



import argparse
from collections import defaultdict
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str				)->List[str]:
      _lowerCAmelCase 			=  f'''{file}_{class_name}_{test_name}'''
      done_test[_id] += 1

      with open(_SCREAMING_SNAKE_CASE     ,				'''r'''				) as f:
            _lowerCAmelCase 			=  f.readlines()

      _lowerCAmelCase 			=  f'''class {class_name}('''
      _lowerCAmelCase 			=  f'''{4 * " "}def {test_name}('''
      _lowerCAmelCase 			=  f'''{8 * " "}{correct_line.split()[0]}'''
      _lowerCAmelCase 			=  f'''{1_6 * " "}{correct_line.split()[0]}'''
      _lowerCAmelCase 			=  False
      _lowerCAmelCase 			=  False
      _lowerCAmelCase 			=  False
      _lowerCAmelCase 			=  False
      _lowerCAmelCase 			=  0
      _lowerCAmelCase 			=  0

      _lowerCAmelCase 			=  []
      for line in lines:
            if line.startswith(_SCREAMING_SNAKE_CASE				):
                  _lowerCAmelCase 			=  True
            elif in_class and line.startswith(_SCREAMING_SNAKE_CASE				):
                  _lowerCAmelCase 			=  True
            elif in_class and in_func and (line.startswith(_SCREAMING_SNAKE_CASE				) or line.startswith(_SCREAMING_SNAKE_CASE				)):
                  _lowerCAmelCase 			=  len(line.split(correct_line.split()[0]				)[0]				)
                  count += 1

                  if count == done_test[_id]:
                        _lowerCAmelCase 			=  True

            if in_class and in_func and in_line:
                  if ")" not in line:
                        continue
                  else:
                        _lowerCAmelCase 			=  True

            if in_class and in_func and in_line and insert_line:
                  new_lines.append(f'''{spaces * " "}{correct_line}'''				)
                  _lowerCAmelCase 			=  _lowerCAmelCase 			=  _lowerCAmelCase 			=  _lowerCAmelCase 			=  False
            else:
                  new_lines.append(_SCREAMING_SNAKE_CASE				)

      with open(_SCREAMING_SNAKE_CASE     ,				'''w'''				) as f:
            for line in new_lines:
                  f.write(_SCREAMING_SNAKE_CASE				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	int=None				)->Any:
      if fail is not None:
            with open(_SCREAMING_SNAKE_CASE     ,				'''r'''				) as f:
                  _lowerCAmelCase 			=  {l.strip() for l in f.readlines()}
      else:
            _lowerCAmelCase 			=  None

      with open(_SCREAMING_SNAKE_CASE     ,				'''r'''				) as f:
            _lowerCAmelCase 			=  f.readlines()

      _lowerCAmelCase 			=  defaultdict(_SCREAMING_SNAKE_CASE				)
      for line in correct_lines:
            _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  line.split(''';'''				)
            if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
                  overwrite_file(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							argparse.ArgumentParser()
    parser.add_argument("--correct_filename", help="filename of tests with expected result")
    parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
    UpperCAmelCase_									=							parser.parse_args()

    main(args.correct_filename, args.fail_filename)




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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor





class 		UpperCAmelCase						(						unittest.TestCase				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=7       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=10       ,     _lowerCAmelCase=18       ,     _lowerCAmelCase=30       ,     _lowerCAmelCase=400       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=None       ,     ):
         _lowerCAmelCase 			=  size if size is not None else {'''shortest_edge''': 18}
         _lowerCAmelCase 			=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  num_frames
         _lowerCAmelCase 			=  image_size
         _lowerCAmelCase 			=  min_resolution
         _lowerCAmelCase 			=  max_resolution
         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  image_mean
         _lowerCAmelCase 			=  image_std
         _lowerCAmelCase 			=  crop_size





   def 	__lowerCAmelCase							(	self			):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_size": self.crop_size,
         }






@require_torch
@require_vision
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     VivitImageProcessor if is_vision_available() else None





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  VivitImageProcessingTester(self			)





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 18}			)
         self.assertEqual(image_processor.crop_size       ,     {'''height''': 18, '''width''': 18}			)

         _lowerCAmelCase 			=  self.image_processing_class.from_dict(self.image_processor_dict       ,     size=42       ,     crop_size=84			)
         self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 42}			)
         self.assertEqual(image_processor.crop_size       ,     {'''height''': 84, '''width''': 84}			)





   def 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random PIL videos
         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  image_processing(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
         _lowerCAmelCase 			=  image_processing(_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 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  image_processing(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
         _lowerCAmelCase 			=  image_processing(_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 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  image_processing(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
         _lowerCAmelCase 			=  image_processing(_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'''],
             )       ,     )