



"""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       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def __init__(      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1_3    ,			SCREAMING_SNAKE_CASE_				:      Any=3    ,			SCREAMING_SNAKE_CASE_				:      Tuple=2_2_4    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=3_0    ,			SCREAMING_SNAKE_CASE_				:      Tuple=4_0_0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      Any=[0.5, 0.5, 0.5]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=[0.5, 0.5, 0.5]    ,			):
        lowerCAmelCase_   :				List[Any]								= size if size is not None else {'height': 1_8, 'width': 1_8}
        lowerCAmelCase_   :				Dict								= parent
        lowerCAmelCase_   :				Optional[int]								= batch_size
        lowerCAmelCase_   :				Optional[Any]								= num_channels
        lowerCAmelCase_   :				Any								= image_size
        lowerCAmelCase_   :				int								= min_resolution
        lowerCAmelCase_   :				List[Any]								= max_resolution
        lowerCAmelCase_   :				Optional[int]								= do_resize
        lowerCAmelCase_   :				Tuple								= size
        lowerCAmelCase_   :				Union[str, Any]								= do_normalize
        lowerCAmelCase_   :				Tuple								= image_mean
        lowerCAmelCase_   :				List[Any]								= image_std





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        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       UpperCamelCase__   (	lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ViTImageProcessor if is_vision_available() else None



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= EfficientFormerImageProcessorTester(self       )



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return self.image_proc_tester.prepare_image_processor_dict()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				List[str]								= self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(UpperCAmelCase__    ,			'image_mean'       )       )
        self.assertTrue(hasattr(UpperCAmelCase__    ,			'image_std'       )       )
        self.assertTrue(hasattr(UpperCAmelCase__    ,			'do_normalize'       )       )
        self.assertTrue(hasattr(UpperCAmelCase__    ,			'do_resize'       )       )
        self.assertTrue(hasattr(UpperCAmelCase__    ,			'size'       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        # Initialize image_processor
        lowerCAmelCase_   :				List[str]								= self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        lowerCAmelCase_   :				str								= prepare_image_inputs(self.image_proc_tester    ,			equal_resolution=UpperCAmelCase__       )
        for image in image_inputs:
          self.assertIsInstance(UpperCAmelCase__    ,			Image.Image       )

        # Test not batched input
        lowerCAmelCase_   :				Optional[Any]								= image_processor(image_inputs[0]    ,			return_tensors='pt'       ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )    ,			)

        # Test batched
        lowerCAmelCase_   :				str								= image_processor(UpperCAmelCase__    ,			return_tensors='pt'       ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        # Initialize image_processor
        lowerCAmelCase_   :				Any								= self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        lowerCAmelCase_   :				Optional[Any]								= prepare_image_inputs(self.image_proc_tester    ,			equal_resolution=UpperCAmelCase__    ,			numpify=UpperCAmelCase__       )
        for image in image_inputs:
          self.assertIsInstance(UpperCAmelCase__    ,			np.ndarray       )

        # Test not batched input
        lowerCAmelCase_   :				Tuple								= image_processor(image_inputs[0]    ,			return_tensors='pt'       ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )    ,			)

        # Test batched
        lowerCAmelCase_   :				Any								= image_processor(UpperCAmelCase__    ,			return_tensors='pt'       ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )    ,			)





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        # Initialize image_processor
        lowerCAmelCase_   :				Any								= self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        lowerCAmelCase_   :				int								= prepare_image_inputs(self.image_proc_tester    ,			equal_resolution=UpperCAmelCase__    ,			torchify=UpperCAmelCase__       )
        for image in image_inputs:
          self.assertIsInstance(UpperCAmelCase__    ,			torch.Tensor       )

        # Test not batched input
        lowerCAmelCase_   :				List[str]								= image_processor(image_inputs[0]    ,			return_tensors='pt'       ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )    ,			)

        # Test batched
        lowerCAmelCase_   :				str								= image_processor(UpperCAmelCase__    ,			return_tensors='pt'       ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size['height'],
                self.image_proc_tester.size['width'],
            )    ,			)









"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if number > 0:
										raise ValueError("input must be a negative integer"   )
					__SCREAMING_SNAKE_CASE						   =				len(bin(lowerCAmelCase_   )[3:]   )
					__SCREAMING_SNAKE_CASE						   =				bin(abs(lowerCAmelCase_   ) - (1 << binary_number_length)   )[3:]
					__SCREAMING_SNAKE_CASE						   =				(
					    (
					        "1"
					        + "0" * (binary_number_length - len(lowerCAmelCase_   ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()
