
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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import CLIPImageProcessor


class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=7						,    __UpperCamelCase=3						,    __UpperCamelCase=18						,    __UpperCamelCase=30						,    __UpperCamelCase=400						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    __UpperCamelCase=True						,    __UpperCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]						,    __UpperCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,    __UpperCamelCase=True						,    ):
      A_								=					size if size is not None else {"shortest_edge": 20}
      A_								=					crop_size if crop_size is not None else {"height": 18, "width": 18}
      A_								=					parent
      A_								=					batch_size
      A_								=					num_channels
      A_								=					image_size
      A_								=					min_resolution
      A_								=					max_resolution
      A_								=					do_resize
      A_								=					size
      A_								=					do_center_crop
      A_								=					crop_size
      A_								=					do_normalize
      A_								=					image_mean
      A_								=					image_std
      A_								=					do_convert_rgb

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

  def 					lowercase_						(					self						,    __UpperCamelCase=False						,    __UpperCamelCase=False						,    __UpperCamelCase=False					):
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
          A_								=					[]
          for i in range(self.batch_size					):
              image_inputs.append(
                  np.random.randint(
                      255						,    size=(self.num_channels, self.max_resolution, self.max_resolution)						,    dtype=np.uinta					)					)
      else:
          A_								=					[]
          for i in range(self.batch_size					):
              A_	,	A_								=					np.random.choice(np.arange(self.min_resolution						,    self.max_resolution					)						,    2					)
              image_inputs.append(np.random.randint(255						,    size=(self.num_channels, width, height)						,    dtype=np.uinta					)					)

      if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          A_								=					[Image.fromarray(np.moveaxis(__UpperCAmelCase						,    0						,    -1					)					) for x in image_inputs]

      if torchify:
          A_								=					[torch.from_numpy(__UpperCAmelCase					) for x in image_inputs]

      return image_inputs



@require_torch
@require_vision
class 							lowerCamelCase       (		lowerCamelCase_			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		CLIPImageProcessor if is_vision_available() else None

  def 					lowercase_						(					self					):
      A_								=					CLIPImageProcessingTester(self					)

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

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_resize"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "size"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_center_crop"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "center_crop"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_normalize"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "image_mean"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "image_std"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_convert_rgb"					)					)

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size						,    {"shortest_edge": 20}					)
      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 					lowercase_						(					self					):
      pass

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      A_								=					self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase						,    Image.Image					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              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_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      A_								=					self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase						,    numpify=__UpperCAmelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase						,    np.ndarray					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              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_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      A_								=					self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase						,    torchify=__UpperCAmelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase						,    torch.Tensor					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              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_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )



@require_torch
@require_vision
class 							lowerCamelCase       (		lowerCamelCase_			,  unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		CLIPImageProcessor if is_vision_available() else None

  def 					lowercase_						(					self					):
      A_								=					CLIPImageProcessingTester(self						,    num_channels=4					)
      A_								=					3

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

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_resize"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "size"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_center_crop"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "center_crop"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_normalize"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "image_mean"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "image_std"					)					)
      self.assertTrue(hasattr(__UpperCAmelCase						,    "do_convert_rgb"					)					)

  def 					lowercase_						(					self					):
      pass

  def 					lowercase_						(					self					):
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      A_								=					self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase					)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase						,    Image.Image					)

      # Test not batched input
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape						,    (
              1,
              self.expected_encoded_image_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_images.shape						,    (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )						,    )












__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCamelCase_     				= gear_one[0]
			gear_one.append(__snake_case					)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(__snake_case					)					) == 0:
						UpperCamelCase_     				= gear_two[0]
						gear_two.append(__snake_case					)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(__snake_case					)					) == 0:
									UpperCamelCase_     				= gear_three[0]
									gear_three.append(__snake_case					)
									del gear_three[0]
									gear_three_pos += 1




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= alphabets.index(__snake_case					)
			UpperCamelCase_     				= gear_one[target]
			UpperCamelCase_     				= gear_two[target]
			UpperCamelCase_     				= gear_three[target]
			UpperCamelCase_     				= reflector[target]
			UpperCamelCase_     				= gear_three.index(__snake_case					)
			UpperCamelCase_     				= gear_two.index(__snake_case					)
			UpperCamelCase_     				= gear_one.index(__snake_case					)
			code.append(alphabets[target]					)
			rotator()


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[Any]							     =		int(input("""Please set token:(must be only digits)\n"""))
												break
								except Exception as error:
												print(error)
				for _ in range(token):
								rotator()
				for j in decode:
								engine(j)
				print("""\n""" + """""".join(code))
				print(
				    f"""\nYour Token is {token} please write it down.\nIf you want to decode """
				    """this message again you should input same digits as token!"""
				)
