import argparse
import json
import re
import string
import sys
from collections import Counter


def       a__      (					a							)		->	int:

       def remove_articles(a							):
              return re.sub(R'''\b(a|an|the)\b'''  ,   ''' '''  ,   a							)

       def white_space_fix(a							):
              return " ".join(text.split()							)

       def remove_punc(a							):
              A_							:       Tuple	      =				set(string.punctuation							)
              return "".join(ch for ch in text if ch not in exclude							)

       def lower(a							):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(a							)							)							)							)


def       a__      (					a  ,   a							)		->	List[Any]:
       A_							:       Any	      =				normalize_answer(a							).split()
       A_							:       Any	      =				normalize_answer(a							).split()
       A_							:       Tuple	      =				Counter(a							) & Counter(a							)
       A_							:       Dict	      =				sum(common.values()							)
       if num_same == 0:
              return 0
       A_							:       Optional[int]	      =				1.0 * num_same / len(a							)
       A_							:       Union[str, Any]	      =				1.0 * num_same / len(a							)
       A_							:       Optional[int]	      =				(2 * precision * recall) / (precision + recall)
       return fa


def       a__      (					a  ,   a							)		->	Optional[int]:
       return normalize_answer(a							) == normalize_answer(a							)


def       a__      (					a  ,   a  ,   a							)		->	Any:
       A_							:       Optional[Any]	      =				[]
       for ground_truth in ground_truths:
              A_							:       int	      =				metric_fn(a  ,   a							)
              scores_for_ground_truths.append(a							)
       return max(a							)


def       a__      (					a  ,   a							)		->	Tuple:
       A_							:       List[Any]	      =				0
       A_							:       int	      =				[]
       for passage in dataset:
              for qa in passage["qas"]:
                     total += 1
                     if qa["id"] not in predictions:
                            A_							:       int	      =				f"""Unanswered question {qa['id']} will receive score 0."""
                            print(a  ,   file=sys.stderr							)
                            continue

                     A_							:       Dict	      =				[x['''text'''] for x in qa['''answers''']]
                     A_							:       Dict	      =				predictions[qa['''id''']]

                     A_							:       List[Any]	      =				metric_max_over_ground_truths(a  ,   a  ,   a							)
                     if int(_exact_match							) == 1:
                            correct_ids.append(qa['''id''']							)
                     exact_match += _exact_match

                     fa += metric_max_over_ground_truths(a  ,   a  ,   a							)

       A_							:       List[str]	      =				exact_match / total
       A_							:       Optional[int]	      =				fa / total

       return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
    _lowerCAmelCase        =       '1.0'
    _lowerCAmelCase        =       argparse.ArgumentParser('Official evaluation script for ReCoRD v1.0.')
    parser.add_argument('data_file', help='The dataset file in JSON format.')
    parser.add_argument('pred_file', help='The model prediction file in JSON format.')
    parser.add_argument('--output_correct_ids', action='store_true', help='Output the correctly answered query IDs.')
    _lowerCAmelCase        =       parser.parse_args()

    with open(args.data_file) as data_file:
        _lowerCAmelCase        =       json.load(data_file)
        if dataset_json["version"] != expected_version:
            print(
                F'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
                file=sys.stderr,
            )
        _lowerCAmelCase        =       dataset_json['data']

    with open(args.pred_file) as pred_file:
        _lowerCAmelCase        =       json.load(pred_file)

    _lowerCAmelCase  ,			_lowerCAmelCase        =       evaluate(dataset, predictions)

    if args.output_correct_ids:
        print(F'Output {len(correct_ids)} correctly answered question IDs.')
        with open('correct_ids.json', 'w') as f:
            json.dump(correct_ids, f)



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				tempfile.mkdtemp()

        A_							:       str	      =				CLIPImageProcessor()
        A_							:       List[Any]	      =				BertTokenizer.from_pretrained(
            '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']      )

        A_							:       Optional[int]	      =				GitProcessor(__magic_name__ ,       __magic_name__      )

        processor.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).tokenizer
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).image_processor
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        shutil.rmtree(self.tmpdirname      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				[np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta      )]

        A_							:       Union[str, Any]	      =				[Image.fromarray(np.moveaxis(__magic_name__ ,       0 ,       -1      )      ) for x in image_inputs]

        return image_inputs
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        A_							:       int	      =				self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''      )
        A_							:       str	      =				self.get_image_processor(do_normalize=__magic_name__ ,       padding_value=1.0      )

        A_							:       int	      =				GitProcessor.from_pretrained(
            self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=__magic_name__ ,       padding_value=1.0      )

        self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()      )
        self.assertIsInstance(processor.tokenizer ,       __magic_name__      )

        self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()      )
        self.assertIsInstance(processor.image_processor ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       str	      =				self.prepare_image_inputs()

        A_							:       List[str]	      =				image_processor(__magic_name__ ,       return_tensors='''np'''      )
        A_							:       int	      =				processor(images=__magic_name__ ,       return_tensors='''np'''      )

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1e-2      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       str	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''

        A_							:       Tuple	      =				processor(text=__magic_name__      )

        A_							:       List[Any]	      =				tokenizer(__magic_name__ ,       return_token_type_ids=__magic_name__      )

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				'''lower newer'''
        A_							:       Dict	      =				self.prepare_image_inputs()

        A_							:       Union[str, Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )

        # test if it raises when no input is passed
        with pytest.raises(__magic_name__      ):
               processor()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A_							:       List[Any]	      =				processor.batch_decode(__magic_name__      )
        A_							:       Dict	      =				tokenizer.batch_decode(__magic_name__      )

        self.assertListEqual(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       str	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''
        A_							:       List[str]	      =				self.prepare_image_inputs()

        A_							:       Optional[Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )


