
from __future__ import annotations

from typing import Generic, TypeVar

__magic_name__  :  Tuple     			=   TypeVar('T')


class 							lowerCamelCase       (		Generic[T]					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase					):
      A_								=					data  # Assign data
      A_								=					None  # Initialize next as null
      A_								=					None  # Initialize prev as null



class 							lowerCamelCase       (		Generic[T]					):



  """simple docstring"""





  def __init__(					self					):
      A_								=					None

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      if self.head is None:
          A_								=					Node(__UpperCamelCase					)
      else:
          A_								=					Node(__UpperCamelCase					)
          A_								=					new_node
          A_								=					self.head
          A_								=					None
          A_								=					new_node

  def 					lowercase_						(					self					):
      if self.head is None:
          return None
      else:
          assert self.head is not None
          A_								=					self.head.data
          A_								=					self.head.next
          if self.head is not None:
              A_								=					None
          return temp

  def 					lowercase_						(					self					):
      return self.head.data if self.head is not None else None

  def __len__(					self					):
      A_								=					self.head
      A_								=					0
      while temp is not None:
          count += 1
          A_								=					temp.next
      return count

  def 					lowercase_						(					self					):
      return self.head is None

  def 					lowercase_						(					self					):
      print("stack elements are:"					)
      A_								=					self.head
      while temp is not None:
          print(temp.data						,    end="->"					)
          A_								=					temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     __magic_name__  :  Stack[int]     			=   Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())








import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "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        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   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'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   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)






