
'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor

class 		__lowercase			(					unittest.TestCase      ):

			def __init__(self     ,				A     ,				A=3     ,				A=3_2     ,				A=3     ,				A=1_0     ,				A=[1_0, 2_0, 3_0, 4_0]     ,				A=[1, 1, 2, 1]     ,				A=True     ,				A=True     ,				A="relu"     ,				A=3     ,				A=None     ,				):
								lowerCamelCase_   : Union[str, Any]       =						parent
								lowerCamelCase_   : List[Any]       =						batch_size
								lowerCamelCase_   : Optional[Any]       =						image_size
								lowerCamelCase_   : Optional[int]       =						num_channels
								lowerCamelCase_   : Tuple       =						embeddings_size
								lowerCamelCase_   : Optional[Any]       =						hidden_sizes
								lowerCamelCase_   : Any       =						depths
								lowerCamelCase_   : List[str]       =						is_training
								lowerCamelCase_   : Optional[int]       =						use_labels
								lowerCamelCase_   : Optional[int]       =						hidden_act
								lowerCamelCase_   : int       =						num_labels
								lowerCamelCase_   : Optional[Any]       =						scope
								lowerCamelCase_   : List[str]       =						len(A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								lowerCamelCase_   : Any       =						self.get_config()

								return config, pixel_values

			def     UpperCAmelCase__	(self ):
								return ResNetConfig(
								    num_channels=self.num_channels     ,				embeddings_size=self.embeddings_size     ,				hidden_sizes=self.hidden_sizes     ,				depths=self.depths     ,				hidden_act=self.hidden_act     ,				num_labels=self.num_labels     ,				image_size=self.image_size     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A ):
								lowerCamelCase_   : Tuple       =						FlaxResNetModel(config=A )
								lowerCamelCase_   : Optional[int]       =						model(A )

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A ):
								lowerCamelCase_   : Any       =						self.num_labels
								lowerCamelCase_   : List[Any]       =						FlaxResNetForImageClassification(config=A )
								lowerCamelCase_   : Union[str, Any]       =						model(A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.num_labels) )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[Any]       =						self.prepare_config_and_inputs()
								lowerCamelCase_, lowerCamelCase_   : Dict       =						config_and_inputs
								lowerCamelCase_   : Optional[int]       =						{'''pixel_values''': pixel_values}
								return config, inputs_dict

@require_flax
class 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						Tuple		 =     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

			lowerCamelCase     :						Optional[Any]		 =     False
			lowerCamelCase     :						List[str]		 =     False
			lowerCamelCase     :						Union[str, Any]		 =     False

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						FlaxResNetModelTester(self )
								lowerCamelCase_   : Union[str, Any]       =						ConfigTester(self     ,				config_class=A     ,				has_text_modality=A )

			def     UpperCAmelCase__	(self ):
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()

			def     UpperCAmelCase__	(self ):
								return

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A )

			@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
			def     UpperCAmelCase__	(self ):
								pass

			@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : List[Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_   : str       =						model_class(A )
													lowerCamelCase_   : Any       =						inspect.signature(model.__call__ )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase_   : int       =						[*signature.parameters.keys()]

													lowerCamelCase_   : Optional[Any]       =						['''pixel_values''']
													self.assertListEqual(arg_names[:1]     ,				A )

			def     UpperCAmelCase__	(self ):
								def check_hidden_states_output(A     ,				A     ,				A ):
													lowerCamelCase_   : List[str]       =						model_class(A )

													lowerCamelCase_   : str       =						model(**self._prepare_for_class(A     ,				A ) )

													lowerCamelCase_   : Tuple       =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowerCamelCase_   : Any       =						self.model_tester.num_stages
													self.assertEqual(len(A )     ,				expected_num_stages + 1 )

			@unittest.skip(reason='''ResNet does not use feedforward chunking''' )
			def     UpperCAmelCase__	(self ):
								pass






