



'''simple docstring'''







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 ViltImageProcessor




class 				__lowerCAmelCase(							unittest.TestCase      ):






			def __init__(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							int=7      ,       SCREAMING_SNAKE_CASE  :							Dict=3      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=18      ,       SCREAMING_SNAKE_CASE  :							List[Any]=30      ,       SCREAMING_SNAKE_CASE  :							List[str]=400      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=True      ,       SCREAMING_SNAKE_CASE  :							List[str]=None      ,       SCREAMING_SNAKE_CASE  :							Any=2      ,       SCREAMING_SNAKE_CASE  :							Any=True      ,       SCREAMING_SNAKE_CASE  :							Tuple=[0.5, 0.5, 0.5]      ,       SCREAMING_SNAKE_CASE  :							List[str]=[0.5, 0.5, 0.5]      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      size if size is not None else {'shortest_edge': 30}
					SCREAMING_SNAKE_CASE_						:List[Any]										=      parent
					SCREAMING_SNAKE_CASE_						:List[Any]										=      batch_size
					SCREAMING_SNAKE_CASE_						:Tuple										=      num_channels
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      image_size
					SCREAMING_SNAKE_CASE_						:List[Any]										=      min_resolution
					SCREAMING_SNAKE_CASE_						:Dict										=      max_resolution
					SCREAMING_SNAKE_CASE_						:Tuple										=      do_resize
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      size
					SCREAMING_SNAKE_CASE_						:str										=      size_divisor
					SCREAMING_SNAKE_CASE_						:Tuple										=      do_normalize
					SCREAMING_SNAKE_CASE_						:str										=      image_mean
					SCREAMING_SNAKE_CASE_						:List[Any]										=      image_std






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""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,
					    "size_divisor": self.size_divisor,
					}






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=False	):




					"""simple docstring"""







					if not batched:
							SCREAMING_SNAKE_CASE_						:List[str]										=      self.size['shortest_edge']
							SCREAMING_SNAKE_CASE_						:List[Any]										=      image_inputs[0]
							if isinstance(lowercase__      ,       Image.Image	):
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      image.size
							else:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      image.shape[1], image.shape[2]
							SCREAMING_SNAKE_CASE_						:Tuple										=      size / min(lowercase__      ,       lowercase__	)
							if h < w:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      size, scale * w
							else:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      scale * h, size

							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      int((1_333 / 800) * size	)
							if max(lowercase__      ,       lowercase__	) > max_size:
									SCREAMING_SNAKE_CASE_						:List[Any]										=      max_size / max(lowercase__      ,       lowercase__	)
									SCREAMING_SNAKE_CASE_						:int										=      newh * scale
									SCREAMING_SNAKE_CASE_						:Dict										=      neww * scale

							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      int(newh + 0.5	), int(neww + 0.5	)
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      (
							    newh // self.size_divisor * self.size_divisor,
							    neww // self.size_divisor * self.size_divisor,
							)

					else:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      []
							for image in image_inputs:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      self.get_expected_values([image]	)
									expected_values.append((expected_height, expected_width)	)
							SCREAMING_SNAKE_CASE_						:Any										=      max(lowercase__      ,       key=lambda SCREAMING_SNAKE_CASE	: item[0]	)[0]
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      max(lowercase__      ,       key=lambda SCREAMING_SNAKE_CASE	: item[1]	)[1]

					return expected_height, expected_width




@require_torch
@require_vision
class 				__lowerCAmelCase(							lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Union[str, Any]		=      ViltImageProcessor if is_vision_available() else None






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      ViltImageProcessingTester(self	)






			@property
			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.image_processing_class(**self.image_processor_dict	)
					self.assertTrue(hasattr(lowercase__      ,       'image_mean'	)	)
					self.assertTrue(hasattr(lowercase__      ,       'image_std'	)	)
					self.assertTrue(hasattr(lowercase__      ,       'do_normalize'	)	)
					self.assertTrue(hasattr(lowercase__      ,       'do_resize'	)	)
					self.assertTrue(hasattr(lowercase__      ,       'size'	)	)
					self.assertTrue(hasattr(lowercase__      ,       'size_divisor'	)	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.image_processing_class.from_dict(self.image_processor_dict	)
					self.assertEqual(image_processor.size      ,       {'shortest_edge': 30}	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.image_processing_class.from_dict(self.image_processor_dict      ,       size=42	)
					self.assertEqual(image_processor.size      ,       {'shortest_edge': 42}	)






