import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def __init__(					self     :				int    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				int=7    ,  _UpperCamelCase     :				Tuple=3    ,  _UpperCamelCase     :				Optional[int]=18    ,  _UpperCamelCase     :				Any=30    ,  _UpperCamelCase     :				Optional[Any]=400    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				Optional[int]=None    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				str=[0.5, 0.5, 0.5]    ,  _UpperCamelCase     :				int=[0.5, 0.5, 0.5]    ,  )				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						size if size is not None else {"""height""": 18, """width""": 18}
          _lowerCamelCase     :		Dict 						=						parent
          _lowerCamelCase     :		List[str] 						=						batch_size
          _lowerCamelCase     :		Union[str, Any] 						=						num_channels
          _lowerCamelCase     :		Dict 						=						image_size
          _lowerCamelCase     :		List[Any] 						=						min_resolution
          _lowerCamelCase     :		Tuple 						=						max_resolution
          _lowerCamelCase     :		int 						=						do_resize
          _lowerCamelCase     :		Tuple 						=						size
          _lowerCamelCase     :		Union[str, Any] 						=						do_normalize
          _lowerCamelCase     :		Dict 						=						image_mean
          _lowerCamelCase     :		str 						=						image_std




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->str:


          """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 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       DPTImageProcessor if is_vision_available() else None



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						DPTImageProcessingTester(self)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Optional[int]:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          _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)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size    ,  {"""height""": 18, """width""": 18})

          _lowerCamelCase     :		Optional[Any] 						=						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42)
          self.assertEqual(image_processor.size    ,  {"""height""": 42, """width""": 42})



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[str]:


          """simple docstring"""


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

          # Test not batched input
          _lowerCamelCase     :		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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )    ,  )

          # Test batched
          _lowerCamelCase     :		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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )    ,  )



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowerCamelCase     :		List[str] 						=						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
          _lowerCamelCase     :		List[Any] 						=						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
          _lowerCamelCase     :		List[str] 						=						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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowerCamelCase     :		Optional[int] 						=						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
          _lowerCamelCase     :		Tuple 						=						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
          _lowerCamelCase     :		str 						=						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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )    ,  )





import math







def 						A__  (					__A	):







    '''simple docstring'''
    assert isinstance(__A   , __A	) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    _lowerCamelCase     :		List[Any] 						=						range(3   , int(math.sqrt(__A	) + 1	)   , 2	)
    return not any(not number % i for i in odd_numbers	)







def 						A__  (					__A   , __A=1   , **__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						factor * value
    _lowerCamelCase     :		str 						=						value

    while not is_prime(__A	):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1   , **__A	)
    return value




