code: 
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]				):
  
         def remove_articles(lowercase_:	Optional[int]				):
                return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)
  
         def white_space_fix(lowercase_:	Optional[Any]				):
                return " ".join(text.split()				)
  
         def remove_punc(lowercase_:	Tuple				):
                _UpperCamelCase     :       List[Any]  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[Any]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]		,						lowercase_:	Optional[Any]				):
         _UpperCamelCase     :       Optional[int]  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Union[str, Any]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       List[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Any  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Optional[int]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Union[str, Any]		,						lowercase_:	int				):
         _UpperCamelCase     :       Any  						=  []
         for ground_truth in ground_truths:
                _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
                scores_for_ground_truths.append(lowercase_				)
         return max(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	Optional[int]				):
         _UpperCamelCase     :       List[Any]  						=  0
         _UpperCamelCase     :       Tuple  						=  []
         for passage in dataset:
                for qa in passage["qas"]:
                       total += 1
                       if qa["id"] not in predictions:
                              _UpperCamelCase     :       Optional[Any]  						=  F"Unanswered question {qa['id']} will receive score 0."
                              print(lowercase_		,						file=sys.stderr				)
                              continue
  
                       _UpperCamelCase     :       List[Any]  						=  [x['text'] for x in qa['answers']]
                       _UpperCamelCase     :       Any  						=  predictions[qa['id']]
  
                       _UpperCamelCase     :       Any  						=  metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                       if int(_exact_match				) == 1:
                              correct_ids.append(qa['id']				)
                       exact_match += _exact_match
  
                       fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Tuple  						=  exact_match / total
         _UpperCamelCase     :       str  						=  fa / total
  
         return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.0'''
   UpperCAmelCase					=				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.''')
   UpperCAmelCase					=				parser.parse_args()
  
   with open(args.data_file) as data_file:
    UpperCAmelCase					=				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,
     )
    UpperCAmelCase					=				dataset_json['''data''']
  
   with open(args.pred_file) as pred_file:
    UpperCAmelCase					=				json.load(pred_file)
  
   UpperCAmelCase		,					UpperCAmelCase					=				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)
  
  
  
  
style_context: import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin
  
  
  if is_torch_available():
   import torch
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import TvltImageProcessor
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Optional[int]=10		,						lowercase_:	Optional[Any]=10		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Any=False				):
         _UpperCamelCase     :       List[Any]  						=  []
         for i in range(image_processor_tester.num_frames				):
                video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta				)				)
  
         if not numpify and not torchify:
                # PIL expects the channel dimension as last dimension
                _UpperCamelCase     :       Tuple  						=  [Image.fromarray(np.moveaxis(lowercase_		,						0		,						-1				)				) for frame in video]
  
         if torchify:
                _UpperCamelCase     :       Union[str, Any]  						=  [torch.from_numpy(lowercase_				) for frame in video]
  
         return video
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Tuple=False		,						lowercase_:	Union[str, Any]=False				):
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
         _UpperCamelCase     :       Any  						=  []
         for i in range(image_processor_tester.batch_size				):
                if equal_resolution:
                       _UpperCamelCase     :       str  						=  image_processor_tester.max_resolution
                else:
                       _UpperCamelCase						,      _UpperCamelCase     :       int  						=  np.random.choice(
                           np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution				)		,						2				)
                       _UpperCamelCase     :       Optional[Any]  						=  prepare_video(
                           image_processor_tester=lowercase_		,						width=lowercase_		,						height=lowercase_		,						numpify=lowercase_		,						torchify=lowercase_		,						)
                video_inputs.append(lowercase_				)
  
         return video_inputs
  
  
  
  
  class    __lowerCAmelCase  (       unittest.TestCase						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]=7       ,			lowerCamelCase       :		List[Any]=3       ,			lowerCamelCase       :		str=4       ,			lowerCamelCase       :		Union[str, Any]=1_8       ,			lowerCamelCase       :		str=3_0       ,			lowerCamelCase       :		List[str]=4_0_0       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		Union[str, Any]=None       ,			lowerCamelCase       :		List[str]=True       ,			lowerCamelCase       :		Optional[Any]=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		Any=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		str=True       ,			lowerCamelCase       :		Optional[Any]=None       ,			) ->							Union[str, Any]:
          _UpperCamelCase     :       Optional[int]  						=  size if size is not None else {'shortest_edge': 1_8}
          _UpperCamelCase     :       Dict  						=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
  
          _UpperCamelCase     :       Any  						=  parent
          _UpperCamelCase     :       Optional[Any]  						=  batch_size
          _UpperCamelCase     :       List[Any]  						=  num_channels
          _UpperCamelCase     :       List[Any]  						=  num_frames
          _UpperCamelCase     :       Union[str, Any]  						=  image_size
          _UpperCamelCase     :       Dict  						=  min_resolution
          _UpperCamelCase     :       int  						=  max_resolution
          _UpperCamelCase     :       Union[str, Any]  						=  do_resize
          _UpperCamelCase     :       str  						=  size
          _UpperCamelCase     :       Dict  						=  do_normalize
          _UpperCamelCase     :       Union[str, Any]  						=  image_mean
          _UpperCamelCase     :       Optional[int]  						=  image_std
          _UpperCamelCase     :       Tuple  						=  do_center_crop
          _UpperCamelCase     :       Any  						=  crop_size
  
   def 					__snake_case					(			self       :		Tuple  ) ->							Optional[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class    __lowerCAmelCase  (       a_,	unittest.TestCase						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Any				    = TvltImageProcessor if is_vision_available() else None
  
   def 					__snake_case					(			self       :		int  ) ->							Optional[int]:
          _UpperCamelCase     :       Optional[Any]  						=  TvltImageProcessorTester(self  )
  
   @property
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
          return self.image_processor_tester.prepare_image_processor_dict()
  
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							str:
          _UpperCamelCase     :       Tuple  						=  self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'image_mean'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'image_std'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'do_normalize'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'do_resize'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'do_center_crop'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'size'  )  )
  
   def 					__snake_case					(			self       :		Dict  ) ->							Optional[int]:
          # Initialize image_processor
          _UpperCamelCase     :       str  						=  self.image_processing_class(**self.image_processor_dict  )
          # create random PIL videos
          _UpperCamelCase     :       int  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase  )
          for video in video_inputs:
                 self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
                 self.assertIsInstance(video[0]       ,			Image.Image  )
  
          # Test not batched input
          _UpperCamelCase     :       Dict  						=  image_processor(video_inputs[0]       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,			(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
          # Test batched
          _UpperCamelCase     :       Any  						=  image_processor(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							List[str]:
          # Initialize image_processor
          _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          _UpperCamelCase     :       int  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			numpify=lowerCamelCase  )
          for video in video_inputs:
                 self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
                 self.assertIsInstance(video[0]       ,			np.ndarray  )
  
          # Test not batched input
          _UpperCamelCase     :       Optional[int]  						=  image_processor(video_inputs[0]       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,			(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
          # Test batched
          _UpperCamelCase     :       int  						=  image_processor(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
          # Initialize image_processor
          _UpperCamelCase     :       int  						=  self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          _UpperCamelCase     :       Optional[Any]  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			torchify=lowerCamelCase  )
          for video in video_inputs:
                 self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
                 self.assertIsInstance(video[0]       ,			torch.Tensor  )
  
          # Test not batched input
          _UpperCamelCase     :       Dict  						=  image_processor(video_inputs[0]       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,			(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
          # Test batched
          _UpperCamelCase     :       Optional[int]  						=  image_processor(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
  
  
  