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : int       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__ ):
																		lowerCamelCase_   : Tuple       =						self._prepare_for_class(A     ,				A )
																		lowerCamelCase_   : Dict       =						model_class(A )

																		@jax.jit
																		def model_jitted(A     ,				**A ):
																							return model(pixel_values=A     ,				**A )

																		with self.subTest('''JIT Enabled''' ):
																							lowerCamelCase_   : Any       =						model_jitted(**A ).to_tuple()

																		with self.subTest('''JIT Disabled''' ):
																							with jax.disable_jit():
																												lowerCamelCase_   : List[Any]       =						model_jitted(**A ).to_tuple()

																		self.assertEqual(len(A )     ,				len(A ) )
																		for jitted_output, output in zip(A     ,				A ):
																							self.assertEqual(jitted_output.shape     ,				output.shape )




def lowercase_		(				)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					return image






@require_flax
class 		__lowercase			(					unittest.TestCase      ):

			@cached_property
			def     UpperCAmelCase__	(self ):
								return AutoImageProcessor.from_pretrained('''microsoft/resnet-50''' ) if is_vision_available() else None






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50''' )

								lowerCamelCase_   : List[Any]       =						self.default_image_processor
								lowerCamelCase_   : List[Any]       =						prepare_img()
								lowerCamelCase_   : List[str]       =						image_processor(images=A     ,				return_tensors='''np''' )

								lowerCamelCase_   : Tuple       =						model(**A )

								# verify the logits
								lowerCamelCase_   : int       =						(1, 1_0_0_0)
								self.assertEqual(outputs.logits.shape     ,				A )

								lowerCamelCase_   : int       =						jnp.array([-11.10_69, -9.78_77, -8.37_77] )

								self.assertTrue(jnp.allclose(outputs.logits[0, :3]     ,				A     ,				atol=1E-4 ) )




'''simple docstring'''


__lowercase							:				str					=       [
    ['''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 lowercase_		(				_lowercase			)       ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_   : Union[str, Any]       =						set()
					for row in edge_array:
										for item in row:
															distinct_edge.add(item[0]			)
					return list(_lowercase			)




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : List[Any]       =						['''0'''] * len(_lowercase			)
					for i, row in enumerate(_lowercase			):
										for item in row:
															if distinct_edge in item[0]:
																				lowerCamelCase_   : Union[str, Any]       =						'''1'''
																				break
					return "".join(_lowercase			)




def lowercase_		(				_lowercase			)       ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_   : int       =						get_distinct_edge(_lowercase			)
					lowerCamelCase_   : Any       =						{}

					for item in distinct_edge:
										lowerCamelCase_   : Dict       =						get_bitcode(_lowercase     ,    _lowercase			)
										# print('bit',bit)
										# bt=''.join(bit)
										lowerCamelCase_   : Dict       =						bit.count('''1'''			)
										lowerCamelCase_   : Tuple       =						[s, bit]
					# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
					lowerCamelCase_   : int       =						[
					    [k, v[0], v[1]]
					    for k, v in sorted(frequency_table.items()     ,    key=lambda _lowercase			: v[1][0]     ,    reverse=_lowercase			)
					]
					return sorted_frequency_table




def lowercase_		(				_lowercase			)       ->							Any:
					'''simple docstring'''

					lowerCamelCase_   : Dict       =						{}
					for _, item in enumerate(_lowercase			):
										nodes.setdefault(item[2]     ,    []			).append(item[0]			)
					return nodes




def lowercase_		(				_lowercase			)       ->							str:
					'''simple docstring'''

					lowerCamelCase_   : List[Any]       =						{}
					for key, value in nodes.items():
										lowerCamelCase_   : Optional[int]       =						value
					return cluster




def lowercase_		(				_lowercase			)       ->							Optional[int]:
					'''simple docstring'''

					return [i * 100 / len(_lowercase			) for i in cluster]




def lowercase_		(				)       ->							None:
					'''simple docstring'''

					print('''\nNodes\n'''			)
					for key, value in nodes.items():
										print(_lowercase     ,    _lowercase			)
					print('''\nSupport\n'''			)
					print(_lowercase			)
					print('''\n Cluster \n'''			)
					for key, value in sorted(cluster.items()     ,    reverse=_lowercase			):
										print(_lowercase     ,    _lowercase			)
					print('''\n Graph\n'''			)
					for key, value in graph.items():
										print(_lowercase     ,    _lowercase			)
					print('''\n Edge List of Frequent subgraphs \n'''			)
					for edge_list in freq_subgraph_edge_list:
										print(_lowercase			)




