






"""simple docstring"""

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						__magic_name__            =     [
						    "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(__lowerCamelCase ,						__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__      ,    __magic_name__            =     emb.weight.shape
						__magic_name__            =     nn.Linear(__lowerCamelCase ,						__lowerCamelCase ,						bias=__lowerCamelCase   )
						__magic_name__            =     emb.weight.data
						return lin_layer






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						__magic_name__            =     torch.load(__lowerCamelCase ,						map_location="cpu"   )
						__magic_name__            =     mam_aaa["args"] or mam_aaa["cfg"]["model"]
						__magic_name__            =     mam_aaa["model"]
						remove_ignore_keys_(__lowerCamelCase   )
						__magic_name__            =     state_dict["encoder.embed_tokens.weight"].shape[0]

						__magic_name__            =     MaMaaaConfig(
						    vocab_size=__lowerCamelCase ,						max_position_embeddings=1_0_2_4 ,						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" ,						)

						__magic_name__            =     state_dict["decoder.embed_tokens.weight"]
						__magic_name__            =     MaMaaaForConditionalGeneration(__lowerCamelCase   )
						model.model.load_state_dict(__lowerCamelCase ,						strict=__lowerCamelCase   )
						__magic_name__            =     make_linear_from_emb(model.model.shared   )

						return model


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











"""simple docstring"""

from __future__ import annotations

lowercase    			=       []






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						for i in range(len(__lowerCamelCase   )   ):
												if board[row][i] == 1:
																		return False
						for i in range(len(__lowerCamelCase   )   ):
												if board[i][column] == 1:
																		return False
						for i, j in zip(range(__lowerCamelCase ,						-1 ,						-1   ) ,						range(__lowerCamelCase ,						-1 ,						-1   )   ):
												if board[i][j] == 1:
																		return False
						for i, j in zip(range(__lowerCamelCase ,						-1 ,						-1   ) ,						range(__lowerCamelCase ,						len(__lowerCamelCase   )   )   ):
												if board[i][j] == 1:
																		return False
						return True






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						if row >= len(__lowerCamelCase   ):
												solution.append(__lowerCamelCase   )
												printboard(__lowerCamelCase   )
												print()
												return True
						for i in range(len(__lowerCamelCase   )   ):
												if is_safe(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   ):
																		__magic_name__            =     1
																		solve(__lowerCamelCase ,						row + 1   )
																		__magic_name__            =     0
						return False






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]]   ):

						'''simple docstring'''






						for i in range(len(__lowerCamelCase   )   ):
												for j in range(len(__lowerCamelCase   )   ):
																		if board[i][j] == 1:
																								print("Q" ,						end=" "   )
																		else:
																								print("." ,						end=" "   )
												print()


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


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



