
'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=7    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=18    ,   lowerCAmelCase_=30    ,   lowerCAmelCase_=4_00    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   ):





								"""simple docstring"""






								_snake_case   						=		parent
								_snake_case   						=		batch_size
								_snake_case   						=		num_channels
								_snake_case   						=		image_size
								_snake_case   						=		min_resolution
								_snake_case   						=		max_resolution
								_snake_case   						=		do_resize
								_snake_case   						=		size if size is not None else {'height': 18, 'width': 20}
								_snake_case   						=		do_thumbnail
								_snake_case   						=		do_align_axis
								_snake_case   						=		do_pad
								_snake_case   						=		do_normalize
								_snake_case   						=		image_mean
								_snake_case   						=		image_std




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}


@require_torch
@require_vision
class 						__UpperCAmelCase      (					_lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       DonutImageProcessor if is_vision_available() else None


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		DonutImageProcessingTester(self  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_resize'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'size'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_thumbnail'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_align_long_axis'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_pad'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_normalize'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'image_mean'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'image_std'  )  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size    ,   {'height': 18, 'width': 20}  )

								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42  )
								self.assertEqual(image_processor.size    ,   {'height': 42, 'width': 42}  )

								# Previous config had dimensions in (width, height) order
								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict    ,   size=(42, 84)  )
								self.assertEqual(image_processor.size    ,   {'height': 84, 'width': 42}  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								pass


							@is_flaky()
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   Image.Image  )

								# Test not batched input
								_snake_case   						=		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.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		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.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )


							@is_flaky()
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								_snake_case   						=		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
								_snake_case   						=		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.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		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.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )




							@is_flaky()
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								_snake_case   						=		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
								_snake_case   						=		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.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		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.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )







"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

				# Validation
				if not isinstance(lowercase__							,    lowercase__		) or len(lowercase__		) == 0:
								raise ValueError('the string should be not empty string'		)

				if not isinstance(lowercase__							,    lowercase__		) or not all(
				    isinstance(lowercase__							,    lowercase__		) and len(lowercase__		) > 0 for item in words		):
								raise ValueError('the words should be a list of non-empty strings'		)

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

				# Dynamic programming method
				@functools.cache
				def is_breakable(lowercase__		) -> bool:
								if index == len_string:
												return True

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

												if trie_node.get(lowercase__							,    lowercase__		) and is_breakable(i + 1		):
																return True

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()