






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 __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=7       ,    UpperCAmelCase=3       ,    UpperCAmelCase=18       ,    UpperCAmelCase=30       ,    UpperCAmelCase=400       ,    UpperCAmelCase=True       ,    UpperCAmelCase=None       ,    UpperCAmelCase=True       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    UpperCAmelCase=[0.5, 0.5, 0.5]       ,    UpperCAmelCase=[0.5, 0.5, 0.5]       ,    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							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 __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      DonutImageProcessor if is_vision_available() else None

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			DonutImageProcessingTester(self    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self.image_processor_tester.prepare_image_processor_dict()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			self.image_processing_class.from_dict(self.image_processor_dict       ,    size=(42, 84)    )
							self.assertEqual(image_processor.size       ,    {'height': 84, 'width': 42}    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@is_flaky()
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

	@is_flaky()
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@is_flaky()
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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






'''simple docstring'''







import os





def 						snake_case_							():
    UpperCAmelCase			       =		os.path.join(os.path.dirname(_a     )    ,   '''num.txt'''     )
    with open(_a     ) as file_hand:
        return str(sum(int(_a     ) for line in file_hand     )     )[:1_0]


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




