






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a       :	int             = {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Any             = [
	    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CLIPSegModel',
	    'CLIPSegPreTrainedModel',
	    'CLIPSegTextModel',
	    'CLIPSegVisionModel',
	    'CLIPSegForImageSegmentation',
	]

if TYPE_CHECKING:
	from .configuration_clipseg import (
	    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPSegConfig,
	    CLIPSegTextConfig,
	    CLIPSegVisionConfig,
	)
	from .processing_clipseg import CLIPSegProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clipseg import (
		    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPSegForImageSegmentation,
		    CLIPSegModel,
		    CLIPSegPreTrainedModel,
		    CLIPSegTextModel,
		    CLIPSegVisionModel,
		)

else:
	import sys

	__a       :	Tuple             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










"""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       :	Optional[int]             = logging.getLogger()

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  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'''    )

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

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




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

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




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  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:     str    ):

												'''simple docstring'''

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


