




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)
lowercase__							:	str											=		logging.getLogger()

lowercase__							:	Optional[int]											=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	int			,      __lowercase				:	Optional[Any]		):






							"""simple docstring"""






							os.makedirs(__lowercase			,      exist_ok=__lowercase		)
							snake_case_						       =	{"source": "What is love ?", "target": "life"}
							snake_case_						       =	{"train": 12, "val": 2, "test": 2}
							for split in ["train", "test", "val"]:
								for field in ["source", "target"]:
									snake_case_						       =	"\n".join([contents[field]] * n_lines[split]		)
									with open(os.path.join(__lowercase			,      f"{split}.{field}"		)			,      "w"		) as f:
										f.write(__lowercase		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	int			,      __lowercase				:	str = "pytorch"		):






							"""simple docstring"""






							snake_case_						       =	self.get_auto_remove_tmp_dir()
							snake_case_						       =	os.path.join(__lowercase			,      "output"		)
							snake_case_						       =	os.path.join(__lowercase			,      "data"		)
							self._create_dummy_data(data_dir=__lowercase		)

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

							snake_case_						       =	[sys.executable, str(Path(finetune_rag.__file__		).resolve()		)] + testargs
							execute_subprocess_async(__lowercase			,      env=self.get_env()		)

							snake_case_						       =	os.path.join(__lowercase			,      "metrics.json"		)
							with open(__lowercase		) as f:
								snake_case_						       =	json.load(__lowercase		)
							return result






						@require_torch_gpu
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self._run_finetune(gpus=1		)
							self.assertGreaterEqual(result["test"][0]["test_avg_em"]			,      0.2		)






						@require_torch_multi_gpu
						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self._run_finetune(gpus=2		)
							self.assertGreaterEqual(result["test"][0]["test_avg_em"]			,      0.2		)






						@require_torch_gpu
						@require_ray
						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	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 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self._run_finetune(gpus=1			,      distributed_retriever="ray"		)
							self.assertGreaterEqual(result["test"][0]["test_avg_em"]			,      0.2		)






def 		lowerCamelCase__       (						):







	'''simple docstring'''





	for n in range(1	,      1000000				):
		yield n * (n + 1) // 2


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	1
	snake_case_						       =	2
	while i * i <= n:
		snake_case_						       =	0
		while n % i == 0:
			n //= i
			multiplicity += 1
		divisors_count *= multiplicity + 1
		i += 1
	if n > 1:
		divisors_count *= 2
	return divisors_count


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	return next(i for i in triangle_number_generator() if count_divisors(_A				) > 500				)


if __name__ == "__main__":
		print(solution())
