





"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




       def merge(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							) -> list:

              def _merge():
                     while left and right:
                            yield (left if left[0] <= right[0] else right).pop(0							)
                     yield from left
                     yield from right

              return list(_merge()							)

       if len(SCREAMING_SNAKE_CASE							) <= 1:
              return collection
       _UpperCAmelCase     =     len(SCREAMING_SNAKE_CASE							) // 2
       return merge(merge_sort(collection[:mid]							),merge_sort(collection[mid:]							)							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    lowerCAmelCase_			=							input('''Enter numbers separated by a comma:\n''').strip()
    lowerCAmelCase_			=							[int(item) for item in user_input.split(''',''')]
    print(*merge_sort(unsorted), sep=''',''')









"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     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='''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.''')
    lowerCAmelCase_			=							parser.parse_args()
    lowerCAmelCase_			=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






