





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




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:

       def remove_articles(UpperCamelCase_	):
              return re.sub(r"\b(a|an|the)\b"       ,		" "       ,		UpperCamelCase_	)

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

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

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

       return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_	)	)	)	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	normalize_answer(UpperCamelCase_	).split()
       UpperCamelCase_ 							=	normalize_answer(UpperCamelCase_	).split()
       UpperCamelCase_ 							=	Counter(UpperCamelCase_	) & Counter(UpperCamelCase_	)
       UpperCamelCase_ 							=	sum(common.values()	)
       if num_same == 0:
              return 0
       UpperCamelCase_ 							=	1.0 * num_same / len(UpperCamelCase_	)
       UpperCamelCase_ 							=	1.0 * num_same / len(UpperCamelCase_	)
       UpperCamelCase_ 							=	(2 * precision * recall) / (precision + recall)
       return fa




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       return normalize_answer(UpperCamelCase_	) == normalize_answer(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	[]
       for ground_truth in ground_truths:
              UpperCamelCase_ 							=	metric_fn(UpperCamelCase_       ,		UpperCamelCase_	)
              scores_for_ground_truths.append(UpperCamelCase_	)
       return max(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	UpperCamelCase_ 							=	UpperCamelCase_ 							=	0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   UpperCamelCase_ 							=	"Unanswered question " + qa["id"] + " will receive score 0."
                                   print(UpperCamelCase_       ,		file=sys.stderr	)
                                   continue
                            UpperCamelCase_ 							=	[x["text"] for x in qa["answers"]]
                            UpperCamelCase_ 							=	predictions[qa["id"]]
                            exact_match += metric_max_over_ground_truths(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
                            fa += metric_max_over_ground_truths(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       UpperCamelCase_ 							=	1_00.0 * exact_match / total
       UpperCamelCase_ 							=	1_00.0 * fa / total

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


if __name__ == "__main__":
     _UpperCAmelCase										=					'1.1'
     _UpperCAmelCase										=					argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
     parser.add_argument('dataset_file', help='Dataset file')
     parser.add_argument('prediction_file', help='Prediction File')
     _UpperCAmelCase										=					parser.parse_args()
     with open(args.dataset_file) as dataset_file:
          _UpperCAmelCase										=					json.load(dataset_file)
          if dataset_json["version"] != expected_version:
               print(
                   'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
                   file=sys.stderr,
               )
          _UpperCAmelCase										=					dataset_json['data']
     with open(args.prediction_file) as prediction_file:
          _UpperCAmelCase										=					json.load(prediction_file)
     print(json.dumps(evaluate(dataset, predictions)))


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''']      )


