
"""simple docstring"""






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 VideoMAEImageProcessor





class 							snake_case       (     unittest.TestCase		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=10       ,							__UpperCAmelCase=18       ,							__UpperCAmelCase=30       ,							__UpperCAmelCase=4_00       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=None       ,							)   ->Dict:
      a_						   =     size if size is not None else {"shortest_edge": 18}
      a_						   =     crop_size if crop_size is not None else {"height": 18, "width": 18}

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







   def    UpperCAmelCase__	( self)   ->List[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,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		List[str]     					=     VideoMAEImageProcessor if is_vision_available() else None







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     VideoMAEImageProcessingTester(self)







   @property
   def    UpperCAmelCase__	( self)   ->Tuple:
      return self.image_processor_tester.prepare_image_processor_dict()







   def    UpperCAmelCase__	( self)   ->int:
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_mean"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_std"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_normalize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_center_crop"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size"))







   def    UpperCAmelCase__	( self)   ->Dict:
      a_						   =     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})

      a_						   =     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    UpperCAmelCase__	( self)   ->List[Any]:
      pass







   def    UpperCAmelCase__	( self)   ->Any:
      # Initialize image_processing
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							Image.Image)

      # Test not batched input
      a_						   =     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
      a_						   =     image_processing(__UpperCAmelCase       ,							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    UpperCAmelCase__	( self)   ->List[Any]:
      # Initialize image_processing
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							numpify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							np.ndarray)

      # Test not batched input
      a_						   =     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
      a_						   =     image_processing(__UpperCAmelCase       ,							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    UpperCAmelCase__	( self)   ->Any:
      # Initialize image_processing
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							torchify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							torch.Tensor)

      # Test not batched input
      a_						   =     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
      a_						   =     image_processing(__UpperCAmelCase       ,							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"],
          )       ,							)

"""simple docstring"""






def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase = 1_000     )						->str:

   """simple docstring"""



   if (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"     )
   elif (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"     )

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   a_						   =     (
       4 * _subsum(UpperCAmelCase ,   1 ,   UpperCAmelCase     )
       - 2 * _subsum(UpperCAmelCase ,   4 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   5 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   6 ,   UpperCAmelCase     )
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16     )     )[2:]
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->float:

   """simple docstring"""



   a_						   =     0.0
   for sum_index in range(digit_pos_to_extract + precision     ):
      a_						   =     8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         a_						   =     pow(
             16 ,   digit_pos_to_extract - 1 - sum_index ,   UpperCAmelCase     )
      else:
         a_						   =     pow(16 ,   digit_pos_to_extract - 1 - sum_index     )
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()