




'''simple docstring'''


UpperCamelCase     =				[
    ['''ab-e1''', '''ac-e3''', '''ad-e5''', '''bc-e4''', '''bd-e2''', '''be-e6''', '''bh-e12''', '''cd-e2''', '''ce-e4''',
     '''de-e1''', '''df-e8''', '''dg-e5''', '''dh-e10''', '''ef-e3''', '''eg-e2''', '''fg-e6''', '''gh-e6''', '''hi-e3'''],
    ['''ab-e1''', '''ac-e3''', '''ad-e5''', '''bc-e4''', '''bd-e2''', '''be-e6''', '''cd-e2''', '''de-e1''', '''df-e8''',
     '''ef-e3''', '''eg-e2''', '''fg-e6'''],
    ['''ab-e1''', '''ac-e3''', '''bc-e4''', '''bd-e2''', '''de-e1''', '''df-e8''', '''dg-e5''', '''ef-e3''', '''eg-e2''',
     '''eh-e12''', '''fg-e6''', '''fh-e10''', '''gh-e6'''],
    ['''ab-e1''', '''ac-e3''', '''bc-e4''', '''bd-e2''', '''bh-e12''', '''cd-e2''', '''df-e8''', '''dh-e10'''],
    ['''ab-e1''', '''ac-e3''', '''ad-e5''', '''bc-e4''', '''bd-e2''', '''cd-e2''', '''ce-e4''', '''de-e1''', '''df-e8''',
     '''dg-e5''', '''ef-e3''', '''eg-e2''', '''fg-e6''']
]
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   List[str]:
     A:		str        =		set()
     for row in edge_array:
          for item in row:
               distinct_edge.add(item[0]  )
     return list(UpperCamelCase__  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   List[str]:
     A:		Optional[Any]        =		['''0'''] * len(UpperCamelCase__  )
     for i, row in enumerate(UpperCamelCase__  ):
          for item in row:
               if distinct_edge in item[0]:
                    A:		Any        =		'''1'''
                    break
     return "".join(UpperCamelCase__  )
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   List[str]:
     A:		Optional[Any]        =		get_distinct_edge(UpperCamelCase__  )
     A:		Dict        =		{}

     for item in distinct_edge:
          A:		Any        =		get_bitcode(UpperCamelCase__		,							UpperCamelCase__  )
          # print('bit',bit)
          # bt=''.join(bit)
          A:		int        =		bit.count('''1'''  )
          A:		Any        =		[s, bit]
     # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
     A:		Tuple        =		[
         [k, v[0], v[1]]
         for k, v in sorted(frequency_table.items()		,							key=lambda __lowercase  : v[1][0]		,							reverse=UpperCamelCase__  )
     ]
     return sorted_frequency_table
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Tuple:
     A:		Dict        =		{}
     for _, item in enumerate(UpperCamelCase__  ):
          nodes.setdefault(item[2]		,							[]  ).append(item[0]  )
     return nodes
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Union[str, Any]:
     A:		str        =		{}
     for key, value in nodes.items():
          A:		Optional[int]        =		value
     return cluster
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   List[Any]:
     return [i * 1_0_0 / len(UpperCamelCase__  ) for i in cluster]
def 				SCREAMING_SNAKE_CASE(      ) ->   Any:
     print('''\nNodes\n'''  )
     for key, value in nodes.items():
          print(UpperCamelCase__		,							UpperCamelCase__  )
     print('''\nSupport\n'''  )
     print(UpperCamelCase__  )
     print('''\n Cluster \n'''  )
     for key, value in sorted(cluster.items()		,							reverse=UpperCamelCase__  ):
          print(UpperCamelCase__		,							UpperCamelCase__  )
     print('''\n Graph\n'''  )
     for key, value in graph.items():
          print(UpperCamelCase__		,							UpperCamelCase__  )
     print('''\n Edge List of Frequent subgraphs \n'''  )
     for edge_list in freq_subgraph_edge_list:
          print(UpperCamelCase__  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase  ) ->   int:






     for i in cluster[ca]:
          A:		Union[str, Any]        =		0
          A:		List[str]        =		ca + 1
          while ca < max(cluster.keys()  ):
               for j in cluster[ca]:
                    if int(UpperCamelCase__		,							2  ) & int(UpperCamelCase__		,							2  ) == int(UpperCamelCase__		,							2  ):
                         if tuple(nodes[i]  ) in graph:
                              graph[tuple(nodes[i]  )].append(nodes[j]  )
                         else:
                              A:		List[Any]        =		[nodes[j]]
                         count += 1
               if count == 0:
                    A:		List[str]        =		ca + 1
               else:
                    break
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   int:
     A:		Any        =		cluster[max(cluster.keys()  )]
     A:		int        =		'''Header'''
     A:		List[str]        =		{}
     for i in x:
          if (["Header"],) in graph:
               graph[(["Header"],)].append(x[i]  )
          else:
               A:		List[Any]        =		[x[i]]
     for i in x:
          A:		List[Any]        =		[['''Header''']]
     A:		List[str]        =		1
     while i < max(UpperCamelCase__  ) - 1:
          create_edge(UpperCamelCase__		,							UpperCamelCase__		,							UpperCamelCase__		,							UpperCamelCase__  )
          A:		Union[str, Any]        =		i + 1
     return graph
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase=None  ) ->   Any:
     A:		Dict        =		(path or []) + [start]
     if start == end:
          paths.append(UpperCamelCase__  )
     for node in graph[start]:
          if tuple(UpperCamelCase__  ) not in path:
               my_dfs(UpperCamelCase__		,							tuple(UpperCamelCase__  )		,							UpperCamelCase__		,							UpperCamelCase__  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase  ) ->   int:
     A:		Optional[int]        =		int(s / 1_0_0 * (len(UpperCamelCase__  ) - 1)  )
     for i in cluster[k]:
          my_dfs(UpperCamelCase__		,							tuple(cluster[k][i]  )		,							(['''Header'''],)  )
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Optional[int]:
     A:		Tuple        =		[]
     for edges in paths:
          A:		Optional[int]        =		[]
          for j in range(len(UpperCamelCase__  ) - 1  ):
               A:		Any        =		list(edges[j]  )
               for e in temp:
                    A:		Tuple        =		(e[0], e[1])
                    el.append(UpperCamelCase__  )
          freq_sub_el.append(UpperCamelCase__  )
     return freq_sub_el
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   List[Any]:






     for i in range(len(UpperCamelCase__  )  ):
          for j in range(len(edge_array[i]  )  ):
               A:		str        =		edge_array[i][j].split('''-'''  )
               A:		Optional[int]        =		t


if __name__ == "__main__":
     preprocess(edge_array)
     UpperCamelCase     =				get_frequency_table(edge_array)
     UpperCamelCase     =				get_nodes(frequency_table)
     UpperCamelCase     =				get_cluster(nodes)
     UpperCamelCase     =				get_support(cluster)
     UpperCamelCase     =				construct_graph(cluster, nodes)
     find_freq_subgraph_given_support(60, cluster, graph)
     UpperCamelCase     =				[]
     UpperCamelCase     =				freq_subgraphs_edge_list(paths)
     print_all()












'''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 	lowerCAmelCase_  (					unittest.TestCase       ):







  '''simple docstring'''

  def __init__(   self							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Any=7				,     SCREAMING_SNAKE_CASE_							:       Dict=3				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=30				,     SCREAMING_SNAKE_CASE_							:       List[Any]=4_00				,     SCREAMING_SNAKE_CASE_							:       str=True				,     SCREAMING_SNAKE_CASE_							:       List[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=True				,     SCREAMING_SNAKE_CASE_							:       List[str]=[0.5, 0.5, 0.5]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=[0.5, 0.5, 0.5]				,     SCREAMING_SNAKE_CASE_							:       Tuple=False				,     ) ->		str:







       '''simple docstring'''



       A:		Dict        =		size if size is not None else {'''height''': 30, '''width''': 30}
       A:		List[Any]        =		parent
       A:		Tuple        =		batch_size
       A:		List[str]        =		num_channels
       A:		Dict        =		min_resolution
       A:		str        =		max_resolution
       A:		int        =		do_resize
       A:		List[str]        =		size
       A:		int        =		do_normalize
       A:		Optional[int]        =		image_mean
       A:		Optional[int]        =		image_std
       A:		Any        =		do_reduce_labels




  def 				_snake_case    (   self							:       Any  ) ->		List[str]:







       '''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 				SCREAMING_SNAKE_CASE(      ) ->   List[Any]:
     A:		Dict        =		load_dataset('''hf-internal-testing/fixtures_ade20k'''		,							split='''test'''  )