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					pass






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.image_processing_class(**self.image_processor_dict	)
					# create random PIL images
					SCREAMING_SNAKE_CASE_						:str										=      prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowercase__	)
					for image in image_inputs:
							self.assertIsInstance(lowercase__      ,       Image.Image	)

					# Test not batched input
					SCREAMING_SNAKE_CASE_						:str										=      image_processing(image_inputs[0]      ,       return_tensors='pt'	).pixel_values

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.image_processor_tester.get_expected_values(lowercase__	)
					self.assertEqual(
					    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

					# Test batched
					SCREAMING_SNAKE_CASE_						:Tuple										=      image_processing(lowercase__      ,       return_tensors='pt'	).pixel_values

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      self.image_processor_tester.get_expected_values(lowercase__      ,       batched=lowercase__	)
					self.assertEqual(
					    encoded_images.shape      ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )      ,       )






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.image_processing_class(**self.image_processor_dict	)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowercase__      ,       numpify=lowercase__	)
					for image in image_inputs:
							self.assertIsInstance(lowercase__      ,       np.ndarray	)

					# Test not batched input
					SCREAMING_SNAKE_CASE_						:Dict										=      image_processing(image_inputs[0]      ,       return_tensors='pt'	).pixel_values

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      self.image_processor_tester.get_expected_values(lowercase__	)
					self.assertEqual(
					    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

					# Test batched
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      image_processing(lowercase__      ,       return_tensors='pt'	).pixel_values

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      self.image_processor_tester.get_expected_values(lowercase__      ,       batched=lowercase__	)
					self.assertEqual(
					    encoded_images.shape      ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )      ,       )






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







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

					# Test not batched input
					SCREAMING_SNAKE_CASE_						:str										=      image_processing(image_inputs[0]      ,       return_tensors='pt'	).pixel_values

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      self.image_processor_tester.get_expected_values(lowercase__	)
					self.assertEqual(
					    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

					# Test batched
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      image_processing(lowercase__      ,       return_tensors='pt'	).pixel_values

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.image_processor_tester.get_expected_values(lowercase__      ,       batched=lowercase__	)
					self.assertEqual(
					    encoded_images.shape      ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )      ,       )

def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if hue < 0 or hue > 3_6_0:
              raise Exception('hue should be between 0 and 360'      )

       if saturation < 0 or saturation > 1:
              raise Exception('saturation should be between 0 and 1'      )

       if value < 0 or value > 1:
              raise Exception('value should be between 0 and 1'      )

       lowerCAmelCase__	      =   value * saturation
       lowerCAmelCase__	      =   hue / 6_0
       lowerCAmelCase__	      =   chroma * (1 - abs(hue_section % 2 - 1      ))
       lowerCAmelCase__	      =   value - chroma

       if hue_section >= 0 and hue_section <= 1:
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
       elif hue_section > 1 and hue_section <= 2:
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
       elif hue_section > 2 and hue_section <= 3:
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
       elif hue_section > 3 and hue_section <= 4:
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
       elif hue_section > 4 and hue_section <= 5:
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
       else:
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )

       return [red, green, blue]






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if red < 0 or red > 2_5_5:
              raise Exception('red should be between 0 and 255'      )

       if green < 0 or green > 2_5_5:
              raise Exception('green should be between 0 and 255'      )

       if blue < 0 or blue > 2_5_5:
              raise Exception('blue should be between 0 and 255'      )

       lowerCAmelCase__	      =   red / 2_5_5
       lowerCAmelCase__	      =   green / 2_5_5
       lowerCAmelCase__	      =   blue / 2_5_5
       lowerCAmelCase__	      =   max(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   value - min(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   0 if value == 0 else chroma / value

       if chroma == 0:
              lowerCAmelCase__	      =   0.0
       elif value == float_red:
              lowerCAmelCase__	      =   6_0 * (0 + (float_green - float_blue) / chroma)
       elif value == float_green:
              lowerCAmelCase__	      =   6_0 * (2 + (float_blue - float_red) / chroma)
       else:
              lowerCAmelCase__	      =   6_0 * (4 + (float_red - float_green) / chroma)

       lowerCAmelCase__	      =   (hue + 3_6_0) % 3_6_0

       return [hue, saturation, value]






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   abs(hsv_a[0] - hsv_a[0]      ) < 0.2
       lowerCAmelCase__	      =   abs(hsv_a[1] - hsv_a[1]      ) < 0.0_0_2
       lowerCAmelCase__	      =   abs(hsv_a[2] - hsv_a[2]      ) < 0.0_0_2

       return check_hue and check_saturation and check_value
