

_a	 =     [
    ["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 _a				( SCREAMING_SNAKE_CASE	:    Optional[int]	)					->					List[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   set()
 for row in edge_array:
  for item in row:
   distinct_edge.add(item[0]	)
 return list(SCREAMING_SNAKE_CASE	)







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   ['0'] * len(SCREAMING_SNAKE_CASE	)
 for i, row in enumerate(SCREAMING_SNAKE_CASE	):
  for item in row:
   if distinct_edge in item[0]:
    __lowerCAmelCase:     Any     			=   '1'
    break
 return "".join(SCREAMING_SNAKE_CASE	)







def _a				( SCREAMING_SNAKE_CASE	:    Tuple	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   get_distinct_edge(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Any     			=   {}

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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Tuple     			=   {}
 for _, item in enumerate(SCREAMING_SNAKE_CASE	):
  nodes.setdefault(item[2]					,      []	).append(item[0]	)
 return nodes







def _a				( SCREAMING_SNAKE_CASE	:    int	)					->					int:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Optional[Any]:

 """simple docstring"""






 return [i * 1_00 / len(SCREAMING_SNAKE_CASE	) for i in cluster]







def _a				( )					->					None:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					Optional[int]:

 """simple docstring"""






 for i in cluster[ca]:
  __lowerCAmelCase:     Dict     			=   0
  __lowerCAmelCase:     Tuple     			=   ca + 1
  while ca < max(cluster.keys()	):
   for j in cluster[ca]:
    if int(SCREAMING_SNAKE_CASE					,      2	) & int(SCREAMING_SNAKE_CASE					,      2	) == int(SCREAMING_SNAKE_CASE					,      2	):
     if tuple(nodes[i]	) in graph:
      graph[tuple(nodes[i]	)].append(nodes[j]	)
     else:
      __lowerCAmelCase:     List[str]     			=   [nodes[j]]
     count += 1
   if count == 0:
    __lowerCAmelCase:     Any     			=   ca + 1
   else:
    break







def _a				( SCREAMING_SNAKE_CASE	:    Union[str, Any]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					str:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    List[Any]=None	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   (path or []) + [start]
 if start == end:
  paths.append(SCREAMING_SNAKE_CASE	)
 for node in graph[start]:
  if tuple(SCREAMING_SNAKE_CASE	) not in path:
   my_dfs(SCREAMING_SNAKE_CASE					,      tuple(SCREAMING_SNAKE_CASE	)					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Dict	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   int(s / 1_00 * (len(SCREAMING_SNAKE_CASE	) - 1)	)
 for i in cluster[k]:
  my_dfs(SCREAMING_SNAKE_CASE					,      tuple(cluster[k][i]	)					,      (['Header'],)	)







def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   []
 for edges in paths:
  __lowerCAmelCase:     Tuple     			=   []
  for j in range(len(SCREAMING_SNAKE_CASE	) - 1	):
   __lowerCAmelCase:     List[Any]     			=   list(edges[j]	)
   for e in temp:
    __lowerCAmelCase:     List[Any]     			=   (e[0], e[1])
    el.append(SCREAMING_SNAKE_CASE	)
  freq_sub_el.append(SCREAMING_SNAKE_CASE	)
 return freq_sub_el







def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					Optional[int]:

 """simple docstring"""






 for i in range(len(SCREAMING_SNAKE_CASE	)	):
  for j in range(len(edge_array[i]	)	):
   __lowerCAmelCase:     Dict     			=   edge_array[i][j].split('-'	)
   __lowerCAmelCase:     Any     			=   t


if __name__ == "__main__":
  preprocess(edge_array)
  _a	 =     get_frequency_table(edge_array)
  _a	 =     get_nodes(frequency_table)
  _a	 =     get_cluster(nodes)
  _a	 =     get_support(cluster)
  _a	 =     construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(6_0, cluster, graph)
  _a	 =     []
  _a	 =     freq_subgraphs_edge_list(paths)
  print_all()








from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

      self.register_modules(vae=UpperCAmelCase    ,						text_encoder=UpperCAmelCase    ,						tokenizer=UpperCAmelCase    ,						unet=UpperCAmelCase    ,						scheduler=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

         __lowerCAmelCase:     List[Any]     			=   (mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         __lowerCAmelCase:     Optional[Any]     			=   torch.cat([input] * 2      )

        else:  # content generation phase
         __lowerCAmelCase:     str     			=   torch.cat([latents] * 2      )

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





