test_class in test_classes:
              __a     				=  get_model_tester_from_test_class(a							)
              if tester_class is not None:
                     tester_classes.append(a							)

    # sort with class names
       return sorted(a ,  key=lambda a							: x.__name__							)



def 					_lowerCamelCase(					a							):
       __a     				=  get_test_classes(a							)
       __a     				=  {test_class: get_model_tester_from_test_class(a							) for test_class in test_classes}
       return test_tester_mapping



def 					_lowerCamelCase(					a							):
       __a     				=  get_model_classes(a							)
       __a     				=  {
           model_class: get_test_classes_for_model(a ,  a							) for model_class in model_classes
       }
       return model_test_mapping



def 					_lowerCamelCase(					a							):
       __a     				=  get_model_classes(a							)
       __a     				=  {
           model_class: get_tester_classes_for_model(a ,  a							) for model_class in model_classes
       }
       return model_to_tester_mapping




def 					_lowerCamelCase(					a							):
       if isinstance(a ,  a							):
              return o
       elif isinstance(a ,  a							):
              return o.__name__
       elif isinstance(a ,  (list, tuple)							):
              return [to_json(a							) for x in o]
       elif isinstance(a ,  a							):
              return {to_json(a							): to_json(a							) for k, v in o.items()}
       else:
              return o

"""simple docstring"""



import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Dict			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["projector.weight"]
       __a     				=  downstream_dict["projector.bias"]
       __a     				=  downstream_dict["model.post_net.linear.weight"]
       __a     				=  downstream_dict["model.post_net.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["connector.weight"]
       __a     				=  downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
              __a     				=  downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              __a     				=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       __a     				=  downstream_dict["objective.W"]
       return model



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

       __a     				=  WavaVecaConfig.from_pretrained(a							)
       __a     				=  WavaVecaFeatureExtractor.from_pretrained(
           a ,  return_attention_mask=a ,  do_normalize=a							)

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

       if hf_config.use_weighted_layer_sum:
              __a     				=  checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(a							)
       hf_model.save_pretrained(a							)


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