

"""simple docstring"""




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 BeitImageProcessor







class      __lowercase			(    unittest.TestCase					):

  '''simple docstring'''





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






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					size if size is not None else {"""height""": 20, """width""": 20}
   UpperCAmelCase__					:  List[Any]					   =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   UpperCAmelCase__					:  Union[str, Any]					   =					parent
   UpperCAmelCase__					:  Optional[int]					   =					batch_size
   UpperCAmelCase__					:  Optional[Any]					   =					num_channels
   UpperCAmelCase__					:  List[str]					   =					image_size
   UpperCAmelCase__					:  Optional[Any]					   =					min_resolution
   UpperCAmelCase__					:  Tuple					   =					max_resolution
   UpperCAmelCase__					:  str					   =					do_resize
   UpperCAmelCase__					:  Optional[Any]					   =					size
   UpperCAmelCase__					:  Dict					   =					do_center_crop
   UpperCAmelCase__					:  str					   =					crop_size
   UpperCAmelCase__					:  Union[str, Any]					   =					do_normalize
   UpperCAmelCase__					:  Any					   =					image_mean
   UpperCAmelCase__					:  Optional[Any]					   =					image_std
   UpperCAmelCase__					:  Tuple					   =					do_reduce_labels




  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_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						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					load_dataset("""hf-internal-testing/fixtures_ade20k"""							,    split="""test"""      )

 UpperCAmelCase__					:  Dict					   =					Image.open(dataset[0]["""file"""]      )
 UpperCAmelCase__					:  int					   =					Image.open(dataset[1]["""file"""]      )

 return image, map
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					load_dataset("""hf-internal-testing/fixtures_ade20k"""							,    split="""test"""      )

 UpperCAmelCase__					:  str					   =					Image.open(ds[0]["""file"""]      )
 UpperCAmelCase__					:  Tuple					   =					Image.open(ds[1]["""file"""]      )
 UpperCAmelCase__					:  List[Any]					   =					Image.open(ds[2]["""file"""]      )
 UpperCAmelCase__					:  Tuple					   =					Image.open(ds[3]["""file"""]      )

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







@require_torch
@require_vision
class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):

  '''simple docstring'''





  snake_case_				       =       BeitImageProcessor if is_vision_available() else None

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					BeitImageProcessingTester(self    )

  @property
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   return self.image_processor_tester.prepare_image_processor_dict()

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					self.image_processing_class(**self.image_processor_dict    )
   self.assertTrue(hasattr(A					,"""do_resize"""    )    )
   self.assertTrue(hasattr(A					,"""size"""    )    )
   self.assertTrue(hasattr(A					,"""do_center_crop"""    )    )
   self.assertTrue(hasattr(A					,"""center_crop"""    )    )
   self.assertTrue(hasattr(A					,"""do_normalize"""    )    )
   self.assertTrue(hasattr(A					,"""image_mean"""    )    )
   self.assertTrue(hasattr(A					,"""image_std"""    )    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class.from_dict(self.image_processor_dict    )
   self.assertEqual(image_processor.size					,{"""height""": 20, """width""": 20}    )
   self.assertEqual(image_processor.crop_size					,{"""height""": 18, """width""": 18}    )
   self.assertEqual(image_processor.do_reduce_labels					,A    )

   UpperCAmelCase__					:  Tuple					   =					self.image_processing_class.from_dict(
       self.image_processor_dict					,size=42					,crop_size=84					,reduce_labels=A    )
   self.assertEqual(image_processor.size					,{"""height""": 42, """width""": 42}    )
   self.assertEqual(image_processor.crop_size					,{"""height""": 84, """width""": 84}    )
   self.assertEqual(image_processor.do_reduce_labels					,A    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   UpperCAmelCase__					:  Optional[int]					   =					prepare_image_inputs(self.image_processor_tester					,equal_resolution=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,Image.Image    )

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

   # Test batched
   UpperCAmelCase__					:  Any					   =					image_processing(A					,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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   UpperCAmelCase__					:  int					   =					prepare_image_inputs(self.image_processor_tester					,equal_resolution=A					,numpify=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,np.ndarray    )

   # Test not batched input
   UpperCAmelCase__					:  str					   =					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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  str					   =					image_processing(A					,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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   UpperCAmelCase__					:  Tuple					   =					prepare_image_inputs(self.image_processor_tester					,equal_resolution=A					,torchify=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,torch.Tensor    )

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

   # Test batched
   UpperCAmelCase__					:  Any					   =					image_processing(A					,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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   UpperCAmelCase__					:  Dict					   =					prepare_image_inputs(self.image_processor_tester					,equal_resolution=A					,torchify=A    )
   UpperCAmelCase__					:  Any					   =					[]
   for image in image_inputs:
    self.assertIsInstance(A					,torch.Tensor    )
    maps.append(torch.zeros(image.shape[-2:]    ).long()    )

   # Test not batched input
   UpperCAmelCase__					:  List[Any]					   =					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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)
   self.assertEqual(
       encoding["""labels"""].shape					,(
           1,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_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__					:  int					   =					image_processing(A					,A					,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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)
   self.assertEqual(
       encoding["""labels"""].shape					,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_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__					:  int					   =					prepare_semantic_single_inputs()

   UpperCAmelCase__					:  Dict					   =					image_processing(A					,A					,return_tensors="""pt"""    )
   self.assertEqual(
       encoding["""pixel_values"""].shape					,(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)
   self.assertEqual(
       encoding["""labels"""].shape					,(
           1,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_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__					:  int					   =					prepare_semantic_batch_inputs()

   UpperCAmelCase__					:  Optional[Any]					   =					image_processing(A					,A					,return_tensors="""pt"""    )
   self.assertEqual(
       encoding["""pixel_values"""].shape					,(
           2,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)
   self.assertEqual(
       encoding["""labels"""].shape					,(
           2,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       )					,)
   self.assertEqual(encoding["""labels"""].dtype					,torch.long    )
   self.assertTrue(encoding["""labels"""].min().item() >= 0    )
   self.assertTrue(encoding["""labels"""].max().item() <= 255    )




  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					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__					:  str					   =					prepare_semantic_single_inputs()
   UpperCAmelCase__					:  Tuple					   =					image_processing(A					,A					,return_tensors="""pt"""    )
   self.assertTrue(encoding["""labels"""].min().item() >= 0    )
   self.assertTrue(encoding["""labels"""].max().item() <= 150    )

   UpperCAmelCase__					:  Tuple					   =					True
   UpperCAmelCase__					:  Dict					   =					image_processing(A					,A					,return_tensors="""pt"""    )
   self.assertTrue(encoding["""labels"""].min().item() >= 0    )
   self.assertTrue(encoding["""labels"""].max().item() <= 255    )








"""simple docstring"""




def   lowerCAmelCase						(					__UpperCamelCase = 1000      ):
 '''simple docstring'''





 return sum(2 * a * ((a - 1) // 2) for a in range(3							,    n + 1      )      )


if __name__ == "__main__":
      print(solution())





