






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   :   Tuple      =    logging.getLogger()

__UpperCamelCase   :   str      =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				Optional[int]      )		->			int:
		"""simple docstring"""







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







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				str = "pytorch"      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.get_auto_remove_tmp_dir()
		__lowercase   							=	os.path.join(_lowerCAmelCase							,					"""output"""      )
		__lowercase   							=	os.path.join(_lowerCAmelCase							,					"""data"""      )
		self._create_dummy_data(data_dir=_lowerCAmelCase      )

		__lowercase   							=	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"""      )

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

		__lowercase   							=	os.path.join(_lowerCAmelCase							,					"""metrics.json"""      )
		with open(_lowerCAmelCase      ) as f:
			__lowercase   							=	json.load(_lowerCAmelCase      )
		return result







	@require_torch_gpu
	def 			_a (	self   :				Any      )		->			Any:
		"""simple docstring"""







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







	@require_torch_multi_gpu
	def 			_a (	self   :				Tuple      )		->			str:
		"""simple docstring"""







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







	@require_torch_gpu
	@require_ray
	def 			_a (	self   :				Union[str, Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	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 			_a (	self   :				Union[str, Any]      )		->			Union[str, Any]:
		"""simple docstring"""







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





'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





