


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)
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					logging.getLogger()

SCREAMING_SNAKE_CASE			: str				   =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class _lowerCamelCase(		_a  ):
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           os.makedirs(lowerCamelCase, exist_ok=lowerCamelCase)
           _lowercase			:				Optional[int] 						=						{'source': 'What is love ?', 'target': 'life'}
           _lowercase			:				Tuple 						=						{'train': 12, 'val': 2, 'test': 2}
           for split in ["train", "test", "val"]:
               for field in ["source", "target"]:
                   _lowercase			:				Union[str, Any] 						=						'\n'.join([contents[field]] * n_lines[split])
                   with open(os.path.join(lowerCamelCase, F'''{split}.{field}'''), 'w') as f:
                       f.write(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = "pytorch")			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Optional[Any] 						=						os.path.join(lowerCamelCase, 'output')
           _lowercase			:				Optional[Any] 						=						os.path.join(lowerCamelCase, 'data')
           self._create_dummy_data(data_dir=lowerCamelCase)

           _lowercase			:				str 						=						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')

           _lowercase			:				Tuple 						=						[sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
           execute_subprocess_async(lowerCamelCase, env=self.get_env())

           _lowercase			:				Dict 						=						os.path.join(lowerCamelCase, 'metrics.json')
           with open(lowerCamelCase) as f:
               _lowercase			:				Dict 						=						json.load(lowerCamelCase)
           return result
       @require_torch_gpu
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						self._run_finetune(gpus=1)
           self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
       @require_torch_multi_gpu
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				int 						=						self._run_finetune(gpus=2)
           self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
       @require_torch_gpu
       @require_ray
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						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)			->  Union[str, Any]:




           """simple docstring"""

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







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
