






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor





class       __a  (   unittest.TestCase	):







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       Dict[str, int] = None							,       SCREAMING_SNAKE_CASE :       int = 32							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       Union[int, float] = 1 / 2_55							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,       SCREAMING_SNAKE_CASE :       Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       Optional[int]=7							,       SCREAMING_SNAKE_CASE :       Any=30							,       SCREAMING_SNAKE_CASE :       Any=4_00							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  parent
      UpperCamelCase__      :       Tuple	    =  do_resize
      UpperCamelCase__      :       Optional[int]	    =  size if size is not None else {"shortest_edge": 2_88}
      UpperCamelCase__      :       Dict	    =  size_divisor
      UpperCamelCase__      :       Tuple	    =  do_rescale
      UpperCamelCase__      :       int	    =  rescale_factor
      UpperCamelCase__      :       int	    =  do_normalize
      UpperCamelCase__      :       Any	    =  do_center_crop
      UpperCamelCase__      :       Optional[Any]	    =  image_mean
      UpperCamelCase__      :       Tuple	    =  image_std
      UpperCamelCase__      :       int	    =  do_pad
      UpperCamelCase__      :       Optional[int]	    =  batch_size
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       Tuple	    =  max_resolution







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       str=False						):


      '''simple docstring'''






      if not batched:
           UpperCamelCase__      :       int	    =  self.size["shortest_edge"]
           UpperCamelCase__      :       List[str]	    =  image_inputs[0]
           if isinstance(SCREAMING_SNAKE_CASE							,       Image.Image						):
                UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  image.size
           else:
                UpperCamelCase__    ,					UpperCamelCase__      :       List[Any]	    =  image.shape[1], image.shape[2]
           UpperCamelCase__      :       Any	    =  size / min(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
           if h < w:
                UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  size, scale * w
           else:
                UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  scale * h, size

           UpperCamelCase__      :       Dict	    =  int((13_33 / 8_00) * size						)
           if max(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						) > max_size:
                UpperCamelCase__      :       Any	    =  max_size / max(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
                UpperCamelCase__      :       str	    =  newh * scale
                UpperCamelCase__      :       Any	    =  neww * scale

           UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  int(newh + 0.5						), int(neww + 0.5						)
           UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

      else:
           UpperCamelCase__      :       Union[str, Any]	    =  []
           for image in image_inputs:
                UpperCamelCase__    ,					UpperCamelCase__      :       List[Any]	    =  self.get_expected_values([image]						)
                expected_values.append((expected_height, expected_width)						)
           UpperCamelCase__      :       int	    =  max(SCREAMING_SNAKE_CASE							,       key=lambda SCREAMING_SNAKE_CASE						: item[0]						)[0]
           UpperCamelCase__      :       List[Any]	    =  max(SCREAMING_SNAKE_CASE							,       key=lambda SCREAMING_SNAKE_CASE						: item[1]						)[1]

      return expected_height, expected_width







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	Optional[Any]				=			BridgeTowerImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  BridgeTowerImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "image_mean"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "image_std"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_normalize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       str	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input
      UpperCamelCase__      :       List[Any]	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values

      UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE						)
      self.assertEqual(
          encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

      # Test batched
      UpperCamelCase__      :       int	    =  image_processing(SCREAMING_SNAKE_CASE							,       return_tensors="pt"						).pixel_values

      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE							,       batched=SCREAMING_SNAKE_CASE						)
      self.assertEqual(
          encoded_images.shape							,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,       )







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Any	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input
      UpperCamelCase__      :       int	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values

      UpperCamelCase__    ,					UpperCamelCase__      :       str	    =  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE						)
      self.assertEqual(
          encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

      # Test batched
      UpperCamelCase__      :       List[Any]	    =  image_processing(SCREAMING_SNAKE_CASE							,       return_tensors="pt"						).pixel_values

      UpperCamelCase__    ,					UpperCamelCase__      :       Any	    =  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE							,       batched=SCREAMING_SNAKE_CASE						)
      self.assertEqual(
          encoded_images.shape							,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,       )







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Any	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       torch.Tensor						)

      # Test not batched input
      UpperCamelCase__      :       Dict	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values

      UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE						)
      self.assertEqual(
          encoded_images.shape							,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,       )

      # Test batched
      UpperCamelCase__      :       int	    =  image_processing(SCREAMING_SNAKE_CASE							,       return_tensors="pt"						).pixel_values

      UpperCamelCase__    ,					UpperCamelCase__      :       str	    =  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE							,       batched=SCREAMING_SNAKE_CASE						)
      self.assertEqual(
          encoded_images.shape							,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )							,       )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     assert isinstance(__lowerCAmelCase ,   __lowerCAmelCase      ), f'The input value of [n={number}] is not an integer'

     if number == 1:
          return 2
     elif number < 1:
          UpperCamelCase__      :       List[Any]	    =  f'The input value of [n={number}] has to be > 0'
          raise ValueError(__lowerCAmelCase      )
     else:
          UpperCamelCase__      :       Optional[Any]	    =  sylvester(number - 1      )
          UpperCamelCase__      :       str	    =  num - 1
          UpperCamelCase__      :       int	    =  num
          return lower * upper + 1


if __name__ == "__main__":
 print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")