



import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor


class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				def __init__(			self  ,			snake_case__  ,			snake_case__=7  ,			snake_case__=3  ,			snake_case__=30  ,			snake_case__=400  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__=[0.5, 0.5, 0.5]  ,			snake_case__=[0.5, 0.5, 0.5]  ,			snake_case__=False  ,			):

									'''simple docstring'''


									UpperCamelCase_			= size if size is not None else {"height": 30, "width": 30}
									UpperCamelCase_			= parent
									UpperCamelCase_			= batch_size
									UpperCamelCase_			= num_channels
									UpperCamelCase_			= min_resolution
									UpperCamelCase_			= max_resolution
									UpperCamelCase_			= do_resize
									UpperCamelCase_			= size
									UpperCamelCase_			= do_normalize
									UpperCamelCase_			= image_mean
									UpperCamelCase_			= image_std
									UpperCamelCase_			= do_reduce_labels

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									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_reduce_labels": self.do_reduce_labels,
									}




def  _lowerCAmelCase     ():
					UpperCamelCase_			= load_dataset("hf-internal-testing/fixtures_ade20k" ,						split="test")

					UpperCamelCase_			= Image.open(dataset[0]["file"])
					UpperCamelCase_			= Image.open(dataset[1]["file"])

					return image, map



def  _lowerCAmelCase     ():
					UpperCamelCase_			= load_dataset("hf-internal-testing/fixtures_ade20k" ,						split="test")

					UpperCamelCase_			= Image.open(dataset[0]["file"])
					UpperCamelCase_			= Image.open(dataset[1]["file"])
					UpperCamelCase_			= Image.open(dataset[2]["file"])
					UpperCamelCase_			= Image.open(dataset[3]["file"])

					return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class    _lowercase (a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    SegformerImageProcessor if is_vision_available() else None

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SegformerImageProcessingTester(self     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(snake_case__  ,			"do_resize"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"size"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"do_normalize"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"image_mean"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"image_std"     )     )
									self.assertTrue(hasattr(snake_case__  ,			"do_reduce_labels"     )     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size  ,			{"height": 30, "width": 30}     )
									self.assertEqual(image_processor.do_reduce_labels  ,			snake_case__     )

									UpperCamelCase_			= self.image_processing_class.from_dict(self.image_processor_dict  ,			size=42  ,			reduce_labels=snake_case__     )
									self.assertEqual(image_processor.size  ,			{"height": 42, "width": 42}     )
									self.assertEqual(image_processor.do_reduce_labels  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''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=snake_case__     )
									for image in image_inputs:
														self.assertIsInstance(snake_case__  ,			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(snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''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=snake_case__  ,			numpify=snake_case__     )
									for image in image_inputs:
														self.assertIsInstance(snake_case__  ,			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(snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''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=snake_case__  ,			torchify=snake_case__     )
									for image in image_inputs:
														self.assertIsInstance(snake_case__  ,			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(snake_case__  ,			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    _lowerCamelCase       (			self     ):

									'''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=snake_case__  ,			torchify=snake_case__     )
									UpperCamelCase_			= []
									for image in image_inputs:
														self.assertIsInstance(snake_case__  ,			torch.Tensor     )
														maps.append(torch.zeros(image.shape[-2:]     ).long()     )

									# Test not batched input
									UpperCamelCase_			= image_processing(image_inputs[0]  ,			maps[0]  ,			return_tensors="pt"     )
									self.assertEqual(
									    encoding["pixel_values"].shape  ,			(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(
									    encoding["labels"].shape  ,			(
									        1,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(encoding["labels"].dtype  ,			torch.long     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 255     )

									# Test batched
									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertEqual(
									    encoding["pixel_values"].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"],
									    )  ,			)
									self.assertEqual(
									    encoding["labels"].shape  ,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(encoding["labels"].dtype  ,			torch.long     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 255     )

									# Test not batched input (PIL images)
									UpperCamelCase_	,     UpperCamelCase_			= prepare_semantic_single_inputs()

									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertEqual(
									    encoding["pixel_values"].shape  ,			(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(
									    encoding["labels"].shape  ,			(
									        1,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(encoding["labels"].dtype  ,			torch.long     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 255     )

									# Test batched input (PIL images)
									UpperCamelCase_	,     UpperCamelCase_			= prepare_semantic_batch_inputs()

									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertEqual(
									    encoding["pixel_values"].shape  ,			(
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(
									    encoding["labels"].shape  ,			(
									        2,
									        self.image_processor_tester.size["height"],
									        self.image_processor_tester.size["width"],
									    )  ,			)
									self.assertEqual(encoding["labels"].dtype  ,			torch.long     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 255     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.image_processing_class(**self.image_processor_dict     )

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									UpperCamelCase_	,     UpperCamelCase_			= prepare_semantic_single_inputs()
									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 150     )

									UpperCamelCase_			= True
									UpperCamelCase_			= image_processing(snake_case__  ,			snake_case__  ,			return_tensors="pt"     )
									self.assertTrue(encoding["labels"].min().item() >= 0     )
									self.assertTrue(encoding["labels"].max().item() <= 255     )

'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float			)							->	list[int]:
  if hue < 0 or hue > 360:
    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'''			)

  lowercase				:							List[str]									=   value * saturation
  lowercase				:							Optional[Any]									=   hue / 60
  lowercase				:							Dict									=   chroma * (1 - abs(hue_section % 2 - 1			))
  lowercase				:							List[Any]									=   value - chroma

  if hue_section >= 0 and hue_section <= 1:
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							int									=   round(255 * (match_value)			)
  elif hue_section > 1 and hue_section <= 2:
    lowercase				:							Optional[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Any									=   round(255 * (chroma + match_value)			)
    lowercase				:							Any									=   round(255 * (match_value)			)
  elif hue_section > 2 and hue_section <= 3:
    lowercase				:							Dict									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
  elif hue_section > 3 and hue_section <= 4:
    lowercase				:							List[str]									=   round(255 * (match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (chroma + match_value)			)
  elif hue_section > 4 and hue_section <= 5:
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
  else:
    lowercase				:							int									=   round(255 * (chroma + match_value)			)
    lowercase				:							Optional[Any]									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)

  return [red, green, blue]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[float]:
  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''			)

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

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

  lowercase				:							str									=   red / 255
  lowercase				:							Union[str, Any]									=   green / 255
  lowercase				:							Union[str, Any]									=   blue / 255
  lowercase				:							str									=   max(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   value - min(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							Optional[int]									=   0 if value == 0 else chroma / value

  if chroma == 0:
    lowercase				:							Dict									=   0.0
  elif value == float_red:
    lowercase				:							Tuple									=   60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    lowercase				:							Optional[int]									=   60 * (2 + (float_blue - float_red) / chroma)
  else:
    lowercase				:							Dict									=   60 * (4 + (float_red - float_green) / chroma)

  lowercase				:							List[str]									=   (hue + 360) % 360

  return [hue, saturation, value]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list[float] ,   __UpperCAmelCase  :					list[float]			)							->	bool:
  lowercase				:							int									=   abs(hsv_a[0] - hsv_a[0]			) < 0.2
  lowercase				:							Optional[int]									=   abs(hsv_a[1] - hsv_a[1]			) < 0.002
  lowercase				:							Dict									=   abs(hsv_a[2] - hsv_a[2]			) < 0.002

  return check_hue and check_saturation and check_value