


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     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A = True  ,       __A = None  ,       __A = 32  ,       __A = True  ,       __A = 1 / 255  ,       __A = True  ,       __A = True  ,       __A = [0.48145466, 0.4578275, 0.40821073]  ,       __A = [0.26862954, 0.26130258, 0.27577711]  ,       __A = True  ,       __A=7  ,       __A=30  ,       __A=400  ,       __A=3  ,       ):
        __a					=  parent
        __a					=  do_resize
        __a					=  size if size is not None else {"""shortest_edge""": 288}
        __a					=  size_divisor
        __a					=  do_rescale
        __a					=  rescale_factor
        __a					=  do_normalize
        __a					=  do_center_crop
        __a					=  image_mean
        __a					=  image_std
        __a					=  do_pad
        __a					=  batch_size
        __a					=  num_channels
        __a					=  min_resolution
        __a					=  max_resolution


     def 	snake_case_  (	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,
            "size_divisor": self.size_divisor,
        }


     def 	snake_case_  (	self  ,       __A  ,       __A=False    ):
        if not batched:
           __a					=  self.size["""shortest_edge"""]
           __a					=  image_inputs[0]
           if isinstance(__a  ,       Image.Image    ):
              __a					=  image.size
           else:
              __a					=  image.shape[1], image.shape[2]
           __a					=  size / min(__a  ,       __a    )
           if h < w:
              __a					=  size, scale * w
           else:
              __a					=  scale * h, size

           __a					=  int((1333 / 800) * size    )
           if max(__a  ,       __a    ) > max_size:
              __a					=  max_size / max(__a  ,       __a    )
              __a					=  newh * scale
              __a					=  neww * scale

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

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

        return expected_height, expected_width







@require_torch
@require_vision
class     __UpperCAmelCase							(       a__ ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      BridgeTowerImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  BridgeTowerImageProcessingTester(self    )


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


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__a  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__a  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__a  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__a  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__a  ,       """size"""    )    )
        self.assertTrue(hasattr(__a  ,       """size_divisor"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=__a    )
        for image in image_inputs:
           self.assertIsInstance(__a  ,       Image.Image    )

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

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

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

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


     def 	snake_case_  (	self    ):
        # Initialize image processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=__a  ,       numpify=__a    )
        for image in image_inputs:
           self.assertIsInstance(__a  ,       np.ndarray    )

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

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

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

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


     def 	snake_case_  (	self    ):
        # Initialize image processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_processor_tester  ,       equal_resolution=__a  ,       torchify=__a    )
        for image in image_inputs:
           self.assertIsInstance(__a  ,       torch.Tensor    )

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

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

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

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









'''simple docstring'''







from __future__ import annotations







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a = 0							)      ->				str:
          snake_case			:      List[Any]        =			key







     def 			snake_case_      (				self							,__a							,__a							)      ->				list[str]:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      Tuple        =			key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(__a							) ^ key							) for ch in content]







     def 			snake_case_      (				self							,__a							,__a							)      ->				list[str]:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      List[Any]        =			key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(__a							) ^ key							) for ch in content]







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				str:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      List[Any]        =			key or self.__key or 1

          # make sure key can be any size
          while key > 255:
               key -= 255

          # This will be returned
          snake_case			:      List[str]        =			""""""

          for ch in content:
               ans += chr(ord(__a							) ^ key							)

          return ans







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				str:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      Any        =			key or self.__key or 1

          # make sure key can be any size
          while key > 255:
               key -= 255

          # This will be returned
          snake_case			:      List[str]        =			""""""

          for ch in content:
               ans += chr(ord(__a							) ^ key							)

          return ans







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				bool:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          try:
               with open(__a							) as fin, open("""encrypt.out"""							,"""w+"""							) as fout:
                    # actual encrypt-process
                    for line in fin:
                         fout.write(self.encrypt_string(__a							,__a							)							)

          except OSError:
               return False

          return True







     def 			snake_case_      (				self							,__a							,__a							)      ->				bool:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          try:
               with open(__a							) as fin, open("""decrypt.out"""							,"""w+"""							) as fout:
                    # actual encrypt-process
                    for line in fin:
                         fout.write(self.decrypt_string(__a							,__a							)							)

          except OSError:
               return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")



