
from __future__ import annotations

_A   =			[]



def 						lowercase_      (       A__			,		A__			,		A__		):
    """simple docstring"""

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



def 						lowercase_      (       A__			,		A__		):
    """simple docstring"""

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



def 						lowercase_      (       A__		):
    """simple docstring"""

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


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





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



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("roberta."		):
            snake_case								= "roberta_prelayernorm." + tensor_key[len("roberta."		) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(".self.LayerNorm.weight"		) or tensor_key.endswith(".self.LayerNorm.bias"		):
            continue

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint-repo",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _A   =			parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

