




"""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__       (snake_case__							:					Any		,   snake_case__							:					Tuple		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  WavaVecaForSequenceClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		List[Any]								=  downstream_dict["""projector.weight"""]
  _snake_case							:		Optional[Any]								=  downstream_dict["""projector.bias"""]
  _snake_case							:		int								=  downstream_dict["""model.post_net.linear.weight"""]
  _snake_case							:		List[str]								=  downstream_dict["""model.post_net.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  WavaVecaForAudioFrameClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[int]								=  downstream_dict["""model.linear.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  WavaVecaForXVector.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[Any]								=  downstream_dict["""connector.weight"""]
  _snake_case							:		Optional[int]								=  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
    _snake_case							:		Tuple								=  downstream_dict[
        F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
    ]
    _snake_case							:		str								=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

  _snake_case							:		Any								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  _snake_case							:		Union[str, Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  _snake_case							:		List[Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  _snake_case							:		List[str]								=  downstream_dict["""objective.W"""]
  return model


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  torch.load(snake_case__		,   map_location="""cpu"""    )

  _snake_case							:		Union[str, Any]								=  checkpoint["""Downstream"""]

  _snake_case							:		Optional[Any]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		Optional[int]								=  WavaVecaFeatureExtractor.from_pretrained(
      snake_case__		,   return_attention_mask=snake_case__		,   do_normalize=snake_case__    )

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

  if hf_config.use_weighted_layer_sum:
    _snake_case							:		Optional[int]								=  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(snake_case__    )
  hf_model.save_pretrained(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)






class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  n
             lowerCamelCase__		:				Union[str, Any]    =  [None] * self.n
             lowerCamelCase__		:				List[str]    =  0  # index of the first element
             lowerCamelCase__		:				Any    =  0
             lowerCamelCase__		:				Any    =  0





       def __len__(   self							:			Tuple       ):


             '''simple docstring'''



             return self.size





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return self.size == 0





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return False if self.is_empty() else self.array[self.front]





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             if self.size >= self.n:
                   raise Exception("QUEUE IS FULL"       )

             lowerCamelCase__		:				Optional[Any]    =  data
             lowerCamelCase__		:				Tuple    =  (self.rear + 1) % self.n
             self.size += 1
             return self





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             if self.size == 0:
                   raise Exception("UNDERFLOW"       )

             lowerCamelCase__		:				Any    =  self.array[self.front]
             lowerCamelCase__		:				List[Any]    =  None
             lowerCamelCase__		:				str    =  (self.front + 1) % self.n
             self.size -= 1
             return temp


