


"""simple docstring"""






from typing import List

import numpy as np
def       A_   (   _lowerCAmelCase     :					dict       ):






     """simple docstring"""

     _a    	=				{key: len(_lowerCAmelCase       ) for key, value in gen_kwargs.items() if isinstance(_lowerCAmelCase,     _lowerCAmelCase       )}
     if len(set(lists_lengths.values()       )       ) > 1:
          raise RuntimeError(
              (
                  '''Sharding is ambiguous for this dataset: '''
                  + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                  + '''\n'''.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()       )
                  + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                  + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
              )       )
     _a    	=				max(lists_lengths.values(),     default=0       )
     return max(1,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				[]
     for group_idx in range(_lowerCAmelCase       ):
          _a    	=				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
          if num_shards_to_add == 0:
               break
          _a    	=				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
          _a    	=				range(_lowerCAmelCase,     start + num_shards_to_add       )
          shards_indices_per_group.append(_lowerCAmelCase       )
     return shards_indices_per_group
def       A_   (   _lowerCAmelCase     :					dict,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				_number_of_shards_in_gen_kwargs(_lowerCAmelCase       )
     if num_shards == 1:
          return [dict(_lowerCAmelCase       )]
     else:
          _a    	=				_distribute_shards(num_shards=_lowerCAmelCase,     max_num_jobs=_lowerCAmelCase       )
          return [
              {
                  key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                  if isinstance(_lowerCAmelCase,     _lowerCAmelCase       )
                  else value
                  for key, value in gen_kwargs.items()
              }
              for group_idx in range(len(_lowerCAmelCase       )       )
          ]
def       A_   (   _lowerCAmelCase     :					List[dict]       ):






     """simple docstring"""

     return {
         key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
         if isinstance(gen_kwargs_list[0][key],     _lowerCAmelCase       )
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }

def       A_   (   _lowerCAmelCase     :					np.random.Generator,     _lowerCAmelCase     :					dict       ):






     """simple docstring"""

     _a    	=				{len(_lowerCAmelCase       ) for value in gen_kwargs.values() if isinstance(_lowerCAmelCase,     _lowerCAmelCase       )}
     _a    	=				{}
     for size in list_sizes:
          _a    	=				list(range(_lowerCAmelCase       )       )
          rng.shuffle(indices_per_size[size]       )
     # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
     _a    	=				dict(_lowerCAmelCase       )
     for key, value in shuffled_kwargs.items():
          if isinstance(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				[value[i] for i in indices_per_size[len(_lowerCAmelCase       )]]
     return shuffled_kwargs



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''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       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

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

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				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)