




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[int]				=			logging.getLogger()

__lowerCamelCase   :						List[str]				=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[int]  )						->   Union[str, Any]:
								os.makedirs(lowerCamelCase_       ,						exist_ok=lowerCamelCase_  )
								__magic_name__							:	Any    							=							{'''source''': '''What is love ?''', '''target''': '''life'''}
								__magic_name__							:	str    							=							{'''train''': 12, '''val''': 2, '''test''': 2}
								for split in ["train", "test", "val"]:
													for field in ["source", "target"]:
																		__magic_name__							:	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			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   str = "pytorch"  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							self.get_auto_remove_tmp_dir()
								__magic_name__							:	Any    							=							os.path.join(lowerCamelCase_       ,						'''output'''  )
								__magic_name__							:	List[Any]    							=							os.path.join(lowerCamelCase_       ,						'''data'''  )
								self._create_dummy_data(data_dir=lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							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'''  )

								__magic_name__							:	List[Any]    							=							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
								execute_subprocess_async(lowerCamelCase_       ,						env=self.get_env()  )

								__magic_name__							:	List[Any]    							=							os.path.join(lowerCamelCase_       ,						'''metrics.json'''  )
								with open(lowerCamelCase_  ) as f:
													__magic_name__							:	Tuple    							=							json.load(lowerCamelCase_  )
								return result

			@require_torch_gpu
			def 							UpperCAmelCase__       ( self			:   Dict  )						->   List[str]:
								__magic_name__							:	Tuple    							=							self._run_finetune(gpus=1  )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,						0.2  )

			@require_torch_multi_gpu
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[Any]:
								__magic_name__							:	str    							=							self._run_finetune(gpus=2  )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,						0.2  )

			@require_torch_gpu
			@require_ray
			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	Optional[int]    							=							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]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							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_tf_available, is_torch_available


__lowerCamelCase   :						int				=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						int				=			[
	    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwinForImageClassification''',
	    '''SwinForMaskedImageModeling''',
	    '''SwinModel''',
	    '''SwinPreTrainedModel''',
	    '''SwinBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[int]				=			[
	    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSwinForImageClassification''',
	    '''TFSwinForMaskedImageModeling''',
	    '''TFSwinModel''',
	    '''TFSwinPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



