


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 MobileViTImageProcessor







class     _lowercase				(							unittest.TestCase):


							"""simple docstring"""




							def __init__(     self							:    Optional[int]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    int=7      ,   __lowerCamelCase							:    Optional[Any]=3      ,   __lowerCamelCase							:    Any=18      ,   __lowerCamelCase							:    Union[str, Any]=30      ,   __lowerCamelCase							:    Dict=400      ,   __lowerCamelCase							:    int=True      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    str=True      ,   __lowerCamelCase							:    str=None      ,   __lowerCamelCase							:    Tuple=True      ,   ):


													'''simple docstring'''



													lowerCamelCase__	:       Optional[Any]					=						size if size is not None else {"shortest_edge": 20}
													lowerCamelCase__	:       List[Any]					=						crop_size if crop_size is not None else {"height": 18, "width": 18}
													lowerCamelCase__	:       Any					=						parent
													lowerCamelCase__	:       Any					=						batch_size
													lowerCamelCase__	:       Dict					=						num_channels
													lowerCamelCase__	:       List[str]					=						image_size
													lowerCamelCase__	:       Dict					=						min_resolution
													lowerCamelCase__	:       str					=						max_resolution
													lowerCamelCase__	:       Dict					=						do_resize
													lowerCamelCase__	:       Optional[Any]					=						size
													lowerCamelCase__	:       int					=						do_center_crop
													lowerCamelCase__	:       Dict					=						crop_size
													lowerCamelCase__	:       Optional[int]					=						do_flip_channel_order





							def 		lowerCAmelCase  (     self							:    int ):


													'''simple docstring'''



													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													    "do_flip_channel_order": self.do_flip_channel_order,
													}







@require_torch
@require_vision
class     _lowercase				(							lowercase__							,						unittest.TestCase):


							"""simple docstring"""




							A__      		=    MobileViTImageProcessor if is_vision_available() else None





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


													'''simple docstring'''



													lowerCamelCase__	:       Optional[Any]					=						MobileViTImageProcessingTester(self )





							@property
							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													return self.image_processor_tester.prepare_image_processor_dict()





							def 		lowerCAmelCase  (     self							:    List[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       Any					=						self.image_processing_class(**self.image_processor_dict )
													self.assertTrue(hasattr(__lowerCamelCase      ,   "do_resize" ) )
													self.assertTrue(hasattr(__lowerCamelCase      ,   "size" ) )
													self.assertTrue(hasattr(__lowerCamelCase      ,   "do_center_crop" ) )
													self.assertTrue(hasattr(__lowerCamelCase      ,   "center_crop" ) )
													self.assertTrue(hasattr(__lowerCamelCase      ,   "do_flip_channel_order" ) )





							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       Optional[int]					=						self.image_processing_class.from_dict(self.image_processor_dict )
													self.assertEqual(image_processor.size      ,   {"shortest_edge": 20} )
													self.assertEqual(image_processor.crop_size      ,   {"height": 18, "width": 18} )

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





							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													pass





							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       Optional[int]					=						self.image_processing_class(**self.image_processor_dict )
													# create random PIL images
													lowerCamelCase__	:       Optional[int]					=						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__	:       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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )      ,   )

													# Test batched
													lowerCamelCase__	:       str					=						image_processing(__lowerCamelCase      ,   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 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       Tuple					=						self.image_processing_class(**self.image_processor_dict )
													# create random numpy tensors
													lowerCamelCase__	:       List[str]					=						prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase      ,   numpify=__lowerCamelCase )
													for image in image_inputs:
																			self.assertIsInstance(__lowerCamelCase      ,   np.ndarray )

													# Test not batched input
													lowerCamelCase__	:       int					=						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__	:       Dict					=						image_processing(__lowerCamelCase      ,   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 		lowerCAmelCase  (     self							:    Dict ):


													'''simple docstring'''



													lowerCamelCase__	:       Any					=						self.image_processing_class(**self.image_processor_dict )
													# create random PyTorch tensors
													lowerCamelCase__	:       Tuple					=						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__	:       int					=						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__	:       Any					=						image_processing(__lowerCamelCase      ,   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    lowercase_	(	_A    :					int					,      _A    :					int		):

      """simple docstring"""




      if a < 0 or b < 0:
            raise ValueError("the value of both inputs must be positive"		)

      lowerCamelCase__	:       List[str]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"
      lowerCamelCase__	:       List[Any]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"

      lowerCamelCase__	:       List[Any]					=						max(len(_A		)					,      len(_A		)		)

      return "0b" + "".join(
          str(int(char_a != char_b		)		)
          for char_a, char_b in zip(a_binary.zfill(_A		)					,      b_binary.zfill(_A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


