


"""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)
__snake_case    					=					logging.getLogger()

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

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = "pytorch"						)      ->							List[Any]:
    lowercase__       :       Union[str, Any]				=						self.get_auto_remove_tmp_dir()
    lowercase__       :       Optional[Any]				=						os.path.join(lowerCamelCase__ ,     """output"""						)
    lowercase__       :       str				=						os.path.join(lowerCamelCase__ ,     """data"""						)
    self._create_dummy_data(data_dir=lowerCamelCase__						)

    lowercase__       :       Optional[int]				=						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__       :       Union[str, Any]				=						[sys.executable, str(Path(finetune_rag.__file__						).resolve()						)] + testargs
    execute_subprocess_async(lowerCamelCase__ ,     env=self.get_env()						)

    lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """metrics.json"""						)
    with open(lowerCamelCase__						) as f:
     lowercase__       :       Dict				=						json.load(lowerCamelCase__						)
    return result


   @require_torch_gpu
   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       str				=						self._run_finetune(gpus=1						)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] ,     0.2						)


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


   @require_torch_gpu
   @require_ray
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       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 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       List[str]				=						self._run_finetune(gpus=1 ,     distributed_retriever="""ray"""						)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] ,     0.2						)



"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()