
"""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)
UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger()

UpperCAmelCase__   :					Any												=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = "pytorch"					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__		: int     =     os.path.join(SCREAMING_SNAKE_CASE__     , """output"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(SCREAMING_SNAKE_CASE__     , """data"""					)
											self._create_dummy_data(data_dir=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: 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"""					)

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

											SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(SCREAMING_SNAKE_CASE__     , """metrics.json"""					)
											with open(SCREAMING_SNAKE_CASE__					) as f:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     json.load(SCREAMING_SNAKE_CASE__					)
											return result



				@require_torch_gpu
				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




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



				@require_torch_multi_gpu
				def 			__magic_name__					(self					)      ->	Tuple:



											"""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 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     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 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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






"""simple docstring"""




def lowercase_						(     _snake_case     ,_snake_case		):
							return int((input_a, input_a).count(1		) != 0		)

def lowercase_						(     ):
							assert or_gate(0     ,0		) == 0
							assert or_gate(0     ,1		) == 1
							assert or_gate(1     ,0		) == 1
							assert or_gate(1     ,1		) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))




