'''simple docstring'''


import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class      __SCREAMING_SNAKE_CASE						(       _lowerCamelCase       ):







  '''simple docstring'''






  lowerCamelCase_		:Optional[Any]						    =		42
  lowerCamelCase_		:Optional[Any]						    =		None







def 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							List[Any]=0.999	,     lowerCamelCase_					:							Dict="cosine"	,     ):



     """simple docstring"""





     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCamelCase_					:							int   ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCamelCase_					:							Dict   ):
               return math.exp(t * -12.0   )

     else:
          raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

     UpperCAmelCase_							:   Dict   =      []
     for i in range(SCREAMING_SNAKE_CASE_   ):
          UpperCAmelCase_							:   int   =      i / num_diffusion_timesteps
          UpperCAmelCase_							:   List[Any]   =      (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_   ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_   )	,     SCREAMING_SNAKE_CASE_   )   )
     return torch.tensor(SCREAMING_SNAKE_CASE_	,     dtype=torch.floataa   )






class      __SCREAMING_SNAKE_CASE						(       _lowerCamelCase						,	_lowerCamelCase       ):







  '''simple docstring'''






  lowerCamelCase_		:str						    =		[e.name for e in KarrasDiffusionSchedulers]
  lowerCamelCase_		:Dict						    =		1


  @register_to_config
  def __init__( self      ,		snake_case_ = 1_0_0_0      ,		snake_case_ = 0.00_01      ,		snake_case_ = 0.02      ,		snake_case_ = "linear"      ,		snake_case_ = None      ,		snake_case_ = False      ,		snake_case_ = "epsilon"      ,		snake_case_ = "linspace"      ,		snake_case_ = 0      ,		):




       '''simple docstring'''

       if trained_betas is not None:
            UpperCAmelCase_							:   Optional[Any]   =      torch.tensor(A__      ,		dtype=torch.floataa						)
       elif beta_schedule == "linear":
            UpperCAmelCase_							:   Dict   =      torch.linspace(A__      ,		A__      ,		A__      ,		dtype=torch.floataa						)
       elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            UpperCAmelCase_							:   Any   =      (
                torch.linspace(beta_start**0.5      ,		beta_end**0.5      ,		A__      ,		dtype=torch.floataa						) ** 2
            )
       elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            UpperCAmelCase_							:   int   =      betas_for_alpha_bar(A__						)
       else:
            raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

       UpperCAmelCase_							:   Tuple   =      1.0 - self.betas
       UpperCAmelCase_							:   Union[str, Any]   =      torch.cumprod(self.alphas      ,		dim=0						)

       UpperCAmelCase_							:   List[str]   =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
       UpperCAmelCase_							:   Dict   =      np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
       UpperCAmelCase_							:   List[str]   =      torch.from_numpy(A__						)

       # setable values
       UpperCAmelCase_							:   List[str]   =      None
       UpperCAmelCase_							:   List[str]   =      use_karras_sigmas
       self.set_timesteps(A__      ,		A__						)
       UpperCAmelCase_							:   Any   =      []
       UpperCAmelCase_							:   List[str]   =      False


  @property
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       if isinstance(A__      ,		torch.Tensor						):
            UpperCAmelCase_							:   Tuple   =      timestep.to(self.timesteps.device						)
       UpperCAmelCase_							:   str   =      (self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:   List[Any]   =      self.sigmas[step_index]
       UpperCAmelCase_							:   Dict   =      sample / ((sigma**2 + 1) ** 0.5)
       UpperCAmelCase_							:   List[Any]   =      True
       return sample


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       def lms_derivative(snake_case_						):
            UpperCAmelCase_							:   Any   =      1.0
            for k in range(A__						):
                 if current_order == k:
                      continue
                 prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
            return prod

       UpperCAmelCase_							:   str   =      integrate.quad(A__      ,		self.sigmas[t]      ,		self.sigmas[t + 1]      ,		epsrel=1E-4						)[0]

       return integrated_coeff


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
            UpperCAmelCase_							:   List[Any]   =      np.linspace(0      ,		self.config.num_train_timesteps - 1      ,		A__      ,		dtype=A__						)[
                ::-1
            ].copy()
       elif self.config.timestep_spacing == "leading":
            UpperCAmelCase_							:   Optional[Any]   =      self.config.num_train_timesteps // self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            UpperCAmelCase_							:   int   =      (np.arange(0      ,		A__						) * step_ratio).round()[::-1].copy().astype(A__						)
            timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
            UpperCAmelCase_							:   List[Any]   =      self.config.num_train_timesteps / self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            UpperCAmelCase_							:   List[str]   =      (np.arange(self.config.num_train_timesteps      ,		0      ,		-step_ratio						)).round().copy().astype(A__						)
            timesteps -= 1
       else:
            raise ValueError(
                F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

