
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 DeiTImageProcessor







class 							UpperCAmelCase	(   unittest.TestCase					):

					def __init__(			self      :					str ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					List[str]=7 ,      lowerCAmelCase      :					List[str]=3 ,      lowerCAmelCase      :					int=18 ,      lowerCAmelCase      :					str=30 ,      lowerCAmelCase      :					Tuple=400 ,      lowerCAmelCase      :					Tuple=True ,      lowerCAmelCase      :					Union[str, Any]=None ,      lowerCAmelCase      :					int=True ,      lowerCAmelCase      :					Optional[int]=None ,      lowerCAmelCase      :					str=True ,      lowerCAmelCase      :					Optional[int]=[0.5, 0.5, 0.5] ,      lowerCAmelCase      :					Optional[int]=[0.5, 0.5, 0.5] ,      ):
											lowercase       :    Dict											=  size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase       :    str											=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase       :    Optional[Any]											=  parent
											lowercase       :    Tuple											=  batch_size
											lowercase       :    int											=  num_channels
											lowercase       :    int											=  image_size
											lowercase       :    Tuple											=  min_resolution
											lowercase       :    Union[str, Any]											=  max_resolution
											lowercase       :    int											=  do_resize
											lowercase       :    Optional[Any]											=  size
											lowercase       :    Optional[int]											=  do_center_crop
											lowercase       :    Union[str, Any]											=  crop_size
											lowercase       :    Optional[Any]											=  do_normalize
											lowercase       :    List[str]											=  image_mean
											lowercase       :    Any											=  image_std




					def        _lowerCAmelCase	(			self      :					Tuple					):
											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,
											}







@require_torch
@require_vision
class 							UpperCAmelCase	(   __lowerCamelCase						, unittest.TestCase					):
					a__: str			  =				DeiTImageProcessor if is_vision_available() else None
					a__: int			  =				True

					def        _lowerCAmelCase	(			self      :					Optional[int]					):
											lowercase       :    int											=  DeiTImageProcessingTester(self					)

					@property
					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											return self.image_processor_tester.prepare_image_processor_dict()

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    Any											=  self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''do_resize'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''size'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''do_center_crop'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''center_crop'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''do_normalize'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''image_mean'''					)					)
											self.assertTrue(hasattr(lowerCAmelCase ,      '''image_std'''					)					)

					def        _lowerCAmelCase	(			self      :					str					):
											lowercase       :    int											=  self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size ,      {'''height''': 20, '''width''': 20}					)
											self.assertEqual(image_processor.crop_size ,      {'''height''': 18, '''width''': 18}					)

											lowercase       :    Optional[int]											=  self.image_processing_class.from_dict(self.image_processor_dict ,      size=42 ,      crop_size=84					)
											self.assertEqual(image_processor.size ,      {'''height''': 42, '''width''': 42}					)
											self.assertEqual(image_processor.crop_size ,      {'''height''': 84, '''width''': 84}					)

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											pass

					def        _lowerCAmelCase	(			self      :					Tuple					):
											# Initialize image_processing
											lowercase       :    Tuple											=  self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											lowercase       :    int											=  prepare_image_inputs(self.image_processor_tester ,      equal_resolution=lowerCAmelCase					)
											for image in image_inputs:
																	self.assertIsInstance(lowerCAmelCase ,      Image.Image					)

											# Test not batched input
											lowercase       :    Union[str, Any]											=  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       :    List[Any]											=  image_processing(lowerCAmelCase ,      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        _lowerCAmelCase	(			self      :					Any					):
											# Initialize image_processing
											lowercase       :    Tuple											=  self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											lowercase       :    Optional[Any]											=  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
											lowercase       :    Union[str, Any]											=  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       :    List[Any]											=  image_processing(lowerCAmelCase ,      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        _lowerCAmelCase	(			self      :					List[str]					):
											# Initialize image_processing
											lowercase       :    int											=  self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											lowercase       :    List[str]											=  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
											lowercase       :    List[str]											=  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       :    Optional[Any]											=  image_processing(lowerCAmelCase ,      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 itertools import combinations_with_replacement

def        lowerCamelCase_      (			UpperCAmelCase_							:				int = 1_00       ):
						lowercase       :    list[int]											=  [*list(range(1							,			21       )       ), 25]
						lowercase       :    list[int]											=  [2 * x for x in range(1							,			21       )] + [50]
						lowercase       :    list[int]											=  [3 * x for x in range(1							,			21       )]
						lowercase       :    list[int]											=  singles + doubles + triples + [0]

						lowercase       :    int											=  0
						lowercase       :    int
						lowercase       :    int
						lowercase       :    int
						lowercase       :    int

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(UpperCAmelCase_							,			2       ):
																		lowercase       :    Union[str, Any]											=  double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


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


