"""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    :							str													=						logging.getLogger()

__lowerCAmelCase    :							Tuple													=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class       a_      (     __UpperCamelCase			):




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     str = "pytorch"     ):
							lowerCAmelCase__        =   self.get_auto_remove_tmp_dir()
							lowerCAmelCase__        =   os.path.join(snake_case__  ,						"""output"""     )
							lowerCAmelCase__        =   os.path.join(snake_case__  ,						"""data"""     )
							self._create_dummy_data(data_dir=snake_case__     )

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

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




					@require_torch_gpu
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=1     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )




					@require_torch_multi_gpu
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=2     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )




					@require_torch_gpu
					@require_ray
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self._run_finetune(gpus=1  ,						distributed_retriever="""ray"""     )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]  ,						0.2     )



"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__ = 50     ):





		"""simple docstring"""




		lowerCAmelCase__        =   [[0] * 3 for _ in range(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     ):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]     )


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




