



"""simple docstring"""





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 ):







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_     					=				size if size is not None else {"height": 18, "width": 18}
             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     :    Tuple       )					->		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__  (       SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	DPTImageProcessor if is_vision_available() else None



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


             '''simple docstring'''




             UpperCAmelCase_     					=				DPTImageProcessingTester(self       )



       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		Union[str, Any]:


             '''simple docstring'''




             return self.image_processor_tester.prepare_image_processor_dict()



       def 	lowercase__							(				self     :    List[str]       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Union[str, Any]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,							{"height": 18, "width": 18}       )

             UpperCAmelCase_     					=				self.image_processing_class.from_dict(self.image_processor_dict						,							size=42       )
             self.assertEqual(image_processor.size						,							{"height": 42, "width": 42}       )



       def 	lowercase__							(				self     :    str       )					->		Optional[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=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							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(_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 	lowercase__							(				self     :    Optional[int]       )					->		Tuple:


             '''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=_UpperCAmelCase						,							numpify=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							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(_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 	lowercase__							(				self     :    str       )					->		Dict:


             '''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=_UpperCAmelCase						,							torchify=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							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(_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"],
                 )						,							)






from __future__ import annotations




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):  # noqa: E741


				'''simple docstring'''
				while r - l > 1:
								lowercase_	    =   (l + r) // 2
								if v[m] >= key:
												lowercase_	    =   m
								else:
												lowercase_	    =   m  # noqa: E741
				return r




def 							a		(				snake_case__:   list[int]   ):


				'''simple docstring'''
				if len(snake_case__   ) == 0:
								return 0

				lowercase_	    =   [0] * len(snake_case__   )
				lowercase_	    =   1

				lowercase_	    =   v[0]

				for i in range(1		,  len(snake_case__   )   ):
								if v[i] < tail[0]:
												lowercase_	    =   v[i]
								elif v[i] > tail[length - 1]:
												lowercase_	    =   v[i]
												length += 1
								else:
												lowercase_	    =   v[i]

				return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




