






'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							_enforce_args(UpperCamelCase				,			UpperCamelCase      )
							if n == 0:
														return 0
							lowerCamelCase__					:	List[str]         =       float("""-inf"""      )
							for i in range(1				,			n + 1      ):
														lowerCamelCase__					:	List[Any]         =       max(
														    UpperCamelCase				,			prices[i - 1] + naive_cut_rod_recursive(n - i				,			UpperCamelCase      )      )

							return max_revue





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Dict:
							_enforce_args(UpperCamelCase				,			UpperCamelCase      )
							lowerCamelCase__					:	Tuple         =       [float("""-inf"""      ) for _ in range(n + 1      )]
							return _top_down_cut_rod_recursive(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:







							if max_rev[n] >= 0:
														return max_rev[n]
							elif n == 0:
														return 0
							else:
														lowerCamelCase__					:	int         =       float("""-inf"""      )
														for i in range(1				,			n + 1      ):
																					lowerCamelCase__					:	List[Any]         =       max(
																					    UpperCamelCase				,			prices[i - 1] + _top_down_cut_rod_recursive(n - i				,			UpperCamelCase				,			UpperCamelCase      )				,			)

														lowerCamelCase__					:	List[str]         =       max_revenue

							return max_rev[n]





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							_enforce_args(UpperCamelCase				,			UpperCamelCase      )

							# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
							# length 0.
							lowerCamelCase__					:	str         =       [float("""-inf"""      ) for _ in range(n + 1      )]
							lowerCamelCase__					:	str         =       0

							for i in range(1				,			n + 1      ):
														lowerCamelCase__					:	str         =       max_rev[i]
														for j in range(1				,			i + 1      ):
																					lowerCamelCase__					:	Optional[int]         =       max(UpperCamelCase				,			prices[j - 1] + max_rev[i - j]      )

														lowerCamelCase__					:	List[str]         =       max_revenue_i

							return max_rev[n]





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Union[str, Any]:







							if n < 0:
														lowerCamelCase__					:	List[str]         =       f'''n must be greater than or equal to 0. Got n = {n}'''
														raise ValueError(UpperCamelCase      )

							if n > len(UpperCamelCase      ):
														lowerCamelCase__					:	int         =       (
														    """Each integral piece of rod must have a corresponding price. """
														    f'''Got n = {n} but length of prices = {len(UpperCamelCase      )}'''
														)
														raise ValueError(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       ()      ->       Any:
							lowerCamelCase__					:	int         =       [6, 10, 12, 15, 20, 23]
							lowerCamelCase__					:	Dict         =       len(UpperCamelCase      )

							# the best revenue comes from cutting the rod into 6 pieces, each
							# of length 1 resulting in a revenue of 6 * 6 = 36.
							lowerCamelCase__					:	Optional[int]         =       36

							lowerCamelCase__					:	Union[str, Any]         =       top_down_cut_rod(UpperCamelCase				,			UpperCamelCase      )
							lowerCamelCase__					:	Tuple         =       bottom_up_cut_rod(UpperCamelCase				,			UpperCamelCase      )
							lowerCamelCase__					:	Optional[int]         =       naive_cut_rod_recursive(UpperCamelCase				,			UpperCamelCase      )

							assert expected_max_revenue == max_rev_top_down
							assert max_rev_top_down == max_rev_bottom_up
							assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
			main()





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''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 _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







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

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

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

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	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							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[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)




