




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							     =					logging.getLogger()

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

class     __a(	_a  ):






    """simple docstring"""





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         os.makedirs(_SCREAMING_SNAKE_CASE  ,exist_ok=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     {'''source''': '''What is love ?''', '''target''': '''life'''}
         UpperCAmelCase_     :				str  =     {'''train''': 12, '''val''': 2, '''test''': 2}
         for split in ["train", "test", "val"]:
              for field in ["source", "target"]:
                   UpperCAmelCase_     :				List[str]  =     '''\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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = "pytorch"    )					->						Optional[int]:
         UpperCAmelCase_     :				str  =     self.get_auto_remove_tmp_dir()
         UpperCAmelCase_     :				Dict  =     os.path.join(_SCREAMING_SNAKE_CASE  ,'''output'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     os.path.join(_SCREAMING_SNAKE_CASE  ,'''data'''    )
         self._create_dummy_data(data_dir=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				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'''    )

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

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





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





    @require_torch_multi_gpu
    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Tuple  =     self._run_finetune(gpus=2    )
         self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']  ,0.2    )





    @require_torch_gpu
    @require_ray
    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Dict  =     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 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Tuple  =     self._run_finetune(gpus=1  ,distributed_retriever='''ray'''    )
         self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']  ,0.2    )





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__a							     =					logging.get_logger(__name__)

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						None:
         warnings.warn(
             '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use CLIPImageProcessor instead.'''  ,_SCREAMING_SNAKE_CASE  ,)
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )