"""simple docstring"""







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 ViTImageProcessor


class      _lowercase						(				unittest.TestCase					):

				def __init__(       self    :      Union[str, Any]    ,      _lowercase    :      Union[str, Any]    ,      _lowercase    :      List[Any]=7    ,      _lowercase    :      Dict=3    ,      _lowercase    :      Optional[int]=1_8    ,      _lowercase    :      List[str]=3_0    ,      _lowercase    :      Union[str, Any]=4_0_0    ,      _lowercase    :      Any=True    ,      _lowercase    :      List[str]=None    ,      _lowercase    :      List[str]=True    ,      _lowercase    :      List[Any]=[0.5, 0.5, 0.5]    ,      _lowercase    :      Optional[int]=[0.5, 0.5, 0.5]    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						size if size is not None else {"""height""": 1_8, """width""": 1_8}
										UpperCAmelCase__   		=						parent
										UpperCAmelCase__   		=						batch_size
										UpperCAmelCase__   		=						num_channels
										UpperCAmelCase__   		=						image_size
										UpperCAmelCase__   		=						min_resolution
										UpperCAmelCase__   		=						max_resolution
										UpperCAmelCase__   		=						do_resize
										UpperCAmelCase__   		=						size
										UpperCAmelCase__   		=						do_normalize
										UpperCAmelCase__   		=						image_mean
										UpperCAmelCase__   		=						image_std

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}


@require_torch
@require_vision
class      _lowercase						(				a_ , unittest.TestCase					):
				lowerCamelCase__									=    ViTImageProcessor if is_vision_available() else None

				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						ViTImageProcessingTester(self       )

				@property
				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(snake_case__    ,      """image_mean"""       )       )
										self.assertTrue(hasattr(snake_case__    ,      """image_std"""       )       )
										self.assertTrue(hasattr(snake_case__    ,      """do_normalize"""       )       )
										self.assertTrue(hasattr(snake_case__    ,      """do_resize"""       )       )
										self.assertTrue(hasattr(snake_case__    ,      """size"""       )       )

				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.image_processing_class.from_dict(self.image_processor_dict       )
										self.assertEqual(image_processor.size    ,      {"""height""": 1_8, """width""": 1_8}       )

										UpperCAmelCase__   		=						self.image_processing_class.from_dict(self.image_processor_dict    ,      size=4_2       )
										self.assertEqual(image_processor.size    ,      {"""height""": 4_2, """width""": 4_2}       )

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										pass

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=snake_case__       )
										for image in image_inputs:
																self.assertIsInstance(snake_case__    ,      Image.Image       )

										# Test not batched input
										UpperCAmelCase__   		=						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
										UpperCAmelCase__   		=						image_processing(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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )    ,      )

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=snake_case__    ,      numpify=snake_case__       )
										for image in image_inputs:
																self.assertIsInstance(snake_case__    ,      np.ndarray       )

										# Test not batched input
										UpperCAmelCase__   		=						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
										UpperCAmelCase__   		=						image_processing(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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )    ,      )

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=snake_case__    ,      torchify=snake_case__       )
										for image in image_inputs:
																self.assertIsInstance(snake_case__    ,      torch.Tensor       )

										# Test not batched input
										UpperCAmelCase__   		=						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
										UpperCAmelCase__   		=						image_processing(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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )    ,      )


"""simple docstring"""







from __future__ import annotations


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						assert (
						    isinstance(UpperCAmelCase__						,     UpperCAmelCase__     ) and mask > 0
						), f'mask needs to be positive integer, your input {mask}'
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						mask

						while submask:
												all_submasks.append(UpperCAmelCase__     )
												UpperCAmelCase__   		=						(submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()

