



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)
__snake_case    :		List[str]  =logging.getLogger()

__snake_case    :		int  =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 		lowerCamelCase__      (							__UpperCamelCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = "pytorch"			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.get_auto_remove_tmp_dir()
						lowerCAmelCase__					:    int												=					os.path.join(__lowerCamelCase		,'''output'''			)
						lowerCAmelCase__					:    int												=					os.path.join(__lowerCamelCase		,'''data'''			)
						self._create_dummy_data(data_dir=__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					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__					:    Optional[Any]												=					[sys.executable, str(Path(finetune_rag.__file__			).resolve()			)] + testargs
						execute_subprocess_async(__lowerCamelCase		,env=self.get_env()			)

						lowerCAmelCase__					:    Dict												=					os.path.join(__lowerCamelCase		,'''metrics.json'''			)
						with open(__lowerCamelCase			) as f:
										lowerCAmelCase__					:    Dict												=					json.load(__lowerCamelCase			)
						return result



		@require_torch_gpu
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self._run_finetune(gpus=1			)
						self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,0.2			)



		@require_torch_multi_gpu
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self._run_finetune(gpus=2			)
						self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,0.2			)



		@require_torch_gpu
		@require_ray
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					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 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self._run_finetune(gpus=1		,distributed_retriever='''ray'''			)
						self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,0.2			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


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

