"""simple docstring"""



from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__:Any			=	TypeVar("""KEY""")
SCREAMING_SNAKE_CASE__:Tuple			=	TypeVar("""VAL""")






@dataclass(frozen=snake_case_,		slots=snake_case_   )
class 				snake_case__     (   Generic[KEY, VAL]   ):
 _snake_case   :			KEY
 _snake_case   :			VAL






class 				snake_case__     (   _Item   ):


 def __init__(				self		):
        super().__init__(lowerCamelCase  ,						lowerCamelCase		)






 def __bool__(				self		):
        return False
SCREAMING_SNAKE_CASE__:Any			=	_DeletedItem()






class 				snake_case__     (   MutableMapping[KEY, VAL]   ):


 def __init__(				self  ,						lowerCamelCase = 8  ,						lowerCamelCase = 0.75		):
        __a     				=  initial_block_size
        __a     				=  [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        __a     				=  capacity_factor
        __a     				=  0


 def 		a__ (				self  ,						lowerCamelCase		):
        return hash(lowerCamelCase		) % len(self._buckets		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return (ind + 1) % len(self._buckets		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self._buckets[ind]
        if not stored:
               __a     				=  _Item(lowerCamelCase  ,						lowerCamelCase		)
               self._len += 1
               return True
        elif stored.key == key:
               __a     				=  _Item(lowerCamelCase  ,						lowerCamelCase		)
               return True
        else:
               return False


 def 		a__ (				self		):
        __a     				=  len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase		)


 def 		a__ (				self		):
        if len(self._buckets		) <= self._initial_block_size:
               return False
        __a     				=  len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self._buckets
        __a     				=  [None] * new_size
        __a     				=  0
        for item in old_buckets:
               if item:
                      self._add_item(item.key  ,						item.val		)


 def 		a__ (				self		):
        self._resize(len(self._buckets		) * 2		)


 def 		a__ (				self		):
        self._resize(len(self._buckets		) // 2		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self._get_bucket_index(lowerCamelCase		)
        for _ in range(len(self._buckets		)		):
               yield ind
               __a     				=  self._get_next_ind(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        for ind in self._iterate_buckets(lowerCamelCase		):
               if self._try_set(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
                      break


 def __setitem__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        if self._is_full():
               self._size_up()

        self._add_item(lowerCamelCase  ,						lowerCamelCase		)


 def __delitem__(				self  ,						lowerCamelCase		):
        for ind in self._iterate_buckets(lowerCamelCase		):
               __a     				=  self._buckets[ind]
               if item is None:
                      raise KeyError(lowerCamelCase		)
               if item is _deleted:
                      continue
               if item.key == key:
                      __a     				=  _deleted
                      self._len -= 1
                      break
        if self._is_sparse():
               self._size_down()


 def __getitem__(				self  ,						lowerCamelCase		):
        for ind in self._iterate_buckets(lowerCamelCase		):
               __a     				=  self._buckets[ind]
               if item is None:
                      break
               if item is _deleted:
                      continue
               if item.key == key:
                      return item.val
        raise KeyError(lowerCamelCase		)


 def __len__(				self		):
        return self._len


 def __iter__(				self		):
        yield from (item.key for item in self._buckets if item)






 def __repr__(				self		):
        __a     				=  " ,".join(
            F"{item.key}: {item.val}" for item in self._buckets if item		)
        return F"HashMap({val_string})"

"""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)
