





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)
_UpperCAmelCase							:			List[Any]        =							logging.getLogger()

_UpperCAmelCase							:			List[Any]        =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Optional[Any]      )     ->      str:
     os.makedirs(UpperCAmelCase     ,	exist_ok=UpperCAmelCase      )
     lowerCamelCase__       : str   					=						{'source': 'What is love ?', 'target': 'life'}
     lowerCamelCase__       : Optional[Any]   					=						{'train': 12, 'val': 2, 'test': 2}
     for split in ["train", "test", "val"]:
         for field in ["source", "target"]:
             lowerCamelCase__       : Tuple   					=						'\n'.join([contents[field]] * n_lines[split]      )
             with open(os.path.join(UpperCAmelCase     ,	F"""{split}.{field}"""      )     ,	'w'      ) as f:
                 f.write(UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : str = "pytorch"      )     ->      Optional[int]:
     lowerCamelCase__       : Dict   					=						self.get_auto_remove_tmp_dir()
     lowerCamelCase__       : Union[str, Any]   					=						os.path.join(UpperCAmelCase     ,	'output'      )
     lowerCamelCase__       : Tuple   					=						os.path.join(UpperCAmelCase     ,	'data'      )
     self._create_dummy_data(data_dir=UpperCAmelCase      )

     lowerCamelCase__       : int   					=						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__       : Optional[Any]   					=						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
     execute_subprocess_async(UpperCAmelCase     ,	env=self.get_env()      )

     lowerCamelCase__       : Optional[Any]   					=						os.path.join(UpperCAmelCase     ,	'metrics.json'      )
     with open(UpperCAmelCase      ) as f:
         lowerCamelCase__       : List[Any]   					=						json.load(UpperCAmelCase      )
     return result
 @require_torch_gpu
 def 	A_      ( self    : Any      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						self._run_finetune(gpus=1      )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']     ,	0.2      )
 @require_torch_multi_gpu
 def 	A_      ( self    : Any      )     ->      Optional[int]:
     lowerCamelCase__       : str   					=						self._run_finetune(gpus=2      )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']     ,	0.2      )
 @require_torch_gpu
 @require_ray
 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : str   					=						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    : str      )     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						self._run_finetune(gpus=1     ,	distributed_retriever='ray'      )
     self.assertGreaterEqual(result['test'][0]['test_avg_em']     ,	0.2      )












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						max(self.process.memory_info().rss     ,	self.cpu_memory_peak      )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break
 def 	A_      ( self    : Tuple      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : List[str]   					=						(torch.cuda.memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20
        lowerCamelCase__       : Optional[Any]   					=						(torch.cuda.max_memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    print(F"""{description}:"""					)
    print(F"""- Time: {measures["time"]:.2f}s"""					)
    for i in range(torch.cuda.device_count()					):
        print(F"""- GPU {i} allocated: {measures[str(_UpperCAmelCase					)]:.2f}MiB"""					)
        lowerCamelCase__       : List[str]   					=						measures[F"""{i}-peak"""]
        print(F"""- GPU {i} peak: {peak:.2f}MiB"""					)
    print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""					)
    print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""					)





