




'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]  )			->			str:
   _SCREAMING_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(_UpperCAmelCase	,					_UpperCAmelCase  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				emb.weight.shape
   _SCREAMING_SNAKE_CASE	=				nn.Linear(_UpperCAmelCase	,					_UpperCAmelCase	,					bias=_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE	=				emb.weight.data
   return lin_layer
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Union[str, Any]="facebook/mbart-large-en-ro"	,					__A    :			List[Any]=False	,					__A    :			Union[str, Any]=False  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				torch.load(_UpperCAmelCase	,					map_location="cpu"  )['model']
   remove_ignore_keys_(_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE	=				state_dict['encoder.embed_tokens.weight'].shape[0]

   _SCREAMING_SNAKE_CASE	=				MBartConfig.from_pretrained(_UpperCAmelCase	,					vocab_size=_UpperCAmelCase  )
   if mbart_aa and finetuned:
      _SCREAMING_SNAKE_CASE	=				'relu'

   _SCREAMING_SNAKE_CASE	=				state_dict['decoder.embed_tokens.weight']
   _SCREAMING_SNAKE_CASE	=				MBartForConditionalGeneration(_UpperCAmelCase  )
   model.model.load_state_dict(_UpperCAmelCase  )

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

   return model


if __name__ == "__main__":
      lowerCamelCase_			=						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_			=						parser.parse_args()
      lowerCamelCase_			=						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)







'''simple docstring'''



from collections.abc import Sequence
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Sequence[int] | None = None  )			->			int:
   if nums is None or not nums:
      raise ValueError("Input sequence should not be empty"  )

   _SCREAMING_SNAKE_CASE	=				nums[0]
   for i in range(1	,					len(__A  )  ):
      _SCREAMING_SNAKE_CASE	=				nums[i]
      _SCREAMING_SNAKE_CASE	=				max(__A	,					ans + num	,					__A  )

   return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 lowerCamelCase_			=						int(input('Enter number of elements : ').strip())
 lowerCamelCase_			=						list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
 print(max_subsequence_sum(array))

