
"""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)
_a														=		logging.getLogger()

_a														=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class    _UpperCAmelCase(   lowerCamelCase     ):



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = "pytorch")	->	Any:






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

							_UpperCamelCase										=  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''')

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

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



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






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



		@require_torch_multi_gpu
		def 						UpperCAmelCase							( self)	->	List[Any]:






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



		@require_torch_gpu
		@require_ray
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  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)	->	Optional[Any]:






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








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






