




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 (       __magic_name__ ):

			"""simple docstring"""







			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[int]:



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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = "pytorch"							)    ->      Optional[Any]:



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

							__lowerCamelCase		       =    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'							)

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

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


			@require_torch_gpu
			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self._run_finetune(gpus=1							)
							self.assertGreaterEqual(result['test'][0]['test_avg_em']			,						0.2							)


			@require_torch_multi_gpu
			def       lowercase_    (      self							)    ->      Optional[Any]:



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


			@require_torch_gpu
			@require_ray
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      int:



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








class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    n
							__lowerCamelCase		       =    [None] * self.n
							__lowerCamelCase		       =    0  # index of the first element
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    0


			def __len__(      self							)    ->      int:



							'''simple docstring'''
							return self.size


			def       lowercase_    (      self							)    ->      bool:



							'''simple docstring'''
							return self.size == 0


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							return False if self.is_empty() else self.array[self.front]


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[Any]:



							'''simple docstring'''
							if self.size >= self.n:
											raise Exception('QUEUE IS FULL'							)

							__lowerCamelCase		       =    data
							__lowerCamelCase		       =    (self.rear + 1) % self.n
							self.size += 1
							return self






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							if self.size == 0:
											raise Exception('UNDERFLOW'							)

							__lowerCamelCase		       =    self.array[self.front]
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    (self.front + 1) % self.n
							self.size -= 1
							return temp


