

import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class      lowerCamelCase_      (	unittest.TestCase	):




	def __init__(     self						:     Tuple					,      __A						:     Optional[Any]					,      __A						:     bool = True					,      __A						:     Dict[str, int] = None					,      __A						:     int = 32					,      __A						:     bool = True					,      __A						:     Union[int, float] = 1 / 255					,      __A						:     bool = True					,      __A						:     bool = True					,      __A						:     Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					,      __A						:     Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					,      __A						:     bool = True					,      __A						:     Tuple=7					,      __A						:     Tuple=30					,      __A						:     List[str]=400					,      __A						:     str=3					,      ):
		__A :			str		    =			parent
		__A :			List[Any]		    =			do_resize
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 288}
		__A :			Optional[Any]		    =			size_divisor
		__A :			str		    =			do_rescale
		__A :			Tuple		    =			rescale_factor
		__A :			Optional[int]		    =			do_normalize
		__A :			Tuple		    =			do_center_crop
		__A :			Any		    =			image_mean
		__A :			Union[str, Any]		    =			image_std
		__A :			str		    =			do_pad
		__A :			Any		    =			batch_size
		__A :			Union[str, Any]		    =			num_channels
		__A :			Dict		    =			min_resolution
		__A :			Tuple		    =			max_resolution




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "size_divisor": self.size_divisor,
		}




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     List[Any]					,      __A						:     Any=False							):
		if not batched:
			__A :			Optional[Any]		    =			self.size["""shortest_edge"""]
			__A :			Optional[int]		    =			image_inputs[0]
			if isinstance(__A					,      Image.Image							):
				__A    ,				__A :			Any		    =			image.size
			else:
				__A    ,				__A :			Dict		    =			image.shape[1], image.shape[2]
			__A :			Tuple		    =			size / min(__A					,      __A							)
			if h < w:
				__A    ,				__A :			Union[str, Any]		    =			size, scale * w
			else:
				__A    ,				__A :			str		    =			scale * h, size

			__A :			List[str]		    =			int((1333 / 800) * size							)
			if max(__A					,      __A							) > max_size:
				__A :			Optional[int]		    =			max_size / max(__A					,      __A							)
				__A :			Tuple		    =			newh * scale
				__A :			Optional[Any]		    =			neww * scale

			__A    ,				__A :			Tuple		    =			int(newh + 0.5							), int(neww + 0.5							)
			__A    ,				__A :			List[Any]		    =			(
			    newh // self.size_divisor * self.size_divisor,
			    neww // self.size_divisor * self.size_divisor,
			)

		else:
			__A :			Dict		    =			[]
			for image in image_inputs:
				__A    ,				__A :			List[str]		    =			self.get_expected_values([image]							)
				expected_values.append((expected_height, expected_width)							)
			__A :			Dict		    =			max(__A					,      key=lambda __A							: item[0]							)[0]
			__A :			Dict		    =			max(__A					,      key=lambda __A							: item[1]							)[1]

		return expected_height, expected_width






@require_torch
@require_vision
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   str     					=				BridgeTowerImageProcessor if is_vision_available() else None




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			str		    =			BridgeTowerImageProcessingTester(self							)




	@property
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.image_processor_tester.prepare_image_processor_dict()




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			int		    =			self.image_processing_class(**self.image_processor_dict							)
		self.assertTrue(hasattr(__A					,      """image_mean"""							)							)
		self.assertTrue(hasattr(__A					,      """image_std"""							)							)
		self.assertTrue(hasattr(__A					,      """do_normalize"""							)							)
		self.assertTrue(hasattr(__A					,      """do_resize"""							)							)
		self.assertTrue(hasattr(__A					,      """size"""							)							)
		self.assertTrue(hasattr(__A					,      """size_divisor"""							)							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		# Initialize image processor
		__A :			Optional[int]		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		__A :			Optional[int]		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      Image.Image							)

		# Test not batched input
		__A :			Dict		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Optional[Any]		    =			self.image_processor_tester.get_expected_values(__A							)
		self.assertEqual(
		    encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

		# Test batched
		__A :			Union[str, Any]		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			List[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)
		self.assertEqual(
		    encoded_images.shape					,      (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,      )




	def        lowerCAmelCase_       (     self						:     int							):
		# Initialize image processor
		__A :			Optional[int]		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		__A :			Optional[int]		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A					,      numpify=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      np.ndarray							)

		# Test not batched input
		__A :			Union[str, Any]		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			str		    =			self.image_processor_tester.get_expected_values(__A							)
		self.assertEqual(
		    encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

		# Test batched
		__A :			List[str]		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Dict		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)
		self.assertEqual(
		    encoded_images.shape					,      (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,      )




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		# Initialize image processor
		__A :			Union[str, Any]		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		__A :			Any		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A					,      torchify=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      torch.Tensor							)

		# Test not batched input
		__A :			List[Any]		    =			image_processing(image_inputs[0]					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			str		    =			self.image_processor_tester.get_expected_values(__A							)
		self.assertEqual(
		    encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

		# Test batched
		__A :			int		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Optional[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)
		self.assertEqual(
		    encoded_images.shape					,      (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,      )








UpperCAmelCase_							:					dict[tuple[int, int, int], int]		 =   {}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   int       )					->				int:

	# if we are absent twice, or late 3 consecutive days,
	# no further prize strings are possible
	if late == 3 or absent == 2:
		return 0

	# if we have no days left, and have not failed any other rules,
	# we have a prize string
	if days == 0:
		return 1

	# No easy solution, so now we need to do the recursive calculation

	# First, check if the combination is already in the cache, and
	# if yes, return the stored value from there since we already
	# know the number of possible prize strings from this point on
	__A :			List[Any]		    =			(days, absent, late)
	if key in cache:
		return cache[key]

	# now we calculate the three possible ways that can unfold from
	# this point on, depending on our attendance today

	# 1) if we are late (but not absent), the "absent" counter stays as
	# it is, but the "late" counter increases by one
	__A :			Dict		    =			_calculate(days - 1			,a__			,late + 1       )

	# 2) if we are absent, the "absent" counter increases by 1, and the
	# "late" counter resets to 0
	__A :			List[str]		    =			_calculate(days - 1			,absent + 1			,0       )

	# 3) if we are on time, this resets the "late" counter and keeps the
	# absent counter
	__A :			int		    =			_calculate(days - 1			,a__			,0       )

	__A :			Optional[int]		    =			state_late + state_absent + state_ontime

	__A :			Tuple		    =			prizestrings
	return prizestrings
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 30       )					->				int:
	return _calculate(a__			,absent=0			,late=0       )


if __name__ == "__main__":
			print(solution())





