

"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				int=7   ,lowercase_			:				str=3   ,lowercase_			:				List[Any]=1_8   ,lowercase_			:				Any=3_0   ,lowercase_			:				str=4_0_0   ,lowercase_			:				List[str]=True   ,lowercase_			:				int=None   ,lowercase_			:				Tuple=True   ,lowercase_			:				Union[str, Any]=False   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				str=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=[0.5, 0.5, 0.5]   ,):
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						Any  				=	num_channels
						lowerCAmelCase__	:						Tuple  				=	image_size
						lowerCAmelCase__	:						str  				=	min_resolution
						lowerCAmelCase__	:						Optional[Any]  				=	max_resolution
						lowerCAmelCase__	:						str  				=	do_resize
						lowerCAmelCase__	:						Dict  				=	size if size is not None else {'''height''': 1_8, '''width''': 2_0}
						lowerCAmelCase__	:						Union[str, Any]  				=	do_thumbnail
						lowerCAmelCase__	:						List[Any]  				=	do_align_axis
						lowerCAmelCase__	:						List[str]  				=	do_pad
						lowerCAmelCase__	:						List[str]  				=	do_normalize
						lowerCAmelCase__	:						Union[str, Any]  				=	image_mean
						lowerCAmelCase__	:						Tuple  				=	image_std







					def        __lowerCAmelCase (       self			:				List[Any]		):
						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 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       DonutImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Dict  				=	DonutImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_thumbnail'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_align_long_axis'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Any  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''height''': 1_8, '''width''': 2_0}		)

						lowerCAmelCase__	:						Optional[int]  				=	self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2		)
						self.assertEqual(image_processor.size   ,{'''height''': 4_2, '''width''': 4_2}		)

						# Previous config had dimensions in (width, height) order
						lowerCAmelCase__	:						int  				=	self.image_processing_class.from_dict(self.image_processor_dict   ,size=(4_2, 8_4)		)
						self.assertEqual(image_processor.size   ,{'''height''': 8_4, '''width''': 4_2}		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						pass







					@is_flaky()
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						List[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	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
						lowerCAmelCase__	:						List[Any]  				=	image_processing(lowercase_   ,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			:				Tuple		):
						# Initialize image_processing
						lowerCAmelCase__	:						int  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Optional[int]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

						# Test not batched input
						lowerCAmelCase__	:						List[str]  				=	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
						lowerCAmelCase__	:						Optional[Any]  				=	image_processing(lowercase_   ,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			:				str		):
						# Initialize image_processing
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						int  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	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
						lowerCAmelCase__	:						Any  				=	image_processing(lowercase_   ,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'''],
						    )   ,)











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




