



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_								=     logging.getLogger()

lowercase_								=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 				_snake_case			(					lowercase__):




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




				def 							A__      (						self					:						int,					__lowercase					:						int,					__lowercase					:						str = "pytorch"						):
							lowercase__					  =				self.get_auto_remove_tmp_dir()
							lowercase__					  =				os.path.join(__lowercase,					"output"						)
							lowercase__					  =				os.path.join(__lowercase,					"data"						)
							self._create_dummy_data(data_dir=__lowercase						)

							lowercase__					  =				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"						)

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

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




				@require_torch_gpu
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self._run_finetune(gpus=1						)
							self.assertGreaterEqual(result["test"][0]["test_avg_em"],					0.2						)




				@require_torch_multi_gpu
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self._run_finetune(gpus=2						)
							self.assertGreaterEqual(result["test"][0]["test_avg_em"],					0.2						)




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









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




