


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str							=						[
     'encoder.version',
     'decoder.version',
     'model.encoder.version',
     'model.decoder.version',
     '_float_tensor',
     'decoder.output_projection.weight',
 ]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						)
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase		:							int							=						emb.weight.shape
 __lowerCamelCase		:							int							=						nn.Linear(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       bias=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Optional[Any]							=						emb.weight.data
 return lin_layer
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__="uclanlp/plbart-base"			,       SCREAMING_SNAKE_CASE__=False			,       SCREAMING_SNAKE_CASE__=False						):
 __lowerCamelCase		:							List[Any]							=						torch.load(SCREAMING_SNAKE_CASE__			,       map_location='cpu'						)['model']
 remove_ignore_keys_(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]							=						state_dict['encoder.embed_tokens.weight'].shape[0]

 __lowerCamelCase		:							int							=						PLBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__			,       vocab_size=SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Optional[int]							=						state_dict['decoder.embed_tokens.weight']
 if not classification:
  __lowerCamelCase		:							Optional[Any]							=						PLBartForConditionalGeneration(SCREAMING_SNAKE_CASE__						)
  model.model.load_state_dict(SCREAMING_SNAKE_CASE__						)
  if finetuned:
   __lowerCamelCase		:							Any							=						make_linear_from_emb(model.model.shared						)

 else:
  __lowerCamelCase		:							int							=						{}
  for key, value in state_dict.copy().items():
   if key.startswith('classification_heads.sentence_classification_head'						):
    __lowerCamelCase		:							int							=						value
    state_dict.pop(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Tuple							=						PLBartForSequenceClassification(SCREAMING_SNAKE_CASE__						)
  model.model.load_state_dict(SCREAMING_SNAKE_CASE__						)
  model.classification_head.load_state_dict(SCREAMING_SNAKE_CASE__						)

 return model


if __name__ == "__main__":
       lowercase_           =	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'
       )
       lowercase_           =	parser.parse_args()
       lowercase_           =	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)





class        A_					:




      '''simple docstring'''


      def __init__(				self:       Tuple						,      a:       Dict=None				):
       __lowerCamelCase		:							List[Any]							=						data
       __lowerCamelCase		:							Optional[Any]							=						None




      def __repr__(				self:       Optional[int]				):
       __lowerCamelCase		:							Any							=						''
       __lowerCamelCase		:							List[Any]							=						self
       while temp:
        string_rep += F'<{temp.data}> ---> '
        __lowerCamelCase		:							Any							=						temp.next
       string_rep += "<END>"
       return string_rep



def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):

 # if elements_list is empty
 if not elements_list:
  raise Exception('The Elements List is empty'						)

 # Set first element as Head
 __lowerCamelCase		:							Optional[int]							=						Node(elements_list[0]						)
 __lowerCamelCase		:							List[str]							=						head
 # Loop through elements from position 1
 for data in elements_list[1:]:
  __lowerCamelCase		:							str							=						Node(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Tuple							=						current.next
 return head


lowercase_           =	[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_           =	make_linked_list(list_data)
print('Linked List:')



print(linked_list)

