code: 
  
  
  
  
  
  
  """simple docstring"""
  import argparse
  
  import torch
  from torch import nn
  
  from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
  
  
  def        A	(							__snake_case:   List[str]     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						[
             "encoder.version",
             "decoder.version",
             "model.encoder.version",
             "model.decoder.version",
             "_float_tensor",
             "decoder.output_projection.weight",
         ]
         for k in ignore_keys:
                state_dict.pop(__snake_case   ,    __snake_case     )
  
  
  def        A	(							__snake_case:   Optional[Any]     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						emb.weight.shape
         __magic_name__										=						nn.Linear(__snake_case   ,    __snake_case   ,    bias=__snake_case     )
         __magic_name__										=						emb.weight.data
         return lin_layer
  
  
  def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Tuple="uclanlp/plbart-base"   ,    __snake_case:   Tuple=False   ,    __snake_case:   Union[str, Any]=False     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						torch.load(__snake_case   ,    map_location='cpu'     )["model"]
         remove_ignore_keys_(__snake_case     )
         __magic_name__										=						state_dict["encoder.embed_tokens.weight"].shape[0]
  
         __magic_name__										=						PLBartConfig.from_pretrained(__snake_case   ,    vocab_size=__snake_case     )
  
         __magic_name__										=						state_dict["decoder.embed_tokens.weight"]
         if not classification:
                __magic_name__										=						PLBartForConditionalGeneration(__snake_case     )
                model.model.load_state_dict(__snake_case     )
                if finetuned:
                       __magic_name__										=						make_linear_from_emb(model.model.shared     )
  
         else:
                __magic_name__										=						{}
                for key, value in state_dict.copy().items():
                       if key.startswith('classification_heads.sentence_classification_head'     ):
                              __magic_name__										=						value
                              state_dict.pop(__snake_case     )
                __magic_name__										=						PLBartForSequenceClassification(__snake_case     )
                model.model.load_state_dict(__snake_case     )
                model.classification_head.load_state_dict(__snake_case     )
  
         return model
  
  
  if __name__ == "__main__":
   snake_case   :   Dict    		=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument(
       """--hf_config""",
       default="""uclanlp/plbart-base""",
       type=str,
       help="""Which huggingface architecture to use: plbart-base""",
   )
   parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
   parser.add_argument(
       """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
   )
   snake_case   :   Optional[Any]    		=		parser.parse_args()
   snake_case   :   Any    		=		convert_fairseq_plbart_checkpoint_from_disk(
       args.fairseq_path,
       hf_config_path=args.hf_config,
       finetuned=args.finetuned,
       classification=args.classification,
   )
   model.save_pretrained(args.pytorch_dump_folder_path)
  
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case   :   int    		=		logging.get_logger(__name__)
  
  snake_case   :   List[str]    		=		{
      """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """nystromformer"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						initializer_range
             __magic_name__										=						type_vocab_size
             __magic_name__										=						segment_means_seq_len
             __magic_name__										=						num_landmarks
             __magic_name__										=						conv_kernel_size
             __magic_name__										=						inv_coeff_init_option
             __magic_name__										=						layer_norm_eps
             super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )