


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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BlipImageProcessor







class    lowerCamelCase__     ( unittest.TestCase):





					def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase=7	,  UpperCAmelCase=3	,  UpperCAmelCase=1_8	,  UpperCAmelCase=3_0	,  UpperCAmelCase=4_0_0	,  UpperCAmelCase=True	,  UpperCAmelCase=None	,  UpperCAmelCase=True	,  UpperCAmelCase=False	,  UpperCAmelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]	,  UpperCAmelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]	,  UpperCAmelCase=True	,  )  ->     List[str]:
						_lowercase							   =size if size is not None else {'''height''': 2_0, '''width''': 2_0}
						_lowercase							   =parent
						_lowercase							   =batch_size
						_lowercase							   =num_channels
						_lowercase							   =image_size
						_lowercase							   =min_resolution
						_lowercase							   =max_resolution
						_lowercase							   =do_resize
						_lowercase							   =size
						_lowercase							   =do_normalize
						_lowercase							   =image_mean
						_lowercase							   =image_std
						_lowercase							   =do_pad
						_lowercase							   =do_convert_rgb





					def   __A      (self )  ->     Optional[int]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						    "do_pad": self.do_pad,
						}





					def   __A      (self	,  UpperCAmelCase=False	,  UpperCAmelCase=False	,  UpperCAmelCase=False )  ->     Union[str, Any]:
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							_lowercase							   =[]
							for i in range(self.batch_size ):
								image_inputs.append(
								    np.random.randint(
								        2_5_5	,  size=(self.num_channels, self.max_resolution, self.max_resolution)	,  dtype=np.uinta ) )
						else:
							_lowercase							   =[]
							for i in range(self.batch_size ):
								_lowercase   ,					_lowercase							   =np.random.choice(np.arange(self.min_resolution	,  self.max_resolution )	,  2 )
								image_inputs.append(np.random.randint(2_5_5	,  size=(self.num_channels, width, height)	,  dtype=np.uinta ) )

						if not numpify and not torchify:
							# PIL expects the channel dimension as last dimension
							_lowercase							   =[Image.fromarray(np.moveaxis(a_	,  0	,  -1 ) ) for x in image_inputs]

						if torchify:
							_lowercase							   =[torch.from_numpy(a_ ) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class    lowerCamelCase__     ( SCREAMING_SNAKE_CASE__     ,							unittest.TestCase):
					SCREAMING_SNAKE_CASE__       =    BlipImageProcessor if is_vision_available() else None





					def   __A      (self )  ->     Any:
						_lowercase							   =BlipImageProcessingTester(self )





					@property
					def   __A      (self )  ->     Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()





					def   __A      (self )  ->     Tuple:
						_lowercase							   =self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(a_	,  '''do_resize''' ) )
						self.assertTrue(hasattr(a_	,  '''size''' ) )
						self.assertTrue(hasattr(a_	,  '''do_normalize''' ) )
						self.assertTrue(hasattr(a_	,  '''image_mean''' ) )
						self.assertTrue(hasattr(a_	,  '''image_std''' ) )
						self.assertTrue(hasattr(a_	,  '''do_convert_rgb''' ) )





					def   __A      (self )  ->     Dict:
						pass





					def   __A      (self )  ->     Optional[Any]:
						_lowercase							   =self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						_lowercase							   =self.image_processor_tester.prepare_inputs(equal_resolution=a_ )
						for image in image_inputs:
							self.assertIsInstance(a_	,  Image.Image )

						# Test not batched input
						_lowercase							   =image_processor(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
						_lowercase							   =image_processor(a_	,  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 )  ->     Any:
						_lowercase							   =self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						_lowercase							   =self.image_processor_tester.prepare_inputs(equal_resolution=a_	,  numpify=a_ )
						for image in image_inputs:
							self.assertIsInstance(a_	,  np.ndarray )

						# Test not batched input
						_lowercase							   =image_processor(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
						_lowercase							   =image_processor(a_	,  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]:
						_lowercase							   =self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						_lowercase							   =self.image_processor_tester.prepare_inputs(equal_resolution=a_	,  torchify=a_ )
						for image in image_inputs:
							self.assertIsInstance(a_	,  torch.Tensor )

						# Test not batched input
						_lowercase							   =image_processor(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
						_lowercase							   =image_processor(a_	,  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'''],
						    )	,  )







@require_torch
@require_vision
class    lowerCamelCase__     ( SCREAMING_SNAKE_CASE__     ,							unittest.TestCase):
					SCREAMING_SNAKE_CASE__       =    BlipImageProcessor if is_vision_available() else None





					def   __A      (self )  ->     Any:
						_lowercase							   =BlipImageProcessingTester(self	,  num_channels=4 )
						_lowercase							   =3





					@property
					def   __A      (self )  ->     Dict:
						return self.image_processor_tester.prepare_image_processor_dict()





					def   __A      (self )  ->     Dict:
						_lowercase							   =self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(a_	,  '''do_resize''' ) )
						self.assertTrue(hasattr(a_	,  '''size''' ) )
						self.assertTrue(hasattr(a_	,  '''do_normalize''' ) )
						self.assertTrue(hasattr(a_	,  '''image_mean''' ) )
						self.assertTrue(hasattr(a_	,  '''image_std''' ) )
						self.assertTrue(hasattr(a_	,  '''do_convert_rgb''' ) )





					def   __A      (self )  ->     Any:
						pass





					def   __A      (self )  ->     Union[str, Any]:
						_lowercase							   =self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						_lowercase							   =self.image_processor_tester.prepare_inputs(equal_resolution=a_ )
						for image in image_inputs:
							self.assertIsInstance(a_	,  Image.Image )

						# Test not batched input
						_lowercase							   =image_processor(image_inputs[0]	,  return_tensors='''pt''' ).pixel_values
						self.assertEqual(
						    encoded_images.shape	,  (
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )	,  )

						# Test batched
						_lowercase							   =image_processor(a_	,  return_tensors='''pt''' ).pixel_values
						self.assertEqual(
						    encoded_images.shape	,  (
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )	,  )





def        UpperCAmelCase_      (							__snake_case		)  ->    bool:







 """simple docstring"""


 if not isinstance(__snake_case  ,		__snake_case		):
  _lowercase							   =F"Input value of [number={number}] must be an integer"
  raise TypeError(__snake_case		)
 if number < 0 or number % 2 == 1:
  return False
 _lowercase							   =int(number**0.5		)
 return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

