



from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def 		UpperCamelCase     (       )     ->					Dict:
   UpperCamelCase   :  int						     =     9, 14  # noqa: F841
   UpperCamelCase   :  Tuple						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     defaultdict(snake_case__     )
   for nodea, nodea, cost in edges:
      adjancency[nodea].append([nodea, cost]     )
      adjancency[nodea].append([nodea, cost]     )

   UpperCamelCase   :  int						     =     mst(snake_case__     )

   UpperCamelCase   :  Dict						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   for answer in expected:
      UpperCamelCase   :  Optional[Any]						     =     tuple(answer[:2]     )
      UpperCamelCase   :  Any						     =     tuple(edge[::-1]     )
      assert edge in result or reverse in result








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 ViTImageProcessor



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase   :  List[Any]						     =     parent
          UpperCamelCase   :  List[Any]						     =     batch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  int						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  int						     =     max_resolution
          UpperCamelCase   :  Any						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std






       def  snake_case_	(      self	)					->		List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }






@require_torch
@require_vision
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  ViTImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Dict						     =     EfficientFormerImageProcessorTester(self	)
       @property
       def  snake_case_	(      self	)					->		List[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  str						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Optional[Any]						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )
       def  snake_case_	(      self	)					->		str:
          # Initialize image_processor
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Union[str, Any]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )






       def  snake_case_	(      self	)					->		Tuple:
          # Initialize image_processor
          UpperCamelCase   :  Any						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  int						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Optional[int]						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  int						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )



