



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 	lowerCamelCase_   (  _a    :		List[Any]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Union[str, Any]   						= [
        '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_   (  _a    :		Union[str, Any]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Dict   						= emb.weight.shape
    UpperCAmelCase_			:    List[Any]   						= nn.Linear(__SCREAMING_SNAKE_CASE					,						__SCREAMING_SNAKE_CASE					,						bias=__SCREAMING_SNAKE_CASE  )
    UpperCAmelCase_			:    List[Any]   						= emb.weight.data
    return lin_layer







def 	lowerCamelCase_   (  _a    :		List[str]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Any   						= torch.load(__SCREAMING_SNAKE_CASE					,						map_location="""cpu"""  )
    UpperCAmelCase_			:    Dict   						= mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase_			:    Optional[int]   						= mam_aaa['model']
    remove_ignore_keys_(__SCREAMING_SNAKE_CASE  )
    UpperCAmelCase_			:    str   						= state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase_			:    Any   						= 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_			:    List[Any]   						= state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase_			:    Tuple   						= MaMaaaForConditionalGeneration(__SCREAMING_SNAKE_CASE  )
    model.model.load_state_dict(__SCREAMING_SNAKE_CASE					,						strict=__SCREAMING_SNAKE_CASE  )
    UpperCAmelCase_			:    Tuple   						= make_linear_from_emb(model.model.shared  )

    return model


if __name__ == "__main__":
      UpperCamelCase_										=					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.''')
      UpperCamelCase_										=					parser.parse_args()
      UpperCamelCase_										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
      model.save_pretrained(args.pytorch_dump_folder_path)

"""simple docstring"""







from __future__ import annotations

__SCREAMING_SNAKE_CASE    	=[]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if board[row][i] == 1:
               return False
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if board[i][column] == 1:
               return False
     for i, j in zip(range(__SCREAMING_SNAKE_CASE     ,			-1     ,			-1      )     ,			range(__SCREAMING_SNAKE_CASE     ,			-1     ,			-1      )      ):
          if board[i][j] == 1:
               return False
     for i, j in zip(range(__SCREAMING_SNAKE_CASE     ,			-1     ,			-1      )     ,			range(__SCREAMING_SNAKE_CASE     ,			len(__SCREAMING_SNAKE_CASE      )      )      ):
          if board[i][j] == 1:
               return False
     return True



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     if row >= len(__SCREAMING_SNAKE_CASE      ):
          solution.append(__SCREAMING_SNAKE_CASE      )
          printboard(__SCREAMING_SNAKE_CASE      )
          print()
          return True
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if is_safe(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				int													=	1
               solve(__SCREAMING_SNAKE_CASE     ,			row + 1      )
               lowercase_	:				Dict													=	0
     return False



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]      ):
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          for j in range(len(__SCREAMING_SNAKE_CASE      )      ):
               if board[i][j] == 1:
                    print('Q'     ,			end=' '      )
               else:
                    print('.'     ,			end=' '      )
          print()


# n=int(input("The no. of queens"))
__SCREAMING_SNAKE_CASE    	=8
__SCREAMING_SNAKE_CASE    	=[[0 for i in range(n)] for j in range(n)]
solve(board, 0)







print("The total no. of solutions are :", len(solution))

