
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

_lowerCamelCase	:    Optional[Any]          =      TypeVar('''T''')




class        SCREAMING_SNAKE_CASE__					(     Generic[T] ):





	'''simple docstring'''



	def __init__(  self     :   Dict      ,  lowercase     :   list[T]      ,  lowercase     :   Callable[[T, T], T]     ):


				'''simple docstring'''


				_snake_case          =      None

				_snake_case          =      len(lowercase     )
				_snake_case          =      [any_type for _ in range(self.N     )] + arr
				_snake_case          =      fnc
				self.build()



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				for p in range(self.N - 1      ,  0      ,  -1     ):
							_snake_case          =      self.fn(self.st[p * 2]      ,  self.st[p * 2 + 1]     )



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   int      ,  lowercase     :   T     ):


				'''simple docstring'''


				p += self.N
				_snake_case          =      v
				while p > 1:
							_snake_case          =      p // 2
							_snake_case          =      self.fn(self.st[p * 2]      ,  self.st[p * 2 + 1]     )





	def 			A							(  self     :   List[str]      ,  lowercase     :   int      ,  lowercase     :   int     ):  # noqa: E741


				'''simple docstring'''


				_snake_case     , _snake_case          =      l + self.N, r + self.N

				_snake_case          =      None
				while l <= r:
							if l % 2 == 1:
										_snake_case          =      self.st[l] if res is None else self.fn(lowercase      ,  self.st[l]     )
							if r % 2 == 0:
										_snake_case          =      self.st[r] if res is None else self.fn(lowercase      ,  self.st[r]     )
							_snake_case     , _snake_case          =      (l + 1) // 2, (r - 1) // 2
				return res


if __name__ == "__main__":
			from functools import reduce

			_lowerCamelCase	:    Optional[int]          =      [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

			_lowerCamelCase	:    Tuple          =      {
			    0: 7,
			    1: 2,
			    2: 6,
			    3: -14,
			    4: 5,
			    5: 4,
			    6: 7,
			    7: -10,
			    8: 9,
			    9: 10,
			    10: 12,
			    11: 1,
			}

			_lowerCamelCase	:    int          =      SegmentTree(test_array, min)
			_lowerCamelCase	:    Optional[Any]          =      SegmentTree(test_array, max)
			_lowerCamelCase	:    int          =      SegmentTree(test_array, lambda a, b: a + b)






			def 	a_		(	)			->  None:
						for i in range(len(__lowercase			)			):
									for j in range(__lowercase	, len(__lowercase			)			):
												_snake_case          =      reduce(__lowercase	, test_array[i : j + 1]			)
												_snake_case          =      reduce(__lowercase	, test_array[i : j + 1]			)
												_snake_case          =      reduce(lambda __lowercase	, __lowercase			: a + b	, test_array[i : j + 1]			)
												assert min_range == min_segment_tree.query(__lowercase	, __lowercase			)
												assert max_range == max_segment_tree.query(__lowercase	, __lowercase			)
												assert sum_range == sum_segment_tree.query(__lowercase	, __lowercase			)

			test_all_segments()

			for index, value in test_updates.items():
						_lowerCamelCase	:    Dict          =      value
						min_segment_tree.update(index, value)
						max_segment_tree.update(index, value)
						sum_segment_tree.update(index, value)
						test_all_segments()

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    '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 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

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

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

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

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      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	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      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)