



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 						__magic_name__    (       unittest.TestCase    ):


 def __init__(						self		: Any			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Dict[str, int] = None			,						lowerCAmelCase__		: int = 3_2			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Union[int, float] = 1 / 2_5_5			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Union[str, Any]=7			,						lowerCAmelCase__		: Dict=3_0			,						lowerCAmelCase__		: Dict=4_0_0			,						lowerCAmelCase__		: Dict=3			,						)    ->      Union[str, Any]:
   UpperCAmelCase					      =  parent
   UpperCAmelCase					      =  do_resize
   UpperCAmelCase					      =  size if size is not None else {"shortest_edge": 2_8_8}
   UpperCAmelCase					      =  size_divisor
   UpperCAmelCase					      =  do_rescale
   UpperCAmelCase					      =  rescale_factor
   UpperCAmelCase					      =  do_normalize
   UpperCAmelCase					      =  do_center_crop
   UpperCAmelCase					      =  image_mean
   UpperCAmelCase					      =  image_std
   UpperCAmelCase					      =  do_pad
   UpperCAmelCase					      =  batch_size
   UpperCAmelCase					      =  num_channels
   UpperCAmelCase					      =  min_resolution
   UpperCAmelCase					      =  max_resolution


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      str:
   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 _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Union[str, Any]=False    )    ->      int:
   if not batched:
     UpperCAmelCase					      =  self.size["shortest_edge"]
     UpperCAmelCase					      =  image_inputs[0]
     if isinstance(lowerCAmelCase__			,						Image.Image    ):
       UpperCAmelCase      ,	UpperCAmelCase					      =  image.size
     else:
       UpperCAmelCase      ,	UpperCAmelCase					      =  image.shape[1], image.shape[2]
     UpperCAmelCase					      =  size / min(lowerCAmelCase__			,						lowerCAmelCase__    )
     if h < w:
       UpperCAmelCase      ,	UpperCAmelCase					      =  size, scale * w
     else:
       UpperCAmelCase      ,	UpperCAmelCase					      =  scale * h, size

     UpperCAmelCase					      =  int((1_3_3_3 / 8_0_0) * size    )
     if max(lowerCAmelCase__			,						lowerCAmelCase__    ) > max_size:
       UpperCAmelCase					      =  max_size / max(lowerCAmelCase__			,						lowerCAmelCase__    )
       UpperCAmelCase					      =  newh * scale
       UpperCAmelCase					      =  neww * scale

     UpperCAmelCase      ,	UpperCAmelCase					      =  int(newh + 0.5    ), int(neww + 0.5    )
     UpperCAmelCase      ,	UpperCAmelCase					      =  (
         newh // self.size_divisor * self.size_divisor,
         neww // self.size_divisor * self.size_divisor,
     )

   else:
     UpperCAmelCase					      =  []
     for image in image_inputs:
       UpperCAmelCase      ,	UpperCAmelCase					      =  self.get_expected_values([image]    )
       expected_values.append((expected_height, expected_width)    )
     UpperCAmelCase					      =  max(lowerCAmelCase__			,						key=lambda lowerCAmelCase__    : item[0]    )[0]
     UpperCAmelCase					      =  max(lowerCAmelCase__			,						key=lambda lowerCAmelCase__    : item[1]    )[1]

   return expected_height, expected_width






@require_torch
@require_vision
class 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						BridgeTowerImageProcessor if is_vision_available() else None


 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   UpperCAmelCase					      =  BridgeTowerImageProcessingTester(self    )


 @property
 def _UpperCamelCase	(						self		: List[str]    )    ->      Union[str, Any]:
   return self.image_processor_tester.prepare_image_processor_dict()


 def _UpperCamelCase	(						self		: str    )    ->      Dict:
   UpperCAmelCase					      =  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__			,						"size"    )    )
   self.assertTrue(hasattr(lowerCAmelCase__			,						"size_divisor"    )    )


 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   pass


 def _UpperCamelCase	(						self		: Dict    )    ->      int:
   # Initialize image processor
   UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   UpperCAmelCase					      =  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					      =  image_processing(image_inputs[0]			,						return_tensors="pt"    ).pixel_values

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor_tester.get_expected_values(lowerCAmelCase__    )
   self.assertEqual(
       encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

   # Test batched
   UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						return_tensors="pt"    ).pixel_values

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor_tester.get_expected_values(lowerCAmelCase__			,						batched=lowerCAmelCase__    )
   self.assertEqual(
       encoded_images.shape			,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )			,						)


 def _UpperCamelCase	(						self		: Any    )    ->      Union[str, Any]:
   # Initialize image processor
   UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   UpperCAmelCase					      =  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					      =  image_processing(image_inputs[0]			,						return_tensors="pt"    ).pixel_values

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor_tester.get_expected_values(lowerCAmelCase__    )
   self.assertEqual(
       encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

   # Test batched
   UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						return_tensors="pt"    ).pixel_values

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor_tester.get_expected_values(lowerCAmelCase__			,						batched=lowerCAmelCase__    )
   self.assertEqual(
       encoded_images.shape			,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )			,						)


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Dict:
   # Initialize image processor
   UpperCAmelCase					      =  self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   UpperCAmelCase					      =  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					      =  image_processing(image_inputs[0]			,						return_tensors="pt"    ).pixel_values

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor_tester.get_expected_values(lowerCAmelCase__    )
   self.assertEqual(
       encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

   # Test batched
   UpperCAmelCase					      =  image_processing(lowerCAmelCase__			,						return_tensors="pt"    ).pixel_values

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.image_processor_tester.get_expected_values(lowerCAmelCase__			,						batched=lowerCAmelCase__    )
   self.assertEqual(
       encoded_images.shape			,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )			,						)






def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  if n == 0:
    return 1

  elif n % 2 == 1:
    return (binary_exponentiation(__A   ,       n - 1   ,       __A			) * a) % mod

  else:
    UpperCAmelCase					      =  binary_exponentiation(__A   ,       n / 2   ,       __A			)
    return (b * b) % mod


# a prime number
lowerCAmelCase__       = 701

lowerCAmelCase__       = 1000000000
lowerCAmelCase__       = 10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)





print((a / b) % p == (a * b ** (p - 2)) % p)



