

'''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 SegformerImageProcessor


class      a__						(							unittest.TestCase						):


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





						"""simple docstring"""





						_lowercase    :   List[str]      =     size if size is not None else {"height": 30, "width": 30}
						_lowercase    :   str      =     parent
						_lowercase    :   Optional[int]      =     batch_size
						_lowercase    :   Optional[int]      =     num_channels
						_lowercase    :   Any      =     min_resolution
						_lowercase    :   Any      =     max_resolution
						_lowercase    :   Tuple      =     do_resize
						_lowercase    :   List[Any]      =     size
						_lowercase    :   Tuple      =     do_normalize
						_lowercase    :   Optional[Any]      =     image_mean
						_lowercase    :   Any      =     image_std
						_lowercase    :   Dict      =     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       _A	(			)    ->			List[Any]:
			_lowercase    :   List[str]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

			_lowercase    :   Union[str, Any]      =     Image.open(dataset[0]["file"]  )
			_lowercase    :   Optional[int]      =     Image.open(dataset[1]["file"]  )

			return image, map







def       _A	(			)    ->			Optional[int]:
			_lowercase    :   List[Any]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

			_lowercase    :   int      =     Image.open(dataset[0]["file"]  )
			_lowercase    :   str      =     Image.open(dataset[1]["file"]  )
			_lowercase    :   Union[str, Any]      =     Image.open(dataset[2]["file"]  )
			_lowercase    :   Union[str, Any]      =     Image.open(dataset[3]["file"]  )

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


@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
			_SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							SegformerImageProcessor if is_vision_available() else None


			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   Union[str, Any]      =     SegformerImageProcessingTester(self						)


			@property
			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						return self.image_processor_tester.prepare_image_processor_dict()


			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
						self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)
						self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
						self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
						self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
						self.assertTrue(hasattr(_UpperCamelCase    ,       "do_reduce_labels"						)						)


			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   Dict      =     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    ,       _UpperCamelCase						)

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


			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						pass


			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





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

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

						# Test batched
						_lowercase    :   int      =     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 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						_lowercase    :   List[str]      =     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
						_lowercase    :   Dict      =     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
						_lowercase    :   List[Any]      =     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 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						_lowercase    :   List[Any]      =     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
						_lowercase    :   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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,       )

						# Test batched
						_lowercase    :   Dict      =     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 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   Union[str, Any]      =     self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						_lowercase    :   Tuple      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
						_lowercase    :   Tuple      =     []
						for image in image_inputs:
									self.assertIsInstance(_UpperCamelCase    ,       torch.Tensor						)
									maps.append(torch.zeros(image.shape[-2:]						).long()						)

						# Test not batched input
						_lowercase    :   List[str]      =     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
						_lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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)
						_lowercase    :   Optional[int]      =     prepare_semantic_single_inputs()

						_lowercase    :   int      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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)
						_lowercase    :   Dict      =     prepare_semantic_batch_inputs()

						_lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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"""





						_lowercase    :   str      =     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
						_lowercase    :   str      =     prepare_semantic_single_inputs()
						_lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
						self.assertTrue(encoding["labels"].min().item() >= 0						)
						self.assertTrue(encoding["labels"].max().item() <= 150						)

						_lowercase    :   str      =     True
						_lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
						self.assertTrue(encoding["labels"].min().item() >= 0						)
						self.assertTrue(encoding["labels"].max().item() <= 255						)









'''simple docstring'''
class      a__						:


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     data
      _lowercase    :   int
      _lowercase    :   Node







def       _A	(			snake_case  )    ->			None:
   _lowercase    :   Any      =     0
   _lowercase    :   str      =     x







def       _A	(			snake_case	,							snake_case  )    ->			None:
   _lowercase  ,   _lowercase    :   Any      =     find_set(snake_case  ), find_set(snake_case  )
   if x == y:
      return

   elif x.rank > y.rank:
      _lowercase    :   Dict      =     x
   else:
      _lowercase    :   Optional[int]      =     y
      if x.rank == y.rank:
         y.rank += 1







def       _A	(			snake_case  )    ->			Node:
   if x != x.parent:
      _lowercase    :   Optional[int]      =     find_set(x.parent  )
   return x.parent







def       _A	(			snake_case  )    ->			set:
   _lowercase    :   Any      =     ({0, 1, 2}, {3, 4, 5})
   for s in sets:
      if node.data in s:
         return s
   _lowercase    :   List[Any]      =     F'''{node.data} is not in {sets}'''
   raise ValueError(snake_case  )







def       _A	(			)    ->			None:
   _lowercase    :   Union[str, Any]      =     [Node(snake_case  ) for i in range(6  )]
   for v in vertex:
      make_set(snake_case  )

   union_set(vertex[0]	,							vertex[1]  )
   union_set(vertex[1]	,							vertex[2]  )
   union_set(vertex[3]	,							vertex[4]  )
   union_set(vertex[3]	,							vertex[5]  )

   for nodea in vertex:
      for nodea in vertex:
         if find_python_set(snake_case  ).isdisjoint(find_python_set(snake_case  )  ):
            assert find_set(snake_case  ) != find_set(snake_case  )
         else:
            assert find_set(snake_case  ) == find_set(snake_case  )


if __name__ == "__main__":
    test_disjoint_set()






