
'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						torch.FloatTensor

class 		__lowercase			(					_lowercase ,       _lowercase      ):

			@register_to_config
			def __init__(self     ,				A = None     ,				A = 3     ,				A = 3     ,				A = False     ,				A = "positional"     ,				A = 0     ,				A = True     ,				A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")     ,				A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")     ,				A = (2_2_4, 4_4_8, 6_7_2, 8_9_6)     ,				A = 2     ,				A = 1     ,				A = 1     ,				A = "conv"     ,				A = "conv"     ,				A = "silu"     ,				A = 8     ,				A = 3_2     ,				A = 1E-5     ,				A = "default"     ,				A = True     ,				A = None     ,				A = None     ,				):
								super().__init__()

								lowerCamelCase_   : Optional[int]       =						sample_size
								lowerCamelCase_   : int       =						block_out_channels[0] * 4

								# Check inputs
								if len(A ) != len(A ):
													raise ValueError(
													    F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.""" )

								if len(A ) != len(A ):
													raise ValueError(
													    F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.""" )

								# input
								lowerCamelCase_   : Optional[int]       =						nn.Convad(A     ,				block_out_channels[0]     ,				kernel_size=3     ,				padding=(1, 1) )

								# time
								if time_embedding_type == "fourier":
													lowerCamelCase_   : Optional[Any]       =						GaussianFourierProjection(embedding_size=block_out_channels[0]     ,				scale=1_6 )
													lowerCamelCase_   : List[str]       =						2 * block_out_channels[0]
								elif time_embedding_type == "positional":
													lowerCamelCase_   : Tuple       =						Timesteps(block_out_channels[0]     ,				A     ,				A )
													lowerCamelCase_   : List[Any]       =						block_out_channels[0]

								lowerCamelCase_   : Union[str, Any]       =						TimestepEmbedding(A     ,				A )

								# class embedding
								if class_embed_type is None and num_class_embeds is not None:
													lowerCamelCase_   : Any       =						nn.Embedding(A     ,				A )
								elif class_embed_type == "timestep":
													lowerCamelCase_   : Union[str, Any]       =						TimestepEmbedding(A     ,				A )
								elif class_embed_type == "identity":
													lowerCamelCase_   : List[Any]       =						nn.Identity(A     ,				A )
								else:
													lowerCamelCase_   : List[str]       =						None

								lowerCamelCase_   : Tuple       =						nn.ModuleList([] )
								lowerCamelCase_   : Union[str, Any]       =						None
								lowerCamelCase_   : Any       =						nn.ModuleList([] )

								# down
								lowerCamelCase_   : int       =						block_out_channels[0]
								for i, down_block_type in enumerate(A ):
													lowerCamelCase_   : Optional[Any]       =						output_channel
													lowerCamelCase_   : Optional[Any]       =						block_out_channels[i]
													lowerCamelCase_   : str       =						i == len(A ) - 1

													lowerCamelCase_   : List[Any]       =						get_down_block(
													    A     ,				num_layers=A     ,				in_channels=A     ,				out_channels=A     ,				temb_channels=A     ,				add_downsample=not is_final_block     ,				resnet_eps=A     ,				resnet_act_fn=A     ,				resnet_groups=A     ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,				downsample_padding=A     ,				resnet_time_scale_shift=A     ,				downsample_type=A     ,				)
													self.down_blocks.append(A )

								# mid
								lowerCamelCase_   : Any       =						UNetMidBlockaD(
								    in_channels=block_out_channels[-1]     ,				temb_channels=A     ,				resnet_eps=A     ,				resnet_act_fn=A     ,				output_scale_factor=A     ,				resnet_time_scale_shift=A     ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]     ,				resnet_groups=A     ,				add_attention=A     ,				)

								# up
								lowerCamelCase_   : List[str]       =						list(reversed(A ) )
								lowerCamelCase_   : Optional[int]       =						reversed_block_out_channels[0]
								for i, up_block_type in enumerate(A ):
													lowerCamelCase_   : int       =						output_channel
													lowerCamelCase_   : List[Any]       =						reversed_block_out_channels[i]
													lowerCamelCase_   : Optional[Any]       =						reversed_block_out_channels[min(i + 1     ,				len(A ) - 1 )]

													lowerCamelCase_   : int       =						i == len(A ) - 1

													lowerCamelCase_   : Any       =						get_up_block(
													    A     ,				num_layers=layers_per_block + 1     ,				in_channels=A     ,				out_channels=A     ,				prev_output_channel=A     ,				temb_channels=A     ,				add_upsample=not is_final_block     ,				resnet_eps=A     ,				resnet_act_fn=A     ,				resnet_groups=A     ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,				resnet_time_scale_shift=A     ,				upsample_type=A     ,				)
													self.up_blocks.append(A )
													lowerCamelCase_   : Any       =						output_channel

								# out
								lowerCamelCase_   : Union[str, Any]       =						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,				3_2 )
								lowerCamelCase_   : List[Any]       =						nn.GroupNorm(num_channels=block_out_channels[0]     ,				num_groups=A     ,				eps=A )
								lowerCamelCase_   : Dict       =						nn.SiLU()
								lowerCamelCase_   : Dict       =						nn.Convad(block_out_channels[0]     ,				A     ,				kernel_size=3     ,				padding=1 )






			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A = None     ,				A = True     ,				):
								# 0. center input if necessary
								if self.config.center_input_sample:
													lowerCamelCase_   : Dict       =						2 * sample - 1.0

								# 1. time
								lowerCamelCase_   : Dict       =						timestep
								if not torch.is_tensor(A ):
													lowerCamelCase_   : Any       =						torch.tensor([timesteps]     ,				dtype=torch.long     ,				device=sample.device )
								elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
													lowerCamelCase_   : Tuple       =						timesteps[None].to(sample.device )

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								lowerCamelCase_   : List[Any]       =						timesteps * torch.ones(sample.shape[0]     ,				dtype=timesteps.dtype     ,				device=timesteps.device )

								lowerCamelCase_   : List[Any]       =						self.time_proj(A )

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might actually be running in fp16. so we need to cast here.
								# there might be better ways to encapsulate this.
								lowerCamelCase_   : Tuple       =						t_emb.to(dtype=self.dtype )
								lowerCamelCase_   : Union[str, Any]       =						self.time_embedding(A )

								if self.class_embedding is not None:
													if class_labels is None:
																		raise ValueError('''class_labels should be provided when doing class conditioning''' )

													if self.config.class_embed_type == "timestep":
																		lowerCamelCase_   : Optional[Any]       =						self.time_proj(A )

													lowerCamelCase_   : Union[str, Any]       =						self.class_embedding(A ).to(dtype=self.dtype )
													lowerCamelCase_   : Optional[Any]       =						emb + class_emb

								# 2. pre-process
								lowerCamelCase_   : Tuple       =						sample
								lowerCamelCase_   : int       =						self.conv_in(A )

								# 3. down
								lowerCamelCase_   : Optional[int]       =						(sample,)
								for downsample_block in self.down_blocks:
													if hasattr(A     ,				'''skip_conv''' ):
																		lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : List[str]       =						downsample_block(
																		    hidden_states=A     ,				temb=A     ,				skip_sample=A )
													else:
																		lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						downsample_block(hidden_states=A     ,				temb=A )

													down_block_res_samples += res_samples

								# 4. mid
								lowerCamelCase_   : Dict       =						self.mid_block(A     ,				A )

								# 5. up
								lowerCamelCase_   : int       =						None
								for upsample_block in self.up_blocks:
													lowerCamelCase_   : Tuple       =						down_block_res_samples[-len(upsample_block.resnets ) :]
													lowerCamelCase_   : Optional[int]       =						down_block_res_samples[: -len(upsample_block.resnets )]

													if hasattr(A     ,				'''skip_conv''' ):
																		lowerCamelCase_, lowerCamelCase_   : List[Any]       =						upsample_block(A     ,				A     ,				A     ,				A )
													else:
																		lowerCamelCase_   : List[str]       =						upsample_block(A     ,				A     ,				A )

        # 6. post-process
								lowerCamelCase_   : Optional[int]       =						self.conv_norm_out(A )
								lowerCamelCase_   : str       =						self.conv_act(A )
								lowerCamelCase_   : str       =						self.conv_out(A )

								if skip_sample is not None:
													sample += skip_sample

								if self.config.time_embedding_type == "fourier":
													lowerCamelCase_   : Any       =						timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:] ))) )
													lowerCamelCase_   : Any       =						sample / timesteps

								if not return_dict:
													return (sample,)

								return UNetaDOutput(sample=A )




'''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()


