






from collections.abc import Sequence
from queue import Queue

class     _SCREAMING_SNAKE_CASE       :





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





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

class     _SCREAMING_SNAKE_CASE       :





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





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					int	):
		self._update_tree(self.root ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Tuple	):
		return self._query_range(self.root ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					List[str]	):
		if start == end:
			return SegmentTreeNode(__lowerCamelCase ,  __lowerCamelCase ,  self.collection[start]	)
		UpperCamelCase     :Dict          =	(start + end) // 2
		UpperCamelCase     :List[str]          =	self._build_tree(__lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Union[str, Any]          =	self._build_tree(mid + 1 ,  __lowerCamelCase	)
		return SegmentTreeNode(__lowerCamelCase ,  __lowerCamelCase ,  self.fn(left.val ,  right.val	) ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					List[str]	):
		if node.start == i and node.end == i:
			UpperCamelCase     :List[Any]          =	val
			return
		if i <= node.mid:
			self._update_tree(node.left ,  __lowerCamelCase ,  __lowerCamelCase	)
		else:
			self._update_tree(node.right ,  __lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Union[str, Any]          =	self.fn(node.left.val ,  node.right.val	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					int	):
		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 ,  __lowerCamelCase ,  __lowerCamelCase	)
			else:
				# range in left child tree and right child tree
				return self.fn(
				    self._query_range(node.left ,  __lowerCamelCase ,  node.mid	) ,  self._query_range(node.right ,  node.mid + 1 ,  __lowerCamelCase	) ,  )
		else:
			# range in right child tree
			return self._query_range(node.right ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		if self.root is not None:
			UpperCamelCase     :Union[str, Any]          =	Queue()
			queue.put(self.root	)
			while not queue.empty():
				UpperCamelCase     :Optional[int]          =	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)
		UpperCAmelCase_ :						Optional[int]      =      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()







'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
           'decoder.output_projection.weight',
       ]
       for k in ignore_keys:
              state_dict.pop(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )['model']
       remove_ignore_keys_(__lowercase     )
       __UpperCamelCase       =  state_dict['encoder.embed_tokens.weight'].shape[0]

       __UpperCamelCase       =  MBartConfig.from_pretrained(__lowercase				,  vocab_size=__lowercase     )
       if mbart_aa and finetuned:
              __UpperCamelCase       =  'relu'

       __UpperCamelCase       =  state_dict['decoder.embed_tokens.weight']
       __UpperCamelCase       =  MBartForConditionalGeneration(__lowercase     )
       model.model.load_state_dict(__lowercase     )

       if finetuned:
              __UpperCamelCase       =  make_linear_from_emb(model.model.shared     )

       return model


if __name__ == "__main__":
    a__							:							Dict =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''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							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)