     A:		Any        =		Image.open(dataset[0]['''file''']  )
     A:		Dict        =		Image.open(dataset[1]['''file''']  )

     return image, map
def 				SCREAMING_SNAKE_CASE(      ) ->   List[Any]:
     A:		Optional[Any]        =		load_dataset('''hf-internal-testing/fixtures_ade20k'''		,							split='''test'''  )

     A:		Union[str, Any]        =		Image.open(dataset[0]['''file''']  )
     A:		str        =		Image.open(dataset[1]['''file''']  )
     A:		str        =		Image.open(dataset[2]['''file''']  )
     A:		Any        =		Image.open(dataset[3]['''file''']  )

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






@require_torch
@require_vision
class 	lowerCAmelCase_  (					UpperCAmelCase_					,				unittest.TestCase       ):







  '''simple docstring'''

  UpperCamelCase_ :			int        =							SegformerImageProcessor if is_vision_available() else None



  def 				_snake_case    (   self							:       List[str]  ) ->		str:







       '''simple docstring'''



       A:		Dict        =		SegformerImageProcessingTester(self  )



  @property
  def 				_snake_case    (   self							:       Tuple  ) ->		List[str]:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()



  def 				_snake_case    (   self							:       Optional[int]  ) ->		Optional[Any]:







       '''simple docstring'''



       A:		Optional[int]        =		self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_				,     '''do_resize'''  )  )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_				,     '''size'''  )  )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_				,     '''do_normalize'''  )  )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_				,     '''image_mean'''  )  )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_				,     '''image_std'''  )  )
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_				,     '''do_reduce_labels'''  )  )



  def 				_snake_case    (   self							:       List[str]  ) ->		Union[str, Any]:







       '''simple docstring'''



       A:		Tuple        =		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				,     SCREAMING_SNAKE_CASE_  )

       A:		str        =		self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     reduce_labels=SCREAMING_SNAKE_CASE_  )
       self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}  )
       self.assertEqual(image_processor.do_reduce_labels				,     SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       Union[str, Any]  ) ->		Any:







       '''simple docstring'''



       pass



  def 				_snake_case    (   self							:       Any  ) ->		Dict:







       '''simple docstring'''



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

       # Test not batched input
       A:		List[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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,     )

       # Test batched
       A:		int        =		image_processing(SCREAMING_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 				_snake_case    (   self							:       str  ) ->		Tuple:







       '''simple docstring'''



       A:		int        =		self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       A:		Optional[Any]        =		prepare_image_inputs(self.image_processor_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
       A:		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
       A:		Any        =		image_processing(SCREAMING_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 				_snake_case    (   self							:       str  ) ->		str:







       '''simple docstring'''



       A:		List[Any]        =		self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       A:		str        =		prepare_image_inputs(self.image_processor_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
       A:		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
       A:		List[Any]        =		image_processing(SCREAMING_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 				_snake_case    (   self							:       Dict  ) ->		Optional[Any]:







       '''simple docstring'''



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

       # Test not batched input
       A:		Tuple        =		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
       A:		Union[str, Any]        =		image_processing(SCREAMING_SNAKE_CASE_				,     SCREAMING_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)
       A    ,						A:		int        =		prepare_semantic_single_inputs()

       A:		Dict        =		image_processing(SCREAMING_SNAKE_CASE_				,     SCREAMING_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)
       A    ,						A:		int        =		prepare_semantic_batch_inputs()

       A:		int        =		image_processing(SCREAMING_SNAKE_CASE_				,     SCREAMING_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 				_snake_case    (   self							:       Tuple  ) ->		Union[str, Any]:







       '''simple docstring'''



       A:		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
       A    ,						A:		Union[str, Any]        =		prepare_semantic_single_inputs()
       A:		Optional[int]        =		image_processing(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     return_tensors='''pt'''  )
       self.assertTrue(encoding['''labels'''].min().item() >= 0  )
       self.assertTrue(encoding['''labels'''].max().item() <= 1_50  )

       A:		Optional[int]        =		True
       A:		str        =		image_processing(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     return_tensors='''pt'''  )
       self.assertTrue(encoding['''labels'''].min().item() >= 0  )
       self.assertTrue(encoding['''labels'''].max().item() <= 2_55  )






