

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)
a_						     =    logging.getLogger()

a_						     =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any      ,      a							: int	)							->       Any:

				"""simple docstring"""

				os.makedirs(a      ,      exist_ok=a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {"source": "What is love ?", "target": "life"}
				SCREAMING_SNAKE_CASE   :      Tuple				     =       {"train": 12, "val": 2, "test": 2}
				for split in ["train", "test", "val"]:
					for field in ["source", "target"]:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "\n".join([contents[field]] * n_lines[split]	)
						with open(os.path.join(a      ,      F"{split}.{field}"	)      ,      "w"	) as f:
							f.write(a	)
			def 							__UpperCamelCase (			self							: str      ,      a							: int      ,      a							: str = "pytorch"	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.get_auto_remove_tmp_dir()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       os.path.join(a      ,      "output"	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       os.path.join(a      ,      "data"	)
				self._create_dummy_data(data_dir=a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       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"	)

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

				SCREAMING_SNAKE_CASE   :      str				     =       os.path.join(a      ,      "metrics.json"	)
				with open(a	) as f:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       json.load(a	)
				return result
			@require_torch_gpu
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self._run_finetune(gpus=1	)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]      ,      0.2	)
			@require_torch_multi_gpu
			def 							__UpperCamelCase (			self							: Dict	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self._run_finetune(gpus=2	)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]      ,      0.2	)
			@require_torch_gpu
			@require_ray
			def 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

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


def  lowerCamelCase__ (					_a = 50):
	SCREAMING_SNAKE_CASE   :      int				     =       [1] * (length + 1)

	for row_length in range(length + 1):
		for tile_length in range(2							,			5):
			for tile_start in range(row_length - tile_length + 1):
				ways_number[row_length] += ways_number[
				    row_length - tile_start - tile_length
				]

	return ways_number[length]


if __name__ == "__main__":
			print(F'''{solution() = }''')