





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_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def __init__(  self,		A_,		A_=7,		A_=3,		A_=18,		A_=30,		A_=400,		A_=True,		A_=None,		A_=True,		A_=None,		A_=True,		A_=[0.5, 0.5, 0.5],		A_=[0.5, 0.5, 0.5],		)	->       Any:
    UpperCAmelCase__						=size if size is not None else {"shortest_edge": 18}
    UpperCAmelCase__						=crop_size if crop_size is not None else {"height": 18, "width": 18}
    UpperCAmelCase__						=parent
    UpperCAmelCase__						=batch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=min_resolution
    UpperCAmelCase__						=max_resolution
    UpperCAmelCase__						=do_resize
    UpperCAmelCase__						=size
    UpperCAmelCase__						=do_center_crop
    UpperCAmelCase__						=crop_size
    UpperCAmelCase__						=do_normalize
    UpperCAmelCase__						=image_mean
    UpperCAmelCase__						=image_std






 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }

@require_torch
@require_vision
class     snake_case_		(   A__,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      LevitImageProcessor if is_vision_available() else None

 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=LevitImageProcessingTester(self		)

 @property
 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    return self.image_processor_tester.prepare_image_processor_dict()

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=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		)	->       Tuple:
    UpperCAmelCase__						=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}		)

    UpperCAmelCase__						=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		)	->       Union[str, Any]:
    pass

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random PIL images
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=_UpperCamelCase		)
    for image in image_inputs:
       self.assertIsInstance(_UpperCamelCase,		Image.Image		)

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

    # Test not batched input
    UpperCAmelCase__						=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
    UpperCAmelCase__						=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  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=_UpperCamelCase,		torchify=_UpperCamelCase		)
    for image in image_inputs:
       self.assertIsInstance(_UpperCamelCase,		torch.Tensor		)

    # Test not batched input
    UpperCAmelCase__						=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
    UpperCAmelCase__						=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"],
        ),		)










from __future__ import annotations
def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   _UpperCAmelCase           =   str(SCREAMING_SNAKE_CASE_						)
   return len(SCREAMING_SNAKE_CASE_						) == 9 and set(SCREAMING_SNAKE_CASE_						) == set('''123456789'''						)
def      A__       (    )  ->  int | None:






   """simple docstring"""







   for base_num in range(99_99				,	49_99				,	-1						):
      _UpperCAmelCase           =   10_00_02 * base_num
      if is_9_pandigital(SCREAMING_SNAKE_CASE_						):
         return candidate

   for base_num in range(3_33				,	99				,	-1						):
      _UpperCAmelCase           =   1_00_20_03 * base_num
      if is_9_pandigital(SCREAMING_SNAKE_CASE_						):
         return candidate

   return None


if __name__ == "__main__":
   print(f'''{solution() = }''')