

"""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)
lowercase_			=  logging.getLogger()

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


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        os.makedirs(_SCREAMING_SNAKE_CASE,exist_ok=_SCREAMING_SNAKE_CASE		)
        A__   				=       {'''source''': '''What is love ?''', '''target''': '''life'''}
        A__   				=       {'''train''': 1_2, '''val''': 2, '''test''': 2}
        for split in ["train", "test", "val"]:
            for field in ["source", "target"]:
                A__   				=       '''\n'''.join([contents[field]] * n_lines[split]		)
                with open(os.path.join(_SCREAMING_SNAKE_CASE,F'{split}.{field}'		),'w'		) as f:
                    f.write(_SCREAMING_SNAKE_CASE		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Tuple,lowercase_			:	int = "pytorch"		)->   List[str]:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir()
        A__   				=       os.path.join(_SCREAMING_SNAKE_CASE,'output'		)
        A__   				=       os.path.join(_SCREAMING_SNAKE_CASE,'data'		)
        self._create_dummy_data(data_dir=_SCREAMING_SNAKE_CASE		)

        A__   				=       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'		)

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

        A__   				=       os.path.join(_SCREAMING_SNAKE_CASE,'metrics.json'		)
        with open(_SCREAMING_SNAKE_CASE		) as f:
            A__   				=       json.load(_SCREAMING_SNAKE_CASE		)
        return result


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





        '''simple docstring'''







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


    @require_torch_multi_gpu
    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''simple docstring'''







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


    @require_torch_gpu
    @require_ray
    def 						snake_case__					(				self			:	Dict		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       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 						snake_case__					(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







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




from __future__ import annotations

from typing import Any
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list				)							->				int:





    '''simple docstring'''






    if not postfix_notation:
        return 0

    A__   				=       {'+', '-', '*', '/'}
    A__   				=       []

    for token in postfix_notation:
        if token in operations:
            A__				,						A__   				=       stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(SCREAMING_SNAKE_CASE__				)				)

    return stack.pop()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

