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      __lowercase					(    _A    ):

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Any   )							->		List[str]:


									'''simple docstring'''

									os.makedirs(__lowerCamelCase							,							exist_ok=__lowerCamelCase   )
									lowercase							=		{'''source''': '''What is love ?''', '''target''': '''life'''}
									lowercase							=		{'''train''': 12, '''val''': 2, '''test''': 2}
									for split in ["train", "test", "val"]:
												for field in ["source", "target"]:
															lowercase							=		'''\n'''.join([contents[field]] * n_lines[split]   )
															with open(os.path.join(__lowerCamelCase							,							f'{split}.{field}'   )							,							'''w'''   ) as f:
																		f.write(__lowerCamelCase   )

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      str = "pytorch"   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		self.get_auto_remove_tmp_dir()
									lowercase							=		os.path.join(__lowerCamelCase							,							'''output'''   )
									lowercase							=		os.path.join(__lowerCamelCase							,							'''data'''   )
									self._create_dummy_data(data_dir=__lowerCamelCase   )

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

									lowercase							=		os.path.join(__lowerCamelCase							,							'''metrics.json'''   )
									with open(__lowerCamelCase   ) as f:
												lowercase							=		json.load(__lowerCamelCase   )
									return result

						@require_torch_gpu
						def        __a		(	self				:      str   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		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]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		self._run_finetune(gpus=2   )
									self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']							,							0.2   )

						@require_torch_gpu
						@require_ray
						def        __a		(	self				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		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				:      List[str]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self._run_finetune(gpus=1							,							distributed_retriever='''ray'''   )
									self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']							,							0.2   )


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_      =      re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		_str_to_version_tuple(self.version_str   )

						def __repr__(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'

						@property
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.major, self.minor, self.patch

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return Version(__lowerCamelCase   )
									elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return other
									raise TypeError(f'{other} (type {type(__lowerCamelCase   )}) cannot be compared to version.'   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									try:
												lowercase							=		self._validate_operand(__lowerCamelCase   )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

						def __lt__(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self._validate_operand(__lowerCamelCase   )
									return self.tuple < other.tuple

						def __hash__(	self				:      int   )							->		str:


									'''simple docstring'''

									return hash(_version_tuple_to_str(self.tuple   )   )

						@classmethod
						def        __a		(	cls				:      List[str]							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		{f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )



						def        __a		(	self				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									return self.version_str






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		_VERSION_REG.match(UpperCAmelCase       )
			if not res:
						raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
			return tuple(int(UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			return ".".join(str(UpperCAmelCase       ) for v in version_tuple       )





