





'''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       		=      logging.getLogger()

lowerCAmelCase       		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 		lowerCamelCase  (      _A       ):



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



	def   _lowerCamelCase    (   self		,      a_		,      a_ = "pytorch"	):
			lowerCAmelCase     :    Any						=   self.get_auto_remove_tmp_dir()
			lowerCAmelCase     :    Optional[Any]						=   os.path.join(a_		,      "output"	)
			lowerCAmelCase     :    Optional[Any]						=   os.path.join(a_		,      "data"	)
			self._create_dummy_data(data_dir=a_	)

			lowerCAmelCase     :    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"	)

			lowerCAmelCase     :    Tuple						=   [sys.executable, str(Path(finetune_rag.__file__	).resolve()	)] + testargs
			execute_subprocess_async(a_		,      env=self.get_env()	)

			lowerCAmelCase     :    Dict						=   os.path.join(a_		,      "metrics.json"	)
			with open(a_	) as f:
					lowerCAmelCase     :    List[str]						=   json.load(a_	)
			return result



	@require_torch_gpu
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   self._run_finetune(gpus=1	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)



	@require_torch_multi_gpu
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self._run_finetune(gpus=2	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)



	@require_torch_gpu
	@require_ray
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Any						=   self._run_finetune(gpus=1		,      distributed_retriever="ray"	)
			self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,      0.2	)










'''simple docstring'''






from collections import namedtuple

lowerCAmelCase       		=      namedtuple("""from_to""", """from_ to""")

lowerCAmelCase       		=      {
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 10_00),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00454, 264.172),
    """cubicyard""": from_to(0.76455, 1.30795),
    """cubicfoot""": from_to(0.028, 35.3147),
    """cup""": from_to(0.000236588, 4226.75),
}






def      __A						(							a_						:  float      ,a_						:  str      ,a_						:  str	):
		if from_type not in METRIC_CONVERSION:
				raise ValueError(
				    f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
				    + ", ".join(a_	)	)
		if to_type not in METRIC_CONVERSION:
				raise ValueError(
				    f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
				    + ", ".join(a_	)	)
		return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()



