






from collections.abc import Sequence
from queue import Queue



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Optional[int]							,		__A					:  Dict							,		__A					:  Tuple							,		__A					:  int							,		__A					:  Optional[Any]=None							,		__A					:  Dict=None	):
						snake_case__			:				int       =  start
						snake_case__			:				Optional[Any]       =  end
						snake_case__			:				List[str]       =  val
						snake_case__			:				str       =  (start + end) // 2
						snake_case__			:				Tuple       =  left
						snake_case__			:				Optional[Any]       =  right






					def __repr__( self					:  List[str]	):
						return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  Sequence							,		__A					:  Optional[int]	):
						snake_case__			:				Optional[int]       =  collection
						snake_case__			:				Union[str, Any]       =  function
						if self.collection:
							snake_case__			:				Tuple       =  self._build_tree(0							,		len(__A	) - 1	)



					def 	_lowercase					( self					:  str							,		__A					:  Union[str, Any]							,		__A					:  Optional[Any]	):
						self._update_tree(self.root							,		__A							,		__A	)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  int							,		__A					:  Optional[Any]	):
						return self._query_range(self.root							,		__A							,		__A	)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Tuple							,		__A					:  Any	):
						if start == end:
							return SegmentTreeNode(__A							,		__A							,		self.collection[start]	)
						snake_case__			:				Dict       =  (start + end) // 2
						snake_case__			:				Tuple       =  self._build_tree(__A							,		__A	)
						snake_case__			:				Tuple       =  self._build_tree(mid + 1							,		__A	)
						return SegmentTreeNode(__A							,		__A							,		self.fn(left.val							,		right.val	)							,		__A							,		__A	)



					def 	_lowercase					( self					:  Any							,		__A					:  str							,		__A					:  int							,		__A					:  Any	):
						if node.start == i and node.end == i:
							snake_case__			:				Optional[Any]       =  val
							return
						if i <= node.mid:
							self._update_tree(node.left							,		__A							,		__A	)
						else:
							self._update_tree(node.right							,		__A							,		__A	)
						snake_case__			:				Union[str, Any]       =  self.fn(node.left.val							,		node.right.val	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  List[Any]							,		__A					:  List[str]							,		__A					:  List[str]	):
						if node.start == i and node.end == j:
							return node.val

						if i <= node.mid:
							if j <= node.mid:
								# range in left child tree
								return self._query_range(node.left							,		__A							,		__A	)
							else:
								# range in left child tree and right child tree
								return self.fn(
								    self._query_range(node.left							,		__A							,		node.mid	)							,		self._query_range(node.right							,		node.mid + 1							,		__A	)							,		)
						else:
							# range in right child tree
							return self._query_range(node.right							,		__A							,		__A	)






					def 	_lowercase					( self					:  List[Any]	):
						if self.root is not None:
							snake_case__			:				Union[str, Any]       =  Queue()
							queue.put(self.root	)
							while not queue.empty():
								snake_case__			:				Union[str, Any]       =  queue.get()
								yield node

								if node.left is not None:
									queue.put(node.left	)

								if node.right is not None:
									queue.put(node.right	)


if __name__ == "__main__":
	import operator

	for fn in [operator.add, max, min]:
		print("""*""" * 50)
		__lowerCamelCase :      Optional[Any]										=				SegmentTree([2, 1, 5, 3, 4], fn)
		for node in arr.traverse():
			print(node)
		print()

		arr.update(1, 5)
		for node in arr.traverse():
			print(node)
		print()

		print(arr.query_range(3, 4))  # 7
		print(arr.query_range(2, 2))  # 5
		print(arr.query_range(1, 3))  # 13
		print()











import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a 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="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      str										=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)



