

"""simple docstring"""





from itertools import combinations_with_replacement




def     _A    (    UpperCamelCase_	:			int = 100)      ->		int:
   '''simple docstring'''


   __lowercase								=		[*list(range(1,     21)), 25]
   __lowercase								=		[2 * x for x in range(1,     21)] + [50]
   __lowercase								=		[3 * x for x in range(1,     21)]
   __lowercase								=		singles + doubles + triples + [0]

   __lowercase								=		0
   __lowercase								=		42
   __lowercase								=		42
   __lowercase								=		42
   __lowercase								=		42

   for double in doubles:
      for throwa, throwa in combinations_with_replacement(UpperCamelCase_,     2):
         __lowercase								=		double + throwa + throwa
         if checkout_total < limit:
            num_checkouts += 1

   return num_checkouts


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







"""simple docstring"""





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 OwlViTImageProcessor




class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int]=7,   UpperCAmelCase__				:     List[str]=3,   UpperCAmelCase__				:     List[Any]=1_8,   UpperCAmelCase__				:     Any=3_0,   UpperCAmelCase__				:     str=4_0_0,   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     Dict=None,   UpperCAmelCase__				:     int=True,   UpperCAmelCase__				:     str=None,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     str=[0.48_145_466, 0.4_578_275, 0.40_821_073],   UpperCAmelCase__				:     Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711],   UpperCAmelCase__				:     Any=True,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		num_channels
    __lowercase								=		image_size
    __lowercase								=		min_resolution
    __lowercase								=		max_resolution
    __lowercase								=		do_resize
    __lowercase								=		size if size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_center_crop
    __lowercase								=		crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_normalize
    __lowercase								=		image_mean
    __lowercase								=		image_std
    __lowercase								=		do_convert_rgb



 def    _lowercase					(     self				:     List[str]      ):
    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,
    }







@require_torch
@require_vision
class    _lowerCAmelCase    (      lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      OwlViTImageProcessor if is_vision_available() else None


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		OwlViTImageProcessingTester(self      )


 @property
 def    _lowercase					(     self				:     int      ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		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				:     Dict      ):
    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size,   {"height": 1_8, "width": 1_8}      )
    self.assertEqual(image_processor.crop_size,   {"height": 1_8, "width": 1_8}      )

    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict,   size=4_2,   crop_size=8_4      )
    self.assertEqual(image_processor.size,   {"height": 4_2, "width": 4_2}      )
    self.assertEqual(image_processor.crop_size,   {"height": 8_4, "width": 8_4}      )


 def    _lowercase					(     self				:     Any      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__      )

    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   Image.Image      )

    # Test not batched input
    __lowercase								=		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
    __lowercase								=		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				:     Optional[Any]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    __lowercase								=		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
    __lowercase								=		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
    __lowercase								=		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				:     List[str]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    __lowercase								=		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
    __lowercase								=		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
    __lowercase								=		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"],
        ),   )




