






import unittest

import numpy as np
import requests

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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							A__			:    Tuple         =				False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Any=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					List[str]=30,						lowerCamelCase		:					Optional[Any]=400,						lowerCamelCase		:					List[Any]=None,						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					int=True,						lowerCamelCase		:					Dict=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''height''': 20, '''width''': 20}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    size
								lowercase__     				=    do_normalize
								lowercase__     				=    do_convert_rgb
								lowercase__     				=    [512, 1_024, 2_048, 4_096]
								lowercase__     				=    patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
								lowercase__     				=    Image.open(requests.get(lowerCamelCase,						stream=lowerCamelCase						).raw						).convert('''RGB'''						)
								return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""	,)
@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= PixaStructImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    PixaStructImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_convert_rgb'''						)						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.image_processor_tester.prepare_dummy_image()

								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								lowercase__     				=    2_048

								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''pt''',						max_patches=lowerCamelCase						)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),						torch.tensor(0.0606						),						atol=1E-3,						rtol=1E-3						)						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    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__     				=    (
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												lowercase__     				=    image_processor(
												    image_inputs[0],						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(1, max_patch, expected_hidden_dim),						)

												# Test batched
												lowercase__     				=    image_processor(
												    lowerCamelCase,						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    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__     				=    (
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								lowercase__     				=    True

								for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												with self.assertRaises(lowerCamelCase						):
																lowercase__     				=    image_processor(
																    image_inputs[0],						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches

												lowercase__     				=    '''Hello'''

												lowercase__     				=    image_processor(
												    image_inputs[0],						return_tensors='''pt''',						max_patches=lowerCamelCase,						header_text=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(1, max_patch, expected_hidden_dim),						)

												# Test batched
												lowercase__     				=    image_processor(
												    lowerCamelCase,						return_tensors='''pt''',						max_patches=lowerCamelCase,						header_text=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								lowercase__     				=    (
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												lowercase__     				=    image_processor(
												    image_inputs[0],						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(1, max_patch, expected_hidden_dim),						)

												# Test batched
												lowercase__     				=    image_processor(
												    lowerCamelCase,						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    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__     				=    (
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												lowercase__     				=    image_processor(
												    image_inputs[0],						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(1, max_patch, expected_hidden_dim),						)

												# Test batched
												lowercase__     				=    image_processor(
												    lowerCamelCase,						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),						)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""	,)
@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= PixaStructImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    PixaStructImageProcessingTester(self,						num_channels=4						)
								lowercase__     				=    3




				@property
				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_convert_rgb'''						)						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    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__     				=    (
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * (self.image_processor_tester.num_channels - 1)
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												lowercase__     				=    image_processor(
												    image_inputs[0],						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(1, max_patch, expected_hidden_dim),						)

												# Test batched
												lowercase__     				=    image_processor(
												    lowerCamelCase,						return_tensors='''pt''',						max_patches=lowerCamelCase						).flattened_patches
												self.assertEqual(
												    encoded_images.shape,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),						)












from functools import reduce

A__			:    Union[str, Any]         =				(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)






def 						a    (		lowerCamelCase_ = N       ):






				'''simple docstring'''
				return max(
				    # mypy cannot properly interpret reduce
				    int(reduce(lambda lowerCamelCase_	,     lowerCamelCase_       : str(int(lowerCamelCase_       ) * int(lowerCamelCase_       )       )	,     n[i : i + 13]       )       )
				    for i in range(len(lowerCamelCase_       ) - 12       )       )


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




