

'''simple docstring'''
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)
lowerCamelCase :      Optional[Any]					 =     logging.getLogger()

lowerCamelCase :      Any					 =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							A__    (       A__  ):





  def 						A  (		self      :	List[str]							,					_a      :	List[str] )	->    Optional[int]:





       '''simple docstring'''






       os.makedirs(_a							,					exist_ok=_a )
       _SCREAMING_SNAKE_CASE							    ={'source': 'What is love ?', 'target': 'life'}
       _SCREAMING_SNAKE_CASE							    ={'train': 12, 'val': 2, 'test': 2}
       for split in ["train", "test", "val"]:
            for field in ["source", "target"]:
                 _SCREAMING_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 						A  (		self      :	int							,					_a      :	int							,					_a      :	str = "pytorch" )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.get_auto_remove_tmp_dir()
       _SCREAMING_SNAKE_CASE							    =os.path.join(_a							,					'output' )
       _SCREAMING_SNAKE_CASE							    =os.path.join(_a							,					'data' )
       self._create_dummy_data(data_dir=_a )

       _SCREAMING_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' )

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

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





  @require_torch_gpu
  def 						A  (		self      :	List[str] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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]:





       '''simple docstring'''






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





  @require_torch_gpu
  @require_ray
  def 						A  (		self      :	str )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_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 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






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



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				=		{'''configuration_mmbt''': ['''MMBTConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__				=		['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
			from .configuration_mmbt import MMBTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
			import sys

			a__				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



