"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]      ):
     lowercase_	:				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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_							,  lowercase_	:				Dict													=	emb.weight.shape
     lowercase_	:				List[Any]													=	nn.Linear(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			bias=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[Any]													=	emb.weight.data
     return lin_layer



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

     lowercase_	:				Any													=	MaMaaaConfig(
         vocab_size=__SCREAMING_SNAKE_CASE     ,			max_position_embeddings=10_24     ,			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'     ,			)

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

     return model


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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.")
       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       __SCREAMING_SNAKE_CASE    	=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))