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase			)       ->							Tuple:
					'''simple docstring'''

					for i in cluster[ca]:
										lowerCamelCase_   : str       =						0
										lowerCamelCase_   : Optional[Any]       =						ca + 1
										while ca < max(cluster.keys()			):
															for j in cluster[ca]:
																				if int(_lowercase     ,    2			) & int(_lowercase     ,    2			) == int(_lowercase     ,    2			):
																									if tuple(nodes[i]			) in graph:
																														graph[tuple(nodes[i]			)].append(nodes[j]			)
																									else:
																														lowerCamelCase_   : Union[str, Any]       =						[nodes[j]]
																									count += 1
															if count == 0:
																				lowerCamelCase_   : Any       =						ca + 1
															else:
																				break




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							Dict:
					'''simple docstring'''

					lowerCamelCase_   : List[str]       =						cluster[max(cluster.keys()			)]
					lowerCamelCase_   : Tuple       =						'''Header'''
					lowerCamelCase_   : Optional[int]       =						{}
					for i in x:
										if (["Header"],) in graph:
															graph[(["Header"],)].append(x[i]			)
										else:
															lowerCamelCase_   : Any       =						[x[i]]
					for i in x:
										lowerCamelCase_   : Any       =						[['''Header''']]
					lowerCamelCase_   : List[Any]       =						1
					while i < max(_lowercase			) - 1:
										create_edge(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase			)
										lowerCamelCase_   : Tuple       =						i + 1
					return graph




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase=None			)       ->							Dict:
					'''simple docstring'''

					lowerCamelCase_   : List[str]       =						(path or []) + [start]
					if start == end:
										paths.append(_lowercase			)
					for node in graph[start]:
										if tuple(_lowercase			) not in path:
															my_dfs(_lowercase     ,    tuple(_lowercase			)     ,    _lowercase     ,    _lowercase			)




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Dict       =						int(s / 100 * (len(_lowercase			) - 1)			)
					for i in cluster[k]:
										my_dfs(_lowercase     ,    tuple(cluster[k][i]			)     ,    (['''Header'''],)			)




def lowercase_		(				_lowercase			)       ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_   : Tuple       =						[]
					for edges in paths:
										lowerCamelCase_   : Optional[int]       =						[]
										for j in range(len(_lowercase			) - 1			):
															lowerCamelCase_   : List[str]       =						list(edges[j]			)
															for e in temp:
																				lowerCamelCase_   : Dict       =						(e[0], e[1])
																				el.append(_lowercase			)
										freq_sub_el.append(_lowercase			)
					return freq_sub_el




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					for i in range(len(_lowercase			)			):
										for j in range(len(edge_array[i]			)			):
															lowerCamelCase_   : Any       =						edge_array[i][j].split('''-'''			)
															lowerCamelCase_   : Optional[Any]       =						t


if __name__ == "__main__":
					preprocess(edge_array)
					__lowercase							:				List[Any]					=       get_frequency_table(edge_array)
					__lowercase							:				Optional[Any]					=       get_nodes(frequency_table)
					__lowercase							:				int					=       get_cluster(nodes)
					__lowercase							:				Any					=       get_support(cluster)
					__lowercase							:				int					=       construct_graph(cluster, nodes)
					find_freq_subgraph_given_support(60, cluster, graph)
					__lowercase							:				list					=       []
					__lowercase							:				Optional[int]					=       freq_subgraphs_edge_list(paths)
					print_all()


