

'''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)
UpperCamelCase_    						=       logging.getLogger()

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





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										os.makedirs(UpperCAmelCase__		,					exist_ok=UpperCAmelCase__   )
										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 :							Any    		='''\n'''.join([contents[field]] * n_lines[split]   )
																						with open(os.path.join(UpperCAmelCase__		,					F'''{split}.{field}'''   )		,					'''w'''   ) as f:
																												f.write(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		str = "pytorch"   ):
										'''simple docstring'''
										lowercase :							Any    		=self.get_auto_remove_tmp_dir()
										lowercase :							List[Any]    		=os.path.join(UpperCAmelCase__		,					'''output'''   )
										lowercase :							List[Any]    		=os.path.join(UpperCAmelCase__		,					'''data'''   )
										self._create_dummy_data(data_dir=UpperCAmelCase__   )

										lowercase :							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'''   )

										lowercase :							Dict    		=[sys.executable, str(Path(finetune_rag.__file__   ).resolve()   )] + testargs
										execute_subprocess_async(UpperCAmelCase__		,					env=self.get_env()   )

										lowercase :							Tuple    		=os.path.join(UpperCAmelCase__		,					'''metrics.json'''   )
										with open(UpperCAmelCase__   ) as f:
																lowercase :							Optional[Any]    		=json.load(UpperCAmelCase__   )
										return result







				@require_torch_gpu
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self._run_finetune(gpus=1   )
										self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,					0.2   )







				@require_torch_multi_gpu
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self._run_finetune(gpus=2   )
										self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,					0.2   )







				@require_torch_gpu
				@require_ray
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							List[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  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self._run_finetune(gpus=1		,					distributed_retriever='''ray'''   )
										self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,					0.2   )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





