



'''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      						=       logging.getLogger()

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

class __snake_case( __snake_case   ):


						'''simple docstring'''



						def 							__snake_case       (   self      ,			A_ )      ->      List[str]:
							os.makedirs(A_      ,			exist_ok=A_ )
							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(A_      ,			f'{split}.{field}' )      ,			"""w""" ) as f:
										f.write(A_ )


						def 							__snake_case       (   self      ,			A_      ,			A_ = "pytorch" )      ->      Tuple:
							lowerCAmelCase       =      self.get_auto_remove_tmp_dir()
							lowerCAmelCase       =      os.path.join(A_      ,			"""output""" )
							lowerCAmelCase       =      os.path.join(A_      ,			"""data""" )
							self._create_dummy_data(data_dir=A_ )

							lowerCAmelCase       =      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""" )

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

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


						@require_torch_gpu
						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      self._run_finetune(gpus=1 )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,			0.2 )


						@require_torch_multi_gpu
						def 							__snake_case       (   self )      ->      int:
							lowerCAmelCase       =      self._run_finetune(gpus=2 )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,			0.2 )


						@require_torch_gpu
						@require_ray
						def 							__snake_case       (   self )      ->      Optional[int]:
							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 							__snake_case       (   self )      ->      Optional[Any]:
							lowerCAmelCase       =      self._run_finetune(gpus=1      ,			distributed_retriever="""ray""" )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,			0.2 )






def 						A_							(	_lowerCAmelCase = 1000	)		->					int:
 UpperCamelCase      :   Optional[int]			      =  -1
 UpperCamelCase      :   int			      =  0
 for a in range(1				,  n // 3	):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  UpperCamelCase      :   Optional[Any]			      =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  UpperCamelCase      :   List[str]			      =  n - a - b
  if c * c == (a * a + b * b):
   UpperCamelCase      :   Union[str, Any]			      =  a * b * c
   if candidate >= product:
    UpperCamelCase      :   Tuple			      =  candidate
 return product


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