
'''simple docstring'''
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:
				__lowerCAmelCase												=	False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor


class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3  ,  UpperCAmelCase=18  ,  UpperCAmelCase=30  ,  UpperCAmelCase=400  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  ):
														_snake_case       =  size if size is not None else {"""height""": 20, """width""": 20}
														_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       =  size
														_snake_case       =  do_normalize
														_snake_case       =  do_convert_rgb
														_snake_case       =  [512, 1024, 2048, 4096]
														_snake_case       =  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}

							def 	lowercase							(self  ):
														return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





							def 	lowercase							(self  ):
														_snake_case       =  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
														_snake_case       =  Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase  ).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       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						PixaStructImageProcessor if is_vision_available() else None

							def 	lowercase							(self  ):
														_snake_case       =  PixaStructImageProcessingTester(self  )

							@property
							def 	lowercase							(self  ):
														return self.image_processor_tester.prepare_image_processor_dict()

							def 	lowercase							(self  ):
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """do_normalize"""  )  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """do_convert_rgb"""  )  )

							def 	lowercase							(self  ):
														_snake_case       =  self.image_processor_tester.prepare_dummy_image()

														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														_snake_case       =  2048

														_snake_case       =  image_processor(UpperCAmelCase  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  )
														self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,  torch.tensor(0.0606  )  ,  atol=1e-3  ,  rtol=1e-3  )  )

							def 	lowercase							(self  ):
														# Initialize image_processor
														_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=UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase  ,  Image.Image  )

														# Test not batched input
														_snake_case       =  (
														    (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
																					_snake_case       =  image_processor(
																					    image_inputs[0]  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (1, max_patch, expected_hidden_dim)  ,  )

																					# Test batched
																					_snake_case       =  image_processor(
																					    UpperCAmelCase  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,  )

							def 	lowercase							(self  ):
														# Initialize image_processor
														_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=UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase  ,  Image.Image  )

														# Test not batched input
														_snake_case       =  (
														    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
														    * self.image_processor_tester.num_channels
														) + 2

														_snake_case       =  True

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

																					_snake_case       =  """Hello"""

																					_snake_case       =  image_processor(
																					    image_inputs[0]  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ,  header_text=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (1, max_patch, expected_hidden_dim)  ,  )

																					# Test batched
																					_snake_case       =  image_processor(
																					    UpperCAmelCase  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ,  header_text=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,  )

							def 	lowercase							(self  ):
														# Initialize image_processor
														_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=UpperCAmelCase  ,  numpify=UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase  ,  np.ndarray  )

														_snake_case       =  (
														    (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
																					_snake_case       =  image_processor(
																					    image_inputs[0]  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (1, max_patch, expected_hidden_dim)  ,  )

																					# Test batched
																					_snake_case       =  image_processor(
																					    UpperCAmelCase  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,  )





							def 	lowercase							(self  ):
														# Initialize image_processor
														_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=UpperCAmelCase  ,  torchify=UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase  ,  torch.Tensor  )

														# Test not batched input
														_snake_case       =  (
														    (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
																					_snake_case       =  image_processor(
																					    image_inputs[0]  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (1, max_patch, expected_hidden_dim)  ,  )

																					# Test batched
																					_snake_case       =  image_processor(
																					    UpperCAmelCase  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).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       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						PixaStructImageProcessor if is_vision_available() else None

							def 	lowercase							(self  ):
														_snake_case       =  PixaStructImageProcessingTester(self  ,  num_channels=4  )
														_snake_case       =  3

							@property
							def 	lowercase							(self  ):
														return self.image_processor_tester.prepare_image_processor_dict()

							def 	lowercase							(self  ):
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """do_normalize"""  )  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """do_convert_rgb"""  )  )





							def 	lowercase							(self  ):
														# Initialize image_processor
														_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=UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase  ,  Image.Image  )

														# Test not batched input
														_snake_case       =  (
														    (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
																					_snake_case       =  image_processor(
																					    image_inputs[0]  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (1, max_patch, expected_hidden_dim)  ,  )

																					# Test batched
																					_snake_case       =  image_processor(
																					    UpperCAmelCase  ,  return_tensors="""pt"""  ,  max_patches=UpperCAmelCase  ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape  ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,  )

'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														if dst_width < 0 or dst_height < 0:
																					raise ValueError("""Destination width/height should be > 0"""  )

														_snake_case       =  img
														_snake_case       =  img.shape[1]
														_snake_case       =  img.shape[0]
														_snake_case       =  dst_width
														_snake_case       =  dst_height

														_snake_case       =  self.src_w / self.dst_w
														_snake_case       =  self.src_h / self.dst_h

														_snake_case       =  _snake_case       =  (
														    np.ones((self.dst_h, self.dst_w, 3)  ,  np.uinta  ) * 255
														)

							def 	lowercase							(self  )					->       List[Any]:
														for i in range(self.dst_h  ):
																					for j in range(self.dst_w  ):
																												_snake_case       =  self.img[self.get_y(UpperCAmelCase  )][self.get_x(UpperCAmelCase  )]

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_x * x  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_y * y  )


if __name__ == "__main__":
				__lowerCAmelCase							,    __lowerCAmelCase												=	800, 600
				__lowerCAmelCase												=	imread('image_data/lena.jpg', 1)
				__lowerCAmelCase												=	NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()