'''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)
__a:			Any	       =       logging.getLogger()

__a:			List[str]	       =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class       UpperCAmelCase  (						lowercase_ ):





       '''simple docstring'''



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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = "pytorch"			)  ->  Optional[int]:
        lowercase__    :   Optional[Any]						=				self.get_auto_remove_tmp_dir()
        lowercase__    :   List[str]						=				os.path.join(a__     ,       '''output'''			)
        lowercase__    :   Any						=				os.path.join(a__     ,       '''data'''			)
        self._create_dummy_data(data_dir=a__			)

        lowercase__    :   Dict						=				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'''			)

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

        lowercase__    :   Dict						=				os.path.join(a__     ,       '''metrics.json'''			)
        with open(a__			) as f:
         lowercase__    :   List[str]						=				json.load(a__			)
        return result

       @require_torch_gpu
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Optional[int]						=				self._run_finetune(gpus=1			)
        self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']     ,       0.2			)

       @require_torch_multi_gpu
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Any						=				self._run_finetune(gpus=2			)
        self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']     ,       0.2			)

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


'''simple docstring'''

import numpy as np
def       __UpperCamelCase	(       UpperCAmelCase					):
 return 1 / (1 + np.exp(-vector					))
def       __UpperCamelCase	(       UpperCAmelCase					):
 return vector * sigmoid(UpperCAmelCase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

