


'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__A   :    Dict											= logging.get_logger(__name__)

__A   :    Optional[Any]											= {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'gpt_neo'
    lowercase  					=   ['past_key_values']
    lowercase  					=   {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Optional[int]=5_02_57       ,		lowerCamelCase       :    str=20_48       ,		lowerCamelCase       :    int=20_48       ,		lowerCamelCase       :    Any=24       ,		lowerCamelCase       :    Tuple=[[["global", "local"], 12]]       ,		lowerCamelCase       :    List[Any]=16       ,		lowerCamelCase       :    Optional[int]=None       ,		lowerCamelCase       :    Tuple=2_56       ,		lowerCamelCase       :    int="gelu_new"       ,		lowerCamelCase       :    int=0.0       ,		lowerCamelCase       :    Union[str, Any]=0.0       ,		lowerCamelCase       :    Dict=0.0       ,		lowerCamelCase       :    int=0.1       ,		lowerCamelCase       :    Any=1E-5       ,		lowerCamelCase       :    str=0.02       ,		lowerCamelCase       :    Any=True       ,		lowerCamelCase       :    List[str]=5_02_56       ,		lowerCamelCase       :    Dict=5_02_56       ,		**lowerCamelCase       :    Any       ,		)  ->    List[Any]:
      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : Tuple     =  max_position_embeddings
      lowerCAmelCase_     : Optional[Any]     =  hidden_size
      lowerCAmelCase_     : List[str]     =  num_layers
      lowerCAmelCase_     : Optional[int]     =  num_heads
      lowerCAmelCase_     : int     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  window_size
      lowerCAmelCase_     : Optional[int]     =  activation_function
      lowerCAmelCase_     : List[str]     =  resid_dropout
      lowerCAmelCase_     : Any     =  embed_dropout
      lowerCAmelCase_     : Optional[int]     =  attention_dropout
      lowerCAmelCase_     : str     =  classifier_dropout
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_epsilon
      lowerCAmelCase_     : List[str]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  use_cache

      lowerCAmelCase_     : Any     =  bos_token_id
      lowerCAmelCase_     : Optional[int]     =  eos_token_id

      lowerCAmelCase_     : Tuple     =  attention_types
      lowerCAmelCase_     : List[str]     =  self.expand_attention_types_params(lowerCamelCase						)

      if len(self.attention_layers						) != self.num_layers:
        raise ValueError(
            """Configuration for convolutional module is incorrect. """
            """It is required that `len(config.attention_layers)` == `config.num_layers` """
            F'but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '
            F'`config.num_layers = {self.num_layers}`. '
            """`config.attention_layers` is prepared using `config.attention_types`. """
            """Please verify the value of `config.attention_types` argument."""						)

      super().__init__(bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)





    @staticmethod
    def __lowercase  (  lowerCamelCase       :    Any						)  ->    List[str]:
      lowerCAmelCase_     : int     =  []
      for item in attention_types:
        for _ in range(item[1]						):
          attentions.extend(item[0]						)
      return attentions







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  List[str]				,       A__   :  Union[str, Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  import torch

  lowerCAmelCase_     : Optional[Any]     =  input.size()
  lowerCAmelCase_     : List[Any]     =  len(A__   )
  lowerCAmelCase_     : Optional[Any]     =  shape[dimension]

  lowerCAmelCase_     : Union[str, Any]     =  torch.arange(0				,       A__				,       A__   )
  lowerCAmelCase_     : Any     =  torch.div(sizedim - size				,       A__				,       rounding_mode="""floor"""   ) + 1
  lowerCAmelCase_     : Dict     =  torch.arange(A__   ) + low_indices[:min_length][:, None]

  lowerCAmelCase_     : List[Any]     =  [slice(A__   )] * rank
  lowerCAmelCase_     : List[str]     =  indices
  lowerCAmelCase_     : Tuple     =  input[s]

  lowerCAmelCase_     : List[Any]     =  list(range(0				,       rank + 1   )   )
  perm.append(perm.pop(dimension + 1   )   )

  return sliced.permute(A__   )







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  List[Any]   ):
  '''simple docstring'''





  import torch

  lowerCAmelCase_     : Any     =  torch.arange(1				,       A__   )
  lowerCAmelCase_     : Optional[int]     =  torch.remainder(A__				,       A__   )
  lowerCAmelCase_     : int     =  remainders == 0
  lowerCAmelCase_     : int     =  candidates[divisor_indices]
  lowerCAmelCase_     : Dict     =  torch.max(A__   )
  return largest_divisor, torch.div(A__				,       A__				,       rounding_mode="""floor"""   )






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[str]						)  ->    Mapping[str, Mapping[int, str]]:
      lowerCAmelCase_     : Optional[int]     =  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
      if self.use_past:
        self.fill_with_past_key_values_(lowerCamelCase       ,		direction="""inputs"""						)
        lowerCAmelCase_     : Tuple     =  {0: """batch""", 1: """past_sequence + sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}

      return common_inputs

    @property
    def __lowercase  (  self       :    Any						)  ->    int:
      return self._config.num_heads

    def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    PreTrainedTokenizer       ,		lowerCamelCase       :    int = -1       ,		lowerCamelCase       :    int = -1       ,		lowerCamelCase       :    bool = False       ,		lowerCamelCase       :    Optional[TensorType] = None       ,		)  ->    Mapping[str, Any]:
      lowerCAmelCase_     : int     =  super(lowerCamelCase       ,		self						).generate_dummy_inputs(
          lowerCamelCase       ,		batch_size=lowerCamelCase       ,		seq_length=lowerCamelCase       ,		is_pair=lowerCamelCase       ,		framework=lowerCamelCase						)

      # We need to order the input in the way they appears in the forward()
      lowerCAmelCase_     : Any     =  OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}						)

      # Need to add the past_keys
      if self.use_past:
        if not is_torch_available():
          raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
        else:
          import torch

          lowerCAmelCase_,				lowerCAmelCase_     : Dict     =  common_inputs["""input_ids"""].shape
          # Not using the same length for past_key_values
          lowerCAmelCase_     : Any     =  seqlen + 2
          lowerCAmelCase_     : Union[str, Any]     =  (
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          lowerCAmelCase_     : int     =  [
              (torch.zeros(lowerCamelCase						), torch.zeros(lowerCamelCase						)) for _ in range(self.num_layers						)
          ]

      lowerCAmelCase_     : Any     =  common_inputs["""attention_mask"""]
      if self.use_past:
        lowerCAmelCase_     : Optional[Any]     =  ordered_inputs["""attention_mask"""].dtype
        lowerCAmelCase_     : Union[str, Any]     =  torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase       ,		lowerCamelCase       ,		dtype=lowerCamelCase						)]       ,		dim=1						)

      return ordered_inputs





    @property
    def __lowercase  (  self       :    Optional[int]						)  ->    int:
      return 13







'''simple docstring'''






import argparse

import torch

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


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

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

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

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[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.")
  __A   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



