



'''simple docstring'''
from typing import List

import numpy as np






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Optional[Any] 				=   {key: len(UpperCAmelCase_							) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_  ,  UpperCAmelCase_							)}
						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.'
												    )							)
						_UpperCamelCase					:		Union[str, Any] 				=   max(lists_lengths.values()  ,  default=0							)
						return max(1  ,  UpperCAmelCase_							)






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Dict 				=   []
						for group_idx in range(UpperCAmelCase_							):
												_UpperCamelCase					:		Tuple 				=   num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
												if num_shards_to_add == 0:
																		break
												_UpperCamelCase					:		Union[str, Any] 				=   shards_indices_per_group[-1].stop if shards_indices_per_group else 0
												_UpperCamelCase					:		Union[str, Any] 				=   range(UpperCAmelCase_  ,  start + num_shards_to_add							)
												shards_indices_per_group.append(UpperCAmelCase_							)
						return shards_indices_per_group






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






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






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









'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __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(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






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

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

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

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

       return model


if __name__ == "__main__":
    a__							:							Dict =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''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							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)
