
"""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   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=30    ,     snake_case_=4_00    ,     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    ,     ):
      _snake_case  :    List[str]            = size if size is not None else {"height": 30, "width": 30}
      _snake_case  :    Optional[int]            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    Any            = num_channels
      _snake_case  :    Optional[Any]            = min_resolution
      _snake_case  :    int            = max_resolution
      _snake_case  :    int            = do_resize
      _snake_case  :    List[Any]            = size
      _snake_case  :    int            = do_normalize
      _snake_case  :    Any            = image_mean
      _snake_case  :    str            = image_std
      _snake_case  :    List[Any]            = do_reduce_labels


    def        lowerCamelCase__						(     self  ):
      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__ ( ):

  """simple docstring"""







  _snake_case  :    List[str]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Any            = Image.open(dataset[0]["file"]		)
  _snake_case  :    Optional[int]            = Image.open(dataset[1]["file"]		)

  return image, map
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    Optional[int]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Optional[int]            = Image.open(dataset[0]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[1]["file"]		)
  _snake_case  :    Dict            = Image.open(dataset[2]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[3]["file"]		)

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






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			Dict	=       SegformerImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = SegformerImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = 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  ):
      _snake_case  :    List[str]            = 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_  )

      _snake_case  :    int            = 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  ):
      pass
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    List[str]            = 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
      _snake_case  :    Any            = 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
      _snake_case  :    List[Any]            = 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  ):
      # Initialize image_processing
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Any            = 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
      _snake_case  :    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
      _snake_case  :    Optional[int]            = 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  ):
      # Initialize image_processing
      _snake_case  :    Optional[int]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[Any]            = 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
      _snake_case  :    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
      _snake_case  :    List[Any]            = 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  ):
      # Initialize image_processing
      _snake_case  :    int            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    List[str]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      _snake_case  :    Optional[int]            = []
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )
        maps.append(torch.zeros(image.shape[-2:]  ).long()  )

      # Test not batched input
      _snake_case  :    Optional[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.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() <= 2_55  )

      # Test batched
      _snake_case  :    List[Any]            = 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() <= 2_55  )

      # Test not batched input (PIL images)
      _snake_case      ,       _snake_case  :    int            = prepare_semantic_single_inputs()

      _snake_case  :    List[str]            = 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() <= 2_55  )

      # Test batched input (PIL images)
      _snake_case      ,       _snake_case  :    str            = prepare_semantic_batch_inputs()

      _snake_case  :    List[Any]            = 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() <= 2_55  )


    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = 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
      _snake_case      ,       _snake_case  :    List[Any]            = prepare_semantic_single_inputs()
      _snake_case  :    Dict            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 1_50  )

      _snake_case  :    Tuple            = True
      _snake_case  :    Union[str, Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )






_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
