
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def  __lowerCamelCase					( _lowercase			)       ->      Dict:
     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(lowerCAmelCase__						,     lowerCAmelCase__			)

def  __lowerCamelCase					( _lowercase			)       ->      int:
     UpperCamelCase    ,		UpperCamelCase						    =					emb.weight.shape
     UpperCamelCase						    =					nn.Linear(lowerCAmelCase__						,     lowerCAmelCase__						,     bias=lowerCAmelCase__			)
     UpperCamelCase						    =					emb.weight.data
     return lin_layer

def  __lowerCamelCase					( _lowercase						,     _lowercase="facebook/mbart-large-en-ro"						,     _lowercase=False						,     _lowercase=False			)       ->      Union[str, Any]:
     UpperCamelCase						    =					torch.load(lowerCAmelCase__						,     map_location='cpu'			)['model']
     remove_ignore_keys_(lowerCAmelCase__			)
     UpperCamelCase						    =					state_dict['encoder.embed_tokens.weight'].shape[0]

     UpperCamelCase						    =					MBartConfig.from_pretrained(lowerCAmelCase__						,     vocab_size=lowerCAmelCase__			)
     if mbart_aa and finetuned:
          UpperCamelCase						    =					'relu'

     UpperCamelCase						    =					state_dict['decoder.embed_tokens.weight']
     UpperCamelCase						    =					MBartForConditionalGeneration(lowerCAmelCase__			)
     model.model.load_state_dict(lowerCAmelCase__			)

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

     return model


if __name__ == "__main__":
 _snake_case      = 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''')
 _snake_case      = parser.parse_args()
 _snake_case      = 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)






from collections.abc import Callable




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  a
   __a					=  b
   if function(lowerCAmelCase__   ) == 0:  # one of the a or b is a root for the function
      return a
   elif function(lowerCAmelCase__   ) == 0:
      return b
   elif (
       function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) > 0
   ):  # if none of these are root and they are both positive or negative,
      # then this algorithm can't find the root
      raise ValueError("""could not find root in given interval."""   )
   else:
      __a					=  start + (end - start) / 2.0
      while abs(start - mid   ) > 10**-7:  # until precisely equals to 10^-7
         if function(lowerCAmelCase__   ) == 0:
            return mid
         elif function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) < 0:
            __a					=  mid
         else:
            __a					=  mid
         __a					=  start + (end - start) / 2.0
      return mid




def  a   (lowerCAmelCase__   ):
   return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()



