


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def   a						(				SCREAMING_SNAKE_CASE_   :		Union[str, Any]							):






       """simple docstring"""





       UpperCamelCase     :						Optional[Any] 						=		[
           '''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						(				SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase						,			UpperCamelCase     :						int 						=		emb.weight.shape
       UpperCamelCase     :						Any 						=		nn.Linear(snake_case__				,      snake_case__				,      bias=snake_case__							)
       UpperCamelCase     :						List[Any] 						=		emb.weight.data
       return lin_layer


def   a						(				SCREAMING_SNAKE_CASE_   :		Dict				,      SCREAMING_SNAKE_CASE_   :		Tuple="uclanlp/plbart-base"				,      SCREAMING_SNAKE_CASE_   :		List[Any]=False				,      SCREAMING_SNAKE_CASE_   :		Any=False							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		torch.load(snake_case__				,      map_location='''cpu'''							)['''model''']
       remove_ignore_keys_(snake_case__							)
       UpperCamelCase     :						int 						=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

       UpperCamelCase     :						int 						=		PLBartConfig.from_pretrained(snake_case__				,      vocab_size=snake_case__							)

       UpperCamelCase     :						List[Any] 						=		state_dict['''decoder.embed_tokens.weight''']
       if not classification:
              UpperCamelCase     :						Any 						=		PLBartForConditionalGeneration(snake_case__							)
              model.model.load_state_dict(snake_case__							)
              if finetuned:
                     UpperCamelCase     :						int 						=		make_linear_from_emb(model.model.shared							)

       else:
              UpperCamelCase     :						Optional[int] 						=		{}
              for key, value in state_dict.copy().items():
                     if key.startswith('''classification_heads.sentence_classification_head'''							):
                            UpperCamelCase     :						str 						=		value
                            state_dict.pop(snake_case__							)
              UpperCamelCase     :						List[Any] 						=		PLBartForSequenceClassification(snake_case__							)
              model.model.load_state_dict(snake_case__							)
              model.classification_head.load_state_dict(snake_case__							)

       return model


if __name__ == "__main__":
  __UpperCAmelCase			:			List[Any]			      =					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"
  )
  __UpperCAmelCase			:			Dict			      =					parser.parse_args()
  __UpperCAmelCase			:			Optional[int]			      =					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)









def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						if not root or root not in binary_tree_mirror_dictionary:
												return
						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    binary_tree_mirror_dictionary[root][:2]
						_SCREAMING_SNAKE_CASE       =    [right_child, left_child]
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = 1						)				->    dict:






						"""simple docstring"""





						if not binary_tree:
												raise ValueError("""binary tree cannot be empty"""						)
						if root not in binary_tree:
												_SCREAMING_SNAKE_CASE       =    F'root {root} is not present in the binary_tree'
												raise ValueError(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    dict(snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						return binary_tree_mirror_dictionary


if __name__ == "__main__":
		UpperCamelCase												=      {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
		print(f"Binary tree: {binary_tree}")
		UpperCamelCase												=      binary_tree_mirror(binary_tree, 5)
		print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")

