

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						(							snake_case_			):







    """simple docstring"""





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





        '''simple docstring'''







        os.makedirs(lowercase_,exist_ok=lowercase_		)
        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(lowercase_,F'{split}.{field}'		),'w'		) as f:
                    f.write(lowercase_		)


    def 						snake_case__					(				self			:	Any,lowercase_			:	Optional[int],lowercase_			:	Optional[int] = "pytorch"		)->   Optional[int]:





        '''simple docstring'''







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

        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(lowercase_,env=self.get_env()		)

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


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





        '''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			:	Optional[Any]		)->   List[Any]:





        '''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			:	Tuple		)->   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			:	List[str]		)->   Optional[Any]:





        '''simple docstring'''







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




import datasets

from .evaluate import evaluate


lowercase_			=  "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

lowercase_			=  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

lowercase_			=  "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION			)
class 			A						(							datasets.Metric			):







    """simple docstring"""





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





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': {'id': datasets.Value('string'		), 'prediction_text': datasets.Value('string'		)},
                    'references': {
                        'id': datasets.Value('string'		),
                        'answers': datasets.features.Sequence(
                            {
                                'text': datasets.Value('string'		),
                                'answer_start': datasets.Value('int32'		),
                            }		),
                    },
                }		),codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'],reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'],)


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





        '''simple docstring'''







        A__   				=       {prediction['id']: prediction['prediction_text'] for prediction in predictions}
        A__   				=       [
            {
                'paragraphs': [
                    {
                        'qas': [
                            {
                                'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                'id': ref['id'],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        A__   				=       evaluate(dataset=lowercase_,predictions=lowercase_		)
        return score

