


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 PoolFormerImageProcessor



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=0.9 ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		size if size is not None else {'''shortest_edge''': 30}
              UpperCamelCase    :	Optional[Any]   	=		crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
              UpperCamelCase    :	List[Any]   	=		parent
              UpperCamelCase    :	str   	=		batch_size
              UpperCamelCase    :	int   	=		num_channels
              UpperCamelCase    :	Any   	=		min_resolution
              UpperCamelCase    :	int   	=		max_resolution
              UpperCamelCase    :	Any   	=		do_resize_and_center_crop
              UpperCamelCase    :	List[Any]   	=		size
              UpperCamelCase    :	Optional[Any]   	=		crop_pct
              UpperCamelCase    :	str   	=		crop_size
              UpperCamelCase    :	Dict   	=		do_normalize
              UpperCamelCase    :	Dict   	=		image_mean
              UpperCamelCase    :	List[str]   	=		image_std






       def  _lowercase   (     self						):




              """simple docstring"""




              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }




@require_torch
@require_vision
class    UpperCAmelCase_	(							__snake_case,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					PoolFormerImageProcessor if is_vision_available() else None






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		PoolFormerImageProcessingTester(self						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.image_processor_tester.prepare_image_processor_dict()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.image_processing_class(**self.image_processor_dict						)
              self.assertTrue(hasattr(__UpperCamelCase ,					'''do_resize_and_center_crop'''						)						)
              self.assertTrue(hasattr(__UpperCamelCase ,					'''size'''						)						)
              self.assertTrue(hasattr(__UpperCamelCase ,					'''crop_pct'''						)						)
              self.assertTrue(hasattr(__UpperCamelCase ,					'''do_normalize'''						)						)
              self.assertTrue(hasattr(__UpperCamelCase ,					'''image_mean'''						)						)
              self.assertTrue(hasattr(__UpperCamelCase ,					'''image_std'''						)						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		self.image_processing_class.from_dict(self.image_processor_dict						)
              self.assertEqual(image_processor.size ,					{'''shortest_edge''': 30}						)
              self.assertEqual(image_processor.crop_size ,					{'''height''': 30, '''width''': 30}						)

              UpperCamelCase    :	str   	=		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  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.image_processing_class(**self.image_processor_dict						)
              # create random PIL images
              UpperCamelCase    :	str   	=		prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCamelCase						)
              for image in image_inputs:
                     self.assertIsInstance(__UpperCamelCase ,					Image.Image						)

              # Test not batched input
              UpperCamelCase    :	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
              UpperCamelCase    :	Union[str, Any]   	=		image_processing(__UpperCamelCase ,					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   (     self						):




              """simple docstring"""




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

              # Test not batched input
              UpperCamelCase    :	Dict   	=		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
              UpperCamelCase    :	Tuple   	=		image_processing(__UpperCamelCase ,					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   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.image_processing_class(**self.image_processor_dict						)
              # create random PyTorch tensors
              UpperCamelCase    :	str   	=		prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCamelCase ,					torchify=__UpperCamelCase						)
              for image in image_inputs:
                     self.assertIsInstance(__UpperCamelCase ,					torch.Tensor						)

              # Test not batched input
              UpperCamelCase    :	Optional[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
              UpperCamelCase    :	Optional[int]   	=		image_processing(__UpperCamelCase ,					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      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





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


if __name__ == "__main__":
  import doctest

  doctest.testmod()

