


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 	__lowercase					(	unittest.TestCase	):





      def __init__(		self    :       Union[str, Any]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       str=7       ,							__lowerCamelCase    :       Tuple=3       ,							__lowerCamelCase    :       List[str]=1_8       ,							__lowerCamelCase    :       int=3_0       ,							__lowerCamelCase    :       str=4_0_0       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       int=None       ,							__lowerCamelCase    :       List[str]=True       ,							__lowerCamelCase    :       Union[str, Any]=[0.5, 0.5, 0.5]       ,							__lowerCamelCase    :       Any=[0.5, 0.5, 0.5]       ,							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  size if size is not None else {"""height""": 1_8, """width""": 1_8}
         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  min_resolution
         UpperCAmelCase      					=  max_resolution
         UpperCAmelCase      					=  do_resize
         UpperCAmelCase      					=  size
         UpperCAmelCase      					=  do_normalize
         UpperCAmelCase      					=  image_mean
         UpperCAmelCase      					=  image_std





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[int]:






         """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 	__lowercase					(	__snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							DPTImageProcessor if is_vision_available() else None





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  DPTImageProcessingTester(self							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> List[Any]:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def 							_lowercase					(		self    :       Tuple							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(__lowerCamelCase       ,							"""image_mean"""							)							)
         self.assertTrue(hasattr(__lowerCamelCase       ,							"""image_std"""							)							)
         self.assertTrue(hasattr(__lowerCamelCase       ,							"""do_normalize"""							)							)
         self.assertTrue(hasattr(__lowerCamelCase       ,							"""do_resize"""							)							)
         self.assertTrue(hasattr(__lowerCamelCase       ,							"""size"""							)							)





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






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,							{"""height""": 1_8, """width""": 1_8}							)

         UpperCAmelCase      					=  self.image_processing_class.from_dict(self.image_processor_dict       ,							size=4_2							)
         self.assertEqual(image_processor.size       ,							{"""height""": 4_2, """width""": 4_2}							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> List[Any]:






         """simple docstring"""







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

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





      def 							_lowercase					(		self    :       Optional[Any]							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  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.size["""height"""],
                 self.image_processor_tester.size["""width"""],
             )       ,							)





      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  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.size["""height"""],
                 self.image_processor_tester.size["""width"""],
             )       ,							)







def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ )

   for i in range(n - 1 ):
      for j in range(i + 1	,    lowerCAmelCase_ ):
         if arr[i] > arr[j]:
            num_inversions += 1

   return num_inversions
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   if len(lowerCAmelCase_ ) <= 1:
      return arr, 0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) // 2
   UpperCAmelCase      					=  arr[0:mid]
   UpperCAmelCase      					=  arr[mid:]

   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  _count_cross_inversions(lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  inversion_p + inversions_q + cross_inversions
   return c, num_inversions
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  UpperCAmelCase      					=  UpperCAmelCase      					=  0
   while i < len(lowerCAmelCase_ ) and j < len(lowerCAmelCase_ ):
      if p[i] > q[j]:
         # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
         # These are all inversions. The claim emerges from the
         # property that P is sorted.
         num_inversion += len(lowerCAmelCase_ ) - i
         r.append(q[j] )
         j += 1
      else:
         r.append(p[i] )
         i += 1

   if i < len(lowerCAmelCase_ ):
      r.extend(p[i:] )
   else:
      r.extend(q[j:] )

   return r, num_inversion
def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  [1_0, 2, 1, 5, 5, 2, 1_1]

   # this arr has 8 inversions:
   # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 8

   print("""number of inversions = """	,    lowerCAmelCase_ )

   # testing an array with zero inversion (a sorted arr_1)

   arr_a.sort()
   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 0
   print("""number of inversions = """	,    lowerCAmelCase_ )

   # an empty list should also have zero inversions
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 0
   print("""number of inversions = """	,    lowerCAmelCase_ )


if __name__ == "__main__":
 main()



