




'''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)
_SCREAMING_SNAKE_CASE							:       Any						=			logging.getLogger()

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




class 			_snake_case		(     lowercase_							):




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

										'''simple docstring'''




										os.makedirs(a__    ,							exist_ok=a__							)
										snake_case_      =				{"source": "What is love ?", "target": "life"}
										snake_case_      =				{"train": 12, "val": 2, "test": 2}
										for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																								snake_case_      =				"\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"							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				self.get_auto_remove_tmp_dir()
										snake_case_      =				os.path.join(a__    ,							"output"							)
										snake_case_      =				os.path.join(a__    ,							"data"							)
										self._create_dummy_data(data_dir=a__							)

										snake_case_      =				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"							)

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

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




			@require_torch_gpu
			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




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




			@require_torch_multi_gpu
			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




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




			@require_torch_gpu
			@require_ray
			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				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							)    ->    Any:

										'''simple docstring'''




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










'''simple docstring'''





from __future__ import annotations

import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				np.shape(snake_case					)
							if rows != columns:
														snake_case_      =				(
														    "'table' has to be of square shaped array but got a "
														    f'{rows}x{columns} array:\n{table}'
														)
														raise ValueError(snake_case					)

							snake_case_      =				np.zeros((rows, columns)					)
							snake_case_      =				np.zeros((rows, columns)					)
							for i in range(snake_case					):
														for j in range(snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					if upper[j][j] == 0:
																												raise ArithmeticError("No LU decomposition exists"					)
																					snake_case_      =				(table[i][j] - total) / upper[j][j]
														snake_case_      =				1
														for j in range(snake_case							,    snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					snake_case_      =				table[i][j] - total
							return lower, upper


if __name__ == "__main__":
		import doctest

		doctest.testmod()




