code: 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, prepare_image_inputs
  
  
  if is_torch_available():
   import torch
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import MobileNetVaImageProcessor
  
  
  
  
  class    __lowerCAmelCase  (       unittest.TestCase						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		List[str]       ,			lowerCamelCase       :		Tuple=7       ,			lowerCamelCase       :		Union[str, Any]=3       ,			lowerCamelCase       :		List[Any]=1_8       ,			lowerCamelCase       :		Optional[Any]=3_0       ,			lowerCamelCase       :		Tuple=4_0_0       ,			lowerCamelCase       :		Union[str, Any]=True       ,			lowerCamelCase       :		List[str]=None       ,			lowerCamelCase       :		Optional[int]=True       ,			lowerCamelCase       :		int=None       ,			) ->							Any:
          _UpperCamelCase     :       Tuple  						=  size if size is not None else {'shortest_edge': 2_0}
          _UpperCamelCase     :       List[str]  						=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
          _UpperCamelCase     :       Union[str, Any]  						=  parent
          _UpperCamelCase     :       Dict  						=  batch_size
          _UpperCamelCase     :       Any  						=  num_channels
          _UpperCamelCase     :       str  						=  image_size
          _UpperCamelCase     :       Tuple  						=  min_resolution
          _UpperCamelCase     :       Union[str, Any]  						=  max_resolution
          _UpperCamelCase     :       int  						=  do_resize
          _UpperCamelCase     :       Union[str, Any]  						=  size
          _UpperCamelCase     :       Optional[Any]  						=  do_center_crop
          _UpperCamelCase     :       List[Any]  						=  crop_size
  
   def 					__snake_case					(			self       :		List[Any]  ) ->							int:
          return {
              "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						:	List[Any]				    = MobileNetVaImageProcessor if is_vision_available() else None
  
   def 					__snake_case					(			self       :		Tuple  ) ->							Dict:
          _UpperCamelCase     :       List[Any]  						=  MobileNetVaImageProcessingTester(self  )
  
   @property
   def 					__snake_case					(			self       :		List[str]  ) ->							Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
  
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							str:
          _UpperCamelCase     :       int  						=  self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'do_resize'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'size'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'do_center_crop'  )  )
          self.assertTrue(hasattr(lowerCamelCase       ,			'center_crop'  )  )
  
   def 					__snake_case					(			self       :		Any  ) ->							Any:
          _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class.from_dict(self.image_processor_dict  )
          self.assertEqual(image_processor.size       ,			{'shortest_edge': 2_0}  )
          self.assertEqual(image_processor.crop_size       ,			{'height': 1_8, 'width': 1_8}  )
  
          _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class.from_dict(self.image_processor_dict       ,			size=4_2       ,			crop_size=8_4  )
          self.assertEqual(image_processor.size       ,			{'shortest_edge': 4_2}  )
          self.assertEqual(image_processor.crop_size       ,			{'height': 8_4, 'width': 8_4}  )
  
   def 					__snake_case					(			self       :		Optional[int]  ) ->							List[str]:
          pass
  
   def 					__snake_case					(			self       :		Any  ) ->							Any:
          # Initialize image_processing
          _UpperCamelCase     :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          _UpperCamelCase     :       Tuple  						=  prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase  )
          for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase       ,			Image.Image  )
  
          # Test not batched input
          _UpperCamelCase     :       List[str]  						=  image_processing(image_inputs[0]       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,			(
                  1,
                  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_processing(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,			(
                  self.image_processor_tester.batch_size,
                  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]  ) ->							int:
          # Initialize image_processing
          _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          _UpperCamelCase     :       Tuple  						=  prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			numpify=lowerCamelCase  )
          for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase       ,			np.ndarray  )
  
          # Test not batched input
          _UpperCamelCase     :       Union[str, Any]  						=  image_processing(image_inputs[0]       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,			(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
          # Test batched
          _UpperCamelCase     :       Optional[Any]  						=  image_processing(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
   def 					__snake_case					(			self       :		int  ) ->							Dict:
          # Initialize image_processing
          _UpperCamelCase     :       Optional[Any]  						=  self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          _UpperCamelCase     :       Any  						=  prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			torchify=lowerCamelCase  )
          for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase       ,			torch.Tensor  )
  
          # Test not batched input
          _UpperCamelCase     :       Any  						=  image_processing(image_inputs[0]       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,			(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
          # Test batched
          _UpperCamelCase     :       List[Any]  						=  image_processing(lowerCamelCase       ,			return_tensors='pt'  ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )       ,			)
  
  
  
  
style_context: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
  
         def remove_articles(lowercase_:	Dict				):
                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_:	List[str]				):
                _UpperCamelCase     :       str  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[str]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
         _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_:	Union[str, Any]		,						lowercase_:	List[Any]				):
         _UpperCamelCase     :       List[Any]  						=  0
         for article in dataset:
                for paragraph in article["paragraphs"]:
                       for qa in paragraph["qas"]:
                              total += 1
                              if qa["id"] not in predictions:
                                     _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                     print(lowercase_		,						file=sys.stderr				)
                                     continue
                              _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                              _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                              exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                              fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
         _UpperCamelCase     :       Optional[Any]  						=  100.0 * 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)))
  
  
  
  