



'''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      ):
    """simple docstring"""







    def __init__(       self   :	Union[str, Any]					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	Optional[int]=7					,	snake_case_   :	Optional[Any]=3					,	snake_case_   :	Optional[Any]=18					,	snake_case_   :	int=30					,	snake_case_   :	Tuple=400					,	snake_case_   :	Tuple=True					,	snake_case_   :	Union[str, Any]=None					,	snake_case_   :	str=True					,	snake_case_   :	Optional[Any]=False					,	snake_case_   :	Union[str, Any]=True					,	snake_case_   :	Any=True					,	snake_case_   :	Optional[Any]=[0.5, 0.5, 0.5]					,	snake_case_   :	Tuple=[0.5, 0.5, 0.5]					,	):
          snake_case__    :       Union[str, Any]       		= parent
          snake_case__    :       Union[str, Any]       		= batch_size
          snake_case__    :       Any       		= num_channels
          snake_case__    :       Tuple       		= image_size
          snake_case__    :       Tuple       		= min_resolution
          snake_case__    :       Tuple       		= max_resolution
          snake_case__    :       Any       		= do_resize
          snake_case__    :       Tuple       		= size if size is not None else {"""height""": 18, """width""": 20}
          snake_case__    :       Optional[int]       		= do_thumbnail
          snake_case__    :       Optional[int]       		= do_align_axis
          snake_case__    :       List[Any]       		= do_pad
          snake_case__    :       Any       		= do_normalize
          snake_case__    :       int       		= image_mean
          snake_case__    :       Dict       		= image_std




    def lowerCamelCase			(       self   :	Optional[int]		):
          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_			(			UpperCAmelCase__						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						DonutImageProcessor if is_vision_available() else None




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Tuple       		= DonutImageProcessingTester(self		)




    @property
    def lowerCamelCase			(       self   :	str		):
          return self.image_processor_tester.prepare_image_processor_dict()




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Dict       		= 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		):
          snake_case__    :       Optional[Any]       		= self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size					,	{"""height""": 18, """width""": 20}		)

          snake_case__    :       List[str]       		= 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__    :       int       		= 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   :	List[Any]		):
          pass




    @is_flaky()
    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       Any       		= self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          snake_case__    :       Optional[int]       		= prepare_image_inputs(self.image_processor_tester					,	equal_resolution=__lowercase		)
          for image in image_inputs:
                self.assertIsInstance(__lowercase					,	Image.Image		)

          # Test not batched input
          snake_case__    :       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
          snake_case__    :       Union[str, 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   :	Optional[int]		):
          snake_case__    :       Tuple       		= self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          snake_case__    :       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
          snake_case__    :       Tuple       		= 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__    :       Union[str, 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   :	List[str]		):
          snake_case__    :       str       		= self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          snake_case__    :       Dict       		= 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
          snake_case__    :       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
          snake_case__    :       Dict       		= 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 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
