





"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




       _UpperCAmelCase     =     False
       while is_sorted is False:  # Until all the indices are traversed keep looping
              _UpperCAmelCase     =     True
              for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all even indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False

              for i in range(1,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all odd indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False
       return input_list


if __name__ == "__main__":
    print('''Enter list to be sorted''')
    lowerCAmelCase_			=							[int(x) for x in input().split()]
    # inputing elements of the list in one line
    lowerCAmelCase_			=							odd_even_sort(input_list)
    print('''The sorted list is''')
    print(sorted_list)













"""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)






