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							:      str      							=logging.getLogger()

_lowercase							:      Optional[int]      							=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					str = "pytorch"     )    ->				Optional[int]:
         A						:     Tuple         =self.get_auto_remove_tmp_dir()
         A						:     List[Any]         =os.path.join(SCREAMING_SNAKE_CASE__      ,	'output'     )
         A						:     List[str]         =os.path.join(SCREAMING_SNAKE_CASE__      ,	'data'     )
         self._create_dummy_data(data_dir=SCREAMING_SNAKE_CASE__     )

         A						:     int         =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'     )

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

         A						:     Optional[int]         =os.path.join(SCREAMING_SNAKE_CASE__      ,	'metrics.json'     )
         with open(SCREAMING_SNAKE_CASE__     ) as f:
                A						:     int         =json.load(SCREAMING_SNAKE_CASE__     )
         return result







  @require_torch_gpu
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Dict:
         A						:     Any         =self._run_finetune(gpus=1     )
         self.assertGreaterEqual(result['test'][0]['test_avg_em']      ,	0.2     )







  @require_torch_multi_gpu
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Dict:
         A						:     Union[str, Any]         =self._run_finetune(gpus=2     )
         self.assertGreaterEqual(result['test'][0]['test_avg_em']      ,	0.2     )







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







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def A__  (			*lowercase:     Tuple,		lowercase:     Optional[Union[Dict, Any]] = None,		lowercase:     Dict=True,		lowercase:     Any=2					)				->		List[Any]:
       from .. import __version__

       A						:     Optional[Any]         =take_from
       A						:     Union[str, Any]         =()
       if not isinstance(args[0],		lowercase					):
              A						:     List[str]         =(args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(lowercase					).base_version					) >= version.parse(lowercase					):
                     raise ValueError(
                         F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                         F' version {__version__} is >= {version_name}'					)

              A						:     Tuple         =None
              if isinstance(lowercase,		lowercase					) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowercase					),)
                     A						:     Union[str, Any]         =F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(lowercase,		lowercase					):
                     values += (getattr(lowercase,		lowercase					),)
                     A						:     Optional[Any]         =F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     A						:     List[Any]         =F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     A						:     List[Any]         =warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message,		lowercase,		stacklevel=lowercase					)

       if isinstance(lowercase,		lowercase					) and len(lowercase					) > 0:
              A						:     Any         =inspect.getouterframes(inspect.currentframe()					)[1]
              A						:     int         =call_frame.filename
              A						:     int         =call_frame.lineno
              A						:     Optional[int]         =call_frame.function
              A  ,  A						:     int         =next(iter(deprecated_kwargs.items()					)					)
              raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'					)

       if len(lowercase					) == 0:
              return
       elif len(lowercase					) == 1:
              return values[0]
       return values






