



"""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:
			__SCREAMING_SNAKE_CASE  :						Any    							=      False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=7				,       lowerCamelCase__=3				,       lowerCamelCase__=1_8				,       lowerCamelCase__=3_0				,       lowerCamelCase__=4_0_0				,       lowerCamelCase__=None				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				min_resolution
									_lowerCamelCase        =				max_resolution
									_lowerCamelCase        =				size
									_lowerCamelCase        =				do_normalize
									_lowerCamelCase        =				do_convert_rgb
									_lowerCamelCase        =				[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
									_lowerCamelCase        =				patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}

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





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
									_lowerCamelCase        =				Image.open(requests.get(lowerCamelCase__				,       stream=lowerCamelCase__   ).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_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= PixaStructImageProcessor if is_vision_available() else None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				PixaStructImageProcessingTester(self   )

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''do_normalize'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''do_convert_rgb'''   )   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processor_tester.prepare_dummy_image()

									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									_lowerCamelCase        =				2_0_4_8

									_lowerCamelCase        =				image_processor(lowerCamelCase__				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   )
									self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,       torch.tensor(0.0_6_0_6   )				,       atol=1e-3				,       rtol=1e-3   )   )

					def 			snake_case__      (    self   ):
									# Initialize image_processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       Image.Image   )

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

													# Test batched
													_lowerCamelCase        =				image_processor(
													    lowerCamelCase__				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,       )

					def 			snake_case__      (    self   ):
									# Initialize image_processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       Image.Image   )

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

									_lowerCamelCase        =				True

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

													_lowerCamelCase        =				'''Hello'''

													_lowerCamelCase        =				image_processor(
													    image_inputs[0]				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__				,       header_text=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (1, max_patch, expected_hidden_dim)				,       )

													# Test batched
													_lowerCamelCase        =				image_processor(
													    lowerCamelCase__				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__				,       header_text=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,       )

					def 			snake_case__      (    self   ):
									# Initialize image_processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__				,       numpify=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       np.ndarray   )

									_lowerCamelCase        =				(
									    (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
													_lowerCamelCase        =				image_processor(
													    image_inputs[0]				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (1, max_patch, expected_hidden_dim)				,       )

													# Test batched
													_lowerCamelCase        =				image_processor(
													    lowerCamelCase__				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,       )





					def 			snake_case__      (    self   ):
									# Initialize image_processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									_lowerCamelCase        =				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
									_lowerCamelCase        =				(
									    (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
													_lowerCamelCase        =				image_processor(
													    image_inputs[0]				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (1, max_patch, expected_hidden_dim)				,       )

													# Test batched
													_lowerCamelCase        =				image_processor(
													    lowerCamelCase__				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   ).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_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Any										= PixaStructImageProcessor if is_vision_available() else None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				PixaStructImageProcessingTester(self				,       num_channels=4   )
									_lowerCamelCase        =				3

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

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''do_normalize'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''do_convert_rgb'''   )   )





					def 			snake_case__      (    self   ):
									# Initialize image_processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       Image.Image   )

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

													# Test batched
													_lowerCamelCase        =				image_processor(
													    lowerCamelCase__				,       return_tensors='''pt'''				,       max_patches=lowerCamelCase__   ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,       )





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return 1 / (1 + np.exp(-vector							))



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return vector * sigmoid(1.7_0_2 * vector							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
