code: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE       = {
      "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
      "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
      "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
      "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
  }
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''longt5'''
      lowercase__		      =   ['''past_key_values''']
      lowercase__		      =   {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
  
  
  
  
      def __init__(					self		: List[str]			,				snake_case_		: List[str]=32_128			,				snake_case_		: str=512			,				snake_case_		: Union[str, Any]=64			,				snake_case_		: str=2_048			,				snake_case_		: Optional[int]=6			,				snake_case_		: Union[str, Any]=None			,				snake_case_		: Union[str, Any]=8			,				snake_case_		: List[Any]=127			,				snake_case_		: Dict=16			,				snake_case_		: List[str]=32			,				snake_case_		: Dict=128			,				snake_case_		: Optional[int]=0.1			,				snake_case_		: int=1e-6			,				snake_case_		: List[Any]=1.0			,				snake_case_		: int="relu"			,				snake_case_		: List[str]=True			,				snake_case_		: Optional[int]="local"			,				snake_case_		: Optional[int]=True			,				snake_case_		: Any=0			,				snake_case_		: int=1			,				**snake_case_		: Optional[int]			,				)   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       vocab_size
       A__ =       d_model
       A__ =       d_kv
       A__ =       d_ff
       A__ =       num_layers
       # default = symmetry
       A__ =       num_decoder_layers if num_decoder_layers is not None else self.num_layers
       A__ =       num_heads
       A__ =       local_radius
       A__ =       global_block_size
       A__ =       relative_attention_num_buckets
       A__ =       relative_attention_max_distance
       A__ =       dropout_rate
       A__ =       layer_norm_epsilon
       A__ =       initializer_factor
       A__ =       feed_forward_proj
       A__ =       encoder_attention_type
       A__ =       use_cache
  
       A__ =       self.feed_forward_proj.split("-"   )
       A__ =       act_info[-1]
       A__ =       act_info[0] == "gated"
  
       if len(snake_case_   ) > 1 and act_info[0] != "gated" or len(snake_case_   ) > 2:
        raise ValueError(
            F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"   )
  
       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
        A__ =       "gelu_new"
  
       super().__init__(
           pad_token_id=snake_case_			,				eos_token_id=snake_case_			,				is_encoder_decoder=snake_case_			,				**snake_case_			,				)
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
  
  
  
  
      @property
      def 							__magic_name__							(					self		: str   )   ->							Mapping[str, Mapping[int, str]]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       {
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
        A__ =       "past_encoder_sequence + sequence"
        A__ =       {0: "batch"}
        A__ =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
        A__ =       {0: "batch", 1: "decoder_sequence"}
        A__ =       {0: "batch", 1: "decoder_sequence"}
  
       if self.use_past:
        self.fill_with_past_key_values_(snake_case_			,				direction="inputs"   )
  
       return common_inputs
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Any   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       return 13
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   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(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = 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       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  