

import unittest

import numpy as np

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

if is_vision_available():
	from PIL import Image

	from transformers import OwlViTImageProcessor


class 			_SCREAMING_SNAKE_CASE      (  unittest.TestCase):







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=7     ,						_SCREAMING_SNAKE_CASE=3     ,						_SCREAMING_SNAKE_CASE=18     ,						_SCREAMING_SNAKE_CASE=30     ,						_SCREAMING_SNAKE_CASE=400     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,						_SCREAMING_SNAKE_CASE=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,						_SCREAMING_SNAKE_CASE=True     ,						)->							Dict:
													lowerCamelCase_								=parent
													lowerCamelCase_								=batch_size
													lowerCamelCase_								=num_channels
													lowerCamelCase_								=image_size
													lowerCamelCase_								=min_resolution
													lowerCamelCase_								=max_resolution
													lowerCamelCase_								=do_resize
													lowerCamelCase_								=size if size is not None else {"""height""": 18, """width""": 18}
													lowerCamelCase_								=do_center_crop
													lowerCamelCase_								=crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
													lowerCamelCase_								=do_normalize
													lowerCamelCase_								=image_mean
													lowerCamelCase_								=image_std
													lowerCamelCase_								=do_convert_rgb







						def        _snake_case				(			self							)->							List[Any]:
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_convert_rgb": self.do_convert_rgb,
													}


@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__ ,    unittest.TestCase):
						_UpperCamelCase:List[str]	=		OwlViTImageProcessor if is_vision_available() else None







						def        _snake_case				(			self							)->							Optional[int]:
													lowerCamelCase_								=OwlViTImageProcessingTester(self							)







						@property
						def        _snake_case				(			self							)->							int:
													return self.image_processor_tester.prepare_image_processor_dict()







						def        _snake_case				(			self							)->							Union[str, Any]:
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_resize"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""size"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_center_crop"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""center_crop"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_normalize"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""image_mean"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""image_std"""							)							)
													self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,						"""do_convert_rgb"""							)							)







						def        _snake_case				(			self							)->							Optional[int]:
													lowerCamelCase_								=self.image_processing_class.from_dict(self.image_processor_dict							)
													self.assertEqual(image_processor.size     ,						{"""height""": 18, """width""": 18}							)
													self.assertEqual(image_processor.crop_size     ,						{"""height""": 18, """width""": 18}							)

													lowerCamelCase_								=self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84							)
													self.assertEqual(image_processor.size     ,						{"""height""": 42, """width""": 42}							)
													self.assertEqual(image_processor.crop_size     ,						{"""height""": 84, """width""": 84}							)







						def        _snake_case				(			self							)->							Tuple:
													# Initialize image_processing
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													# create random PIL images
													lowerCamelCase_								=prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_SCREAMING_SNAKE_CASE							)

													for image in image_inputs:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						Image.Image							)

													# Test not batched input
													lowerCamelCase_								=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.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)

													# Test batched
													lowerCamelCase_								=image_processing(_SCREAMING_SNAKE_CASE     ,						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.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)







						def        _snake_case				(			self							)->							Any:
													# Initialize image_processing
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													lowerCamelCase_								=prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_SCREAMING_SNAKE_CASE     ,						numpify=_SCREAMING_SNAKE_CASE							)
													for image in image_inputs:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						np.ndarray							)

													# Test not batched input
													lowerCamelCase_								=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.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)

													# Test batched
													lowerCamelCase_								=image_processing(_SCREAMING_SNAKE_CASE     ,						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.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)







						def        _snake_case				(			self							)->							str:
													# Initialize image_processing
													lowerCamelCase_								=self.image_processing_class(**self.image_processor_dict							)
													# create random PyTorch tensors
													lowerCamelCase_								=prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=_SCREAMING_SNAKE_CASE     ,						torchify=_SCREAMING_SNAKE_CASE							)
													for image in image_inputs:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,						torch.Tensor							)

													# Test not batched input
													lowerCamelCase_								=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.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)

													# Test batched
													lowerCamelCase_								=image_processing(_SCREAMING_SNAKE_CASE     ,						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.crop_size["""height"""],
													        self.image_processor_tester.crop_size["""width"""],
													    )     ,						)



from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
