



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def        a( A			:       Optional[int]			)						->			Optional[int]:
					"""simple docstring"""





					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(A					,	A			)






def        a( A			:       Optional[int]			)						->			List[Any]:
					"""simple docstring"""





					a	,							a   	=  emb.weight.shape
					a   	=  nn.Linear(A					,	A					,	bias=A			)
					a   	=  emb.weight.data
					return lin_layer






def        a( A			:       Optional[int]					,	A			:       str="uclanlp/plbart-base"					,	A			:       Any=False					,	A			:       Optional[int]=False			)						->			List[Any]:
					"""simple docstring"""





					a   	=  torch.load(A					,	map_location="cpu"			)["model"]
					remove_ignore_keys_(A			)
					a   	=  state_dict["encoder.embed_tokens.weight"].shape[0]

					a   	=  PLBartConfig.from_pretrained(A					,	vocab_size=A			)

					a   	=  state_dict["decoder.embed_tokens.weight"]
					if not classification:
										a   	=  PLBartForConditionalGeneration(A			)
										model.model.load_state_dict(A			)
										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(A			)
										a   	=  PLBartForSequenceClassification(A			)
										model.model.load_state_dict(A			)
										model.classification_head.load_state_dict(A			)

					return model


if __name__ == "__main__":
				_lowercase:					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"
				)
				_lowercase:					Optional[Any]      =						parser.parse_args()
				_lowercase:					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)





from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass



@dataclass
class       _lowercase				:





	"""simple docstring"""
	__A		      =				42
	__A		      =				None
	__A		      =				None






def        a( )						->			TreeNode:
					"""simple docstring"""





					return TreeNode(3					,	TreeNode(9			)					,	TreeNode(20					,	TreeNode(15			)					,	TreeNode(7			)			)			)






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															right_view.append(root.val			)

										depth_first_search(root.right					,	depth + 1					,	A			)
										depth_first_search(root.left					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return right_view

					depth_first_search(A					,	0					,	A			)
					return right_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															left_view.append(root.val			)

										depth_first_search(root.left					,	depth + 1					,	A			)
										depth_first_search(root.right					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return left_view

					depth_first_search(A					,	0					,	A			)
					return left_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first

															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															top_view.append(pair[1][0]			)

					a   	=  []
					if not root:
										return top_view

					breadth_first_search(A					,	A			)
					return top_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					from collections import defaultdict

					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first
															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															bottom_view.append(pair[1][-1]			)

					a   	=  []
					if not root:
										return bottom_view

					breadth_first_search(A					,	A			)
					return bottom_view


if __name__ == "__main__":
				import doctest

				doctest.testmod()
