






'''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 LevitImageProcessor




class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=7   ,_lowerCAmelCase=3   ,_lowerCAmelCase=18   ,_lowerCAmelCase=30   ,_lowerCAmelCase=4_00   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,):
								lowerCamelCase__         =  size if size is not None else {"""shortest_edge""": 18}
								lowerCamelCase__         =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  min_resolution
								lowerCamelCase__         =  max_resolution
								lowerCamelCase__         =  do_resize
								lowerCamelCase__         =  size
								lowerCamelCase__         =  do_center_crop
								lowerCamelCase__         =  crop_size
								lowerCamelCase__         =  do_normalize
								lowerCamelCase__         =  image_mean
								lowerCamelCase__         =  image_std






		def 			UpperCamelCase_					(      self       ):
								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 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					LevitImageProcessor if is_vision_available() else None






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  LevitImageProcessingTester(self       )






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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  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}       )

								lowerCamelCase__         =  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       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=__UpperCamelCase       )
								for image in image_inputs:
														self.assertIsInstance(__UpperCamelCase   ,Image.Image       )

								# Test not batched input
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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"""],
								    )   ,)









'''simple docstring'''







from math import factorial

UpperCamelCase    :      dict[str, int]  			=		{str(digit): factorial(digit) for digit in range(10)}
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise TypeError("""Parameter number must be int"""						)

						if number < 0:
												raise ValueError("""Parameter number must be greater than or equal to 0"""						)

						# Converts number in string to iterate on its digits and adds its factorial.
						return sum(DIGIT_FACTORIAL[digit] for digit in str(__lowerCAmelCase						)						)
def 						A__			(				__lowerCAmelCase		:			int = 60   ,    __lowerCAmelCase		:			int = 100_0000						):

						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						) or not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise TypeError("""Parameters chain_length and number_limit must be int"""						)

						if chain_length <= 0 or number_limit <= 0:
												raise ValueError(
												    """Parameters chain_length and number_limit must be greater than 0"""						)

						# the counter for the chains with the exact desired length
						lowerCamelCase__         =  0
						# the cached sizes of the previous chains
						lowerCamelCase__         =  {}

						for start_chain_element in range(1   ,    __lowerCAmelCase						):
												# The temporary set will contain the elements of the chain
												lowerCamelCase__         =  set()
												lowerCamelCase__         =  0

												# Stop computing the chain when you find a cached size, a repeating item or the
												# length is greater then the desired one.
												lowerCamelCase__         =  start_chain_element
												while (
												    chain_element not in chain_sets_lengths
												    and chain_element not in chain_set
												    and chain_set_length <= chain_length
												):
																		chain_set.add(__lowerCAmelCase						)
																		chain_set_length += 1
																		lowerCamelCase__         =  digit_factorial_sum(__lowerCAmelCase						)

												if chain_element in chain_sets_lengths:
																		chain_set_length += chain_sets_lengths[chain_element]

												lowerCamelCase__         =  chain_set_length

												# If chain contains the exact amount of elements increase the counter
												if chain_set_length == chain_length:
																		chains_counter += 1

						return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'{solution()}')

