





'''simple docstring'''






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase       		=      logging.getLogger()

lowerCAmelCase       		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 		lowerCamelCase  (      _A       ):



	def   _lowerCamelCase    (   self		,      a_	):
			os.makedirs(a_		,      exist_ok=a_	)
			lowerCAmelCase     :    List[Any]						=   {"source": "What is love ?", "target": "life"}
			lowerCAmelCase     :    Optional[int]						=   {"train": 12, "val": 2, "test": 2}
			for split in ["train", "test", "val"]:
					for field in ["source", "target"]:
							lowerCAmelCase     :    int						=   "\n".join([contents[field]] * n_lines[split]	)
							with open(os.path.join(a_		,      F'''{split}.{field}'''	)		,      "w"	) as f:
									f.write(a_	)



	def   _lowerCamelCase    (   self		,      a_		,      a_ = "pytorch"	):
			lowerCAmelCase     :    Any						=   self.get_auto_remove_tmp_dir()
			lowerCAmelCase     :    Optional[Any]						=   os.path.join(a_		,      "output"	)
			lowerCAmelCase     :    Optional[Any]						=   os.path.join(a_		,      "data"	)
			self._create_dummy_data(data_dir=a_	)

			lowerCAmelCase     :    Any						=   F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

			if gpus > 0:
					testargs.append(F'''--gpus={gpus}'''	)
					if is_apex_available():
							testargs.append("--fp16"	)
			else:
					testargs.append("--gpus=0"	)
					testargs.append("--distributed_backend=ddp_cpu"	)
					testargs.append("--num_processes=2"	)

			lowerCAmelCase     :    Tuple						=   [sys.executable, str(Path(finetune_rag.__file__	).resolve()	)] + testargs
			execute_subprocess_async(a_		,      env=self.get_env()	)

			lowerCAmelCase     :    Dict						=   os.path.join(a_		,      "metrics.json"	)
			with open(a_	) as f:
					lowerCAmelCase     :    List[str]						=   json.load(a_	)
			return result



	@require_torch_gpu
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   self._run_finetune(gpus=1	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)



	@require_torch_multi_gpu
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self._run_finetune(gpus=2	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)



	@require_torch_gpu
	@require_ray
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self._run_finetune(gpus=1		,      distributed_retriever="ray"	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)





	@require_torch_multi_gpu
	@require_ray
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self._run_finetune(gpus=1		,      distributed_retriever="ray"	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase       		=      []






def      __A						(							a_						:  list[list[int]]      ,a_						:  int      ,a_						:  int	):
		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      __A						(							a_						:  list[list[int]]      ,a_						:  int	):
		if row >= len(a_	):
				solution.append(a_	)
				printboard(a_	)
				print()
				return True
		for i in range(len(a_	)	):
				if is_safe(a_      ,a_      ,a_	):
						lowerCAmelCase     :    Any						=   1
						solve(a_      ,row + 1	)
						lowerCAmelCase     :    List[Any]						=   0
		return False






def      __A						(							a_						:  list[list[int]]	):
		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"))
lowerCAmelCase       		=      8
lowerCAmelCase       		=      [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

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



