
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 					lowerCamelCase		(  A_  ):





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   int       )						->						List[str]:
         os.makedirs(_A    ,   exist_ok=_A       )
         snake_case								= {"source": "What is love ?", "target": "life"}
         snake_case								= {"train": 1_2, "val": 2, "test": 2}
         for split in ["train", "test", "val"]:
             for field in ["source", "target"]:
                 snake_case								= "\n".join([contents[field]] * n_lines[split]       )
                 with open(os.path.join(_A    ,   f'{split}.{field}'       )    ,   "w"       ) as f:
                     f.write(_A       )





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   int    ,   _A				:   str = "pytorch"       )						->						Any:
         snake_case								= self.get_auto_remove_tmp_dir()
         snake_case								= os.path.join(_A    ,   "output"       )
         snake_case								= os.path.join(_A    ,   "data"       )
         self._create_dummy_data(data_dir=_A       )

         snake_case								= 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"       )

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

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





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





     @require_torch_multi_gpu
     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Optional[Any]:
         snake_case								= self._run_finetune(gpus=2       )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]    ,   0.2       )





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



from ..utils import DummyObject, requires_backends







class 					lowerCamelCase		(  metaclass=A_  ):
     UpperCAmelCase__    :							Union[str, Any]									=		["onnx"]





     def __init__(self				:   Tuple    ,   *_A				:   Optional[int]    ,   **_A				:   Any       )						->						Dict:
         requires_backends(self    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   int    ,   *_A				:   Dict    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         requires_backends(cls    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   Dict    ,   *_A				:   Tuple    ,   **_A				:   Optional[Any]       )						->						int:
         requires_backends(cls    ,   ["onnx"]       )