       UpperCAmelCase_							:   Union[str, Any]   =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
       UpperCAmelCase_							:   List[str]   =      np.log(A__						)
       UpperCAmelCase_							:   Tuple   =      np.interp(A__      ,		np.arange(0      ,		len(A__						)						)      ,		A__						)

       if self.use_karras_sigmas:
            UpperCAmelCase_							:   Optional[int]   =      self._convert_to_karras(in_sigmas=A__						)
            UpperCAmelCase_							:   Tuple   =      np.array([self._sigma_to_t(A__      ,		A__						) for sigma in sigmas]						)

       UpperCAmelCase_							:   int   =      np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

       UpperCAmelCase_							:   List[str]   =      torch.from_numpy(A__						).to(device=A__						)
       if str(A__						).startswith('mps'						):
            # mps does not support float64
            UpperCAmelCase_							:   Optional[Any]   =      torch.from_numpy(A__						).to(A__      ,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:   List[str]   =      torch.from_numpy(A__						).to(device=A__						)

       UpperCAmelCase_							:   Union[str, Any]   =      []


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      np.log(A__						)

       # get distribution
       UpperCAmelCase_							:   int   =      log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       UpperCAmelCase_							:   int   =      np.cumsum((dists >= 0)      ,		axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
       UpperCAmelCase_							:   Dict   =      low_idx + 1

       UpperCAmelCase_							:   Optional[Any]   =      log_sigmas[low_idx]
       UpperCAmelCase_							:   Any   =      log_sigmas[high_idx]

       # interpolate sigmas
       UpperCAmelCase_							:   Optional[Any]   =      (low - log_sigma) / (low - high)
       UpperCAmelCase_							:   Union[str, Any]   =      np.clip(A__      ,		0      ,		1						)

       # transform interpolation to time range
       UpperCAmelCase_							:   Any   =      (1 - w) * low_idx + w * high_idx
       UpperCAmelCase_							:   Any   =      t.reshape(sigma.shape						)
       return t


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      in_sigmas[-1].item()
       UpperCAmelCase_							:   Tuple   =      in_sigmas[0].item()

       UpperCAmelCase_							:   Optional[Any]   =      7.0  # 7.0 is the value used in the paper
       UpperCAmelCase_							:   int   =      np.linspace(0      ,		1      ,		self.num_inference_steps						)
       UpperCAmelCase_							:   str   =      sigma_min ** (1 / rho)
       UpperCAmelCase_							:   Any   =      sigma_max ** (1 / rho)
       UpperCAmelCase_							:   Tuple   =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_ = 4      ,		snake_case_ = True      ,		):




       '''simple docstring'''

       if not self.is_scale_input_called:
            warnings.warn(
                'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                'See `StableDiffusionPipeline` for a usage example.'						)

       if isinstance(A__      ,		torch.Tensor						):
            UpperCAmelCase_							:   int   =      timestep.to(self.timesteps.device						)
       UpperCAmelCase_							:   str   =      (self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:   List[str]   =      self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
            UpperCAmelCase_							:   List[Any]   =      sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
            # * c_out + input * c_skip
            UpperCAmelCase_							:   Optional[Any]   =      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
            UpperCAmelCase_							:   Tuple   =      model_output
       else:
            raise ValueError(
                F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

       # 2. Convert to an ODE derivative
       UpperCAmelCase_							:   Union[str, Any]   =      (sample - pred_original_sample) / sigma
       self.derivatives.append(A__						)
       if len(self.derivatives						) > order:
            self.derivatives.pop(0						)

       # 3. Compute linear multistep coefficients
       UpperCAmelCase_							:   Any   =      min(step_index + 1      ,		A__						)
       UpperCAmelCase_							:   Tuple   =      [self.get_lms_coefficient(A__      ,		A__      ,		A__						) for curr_order in range(A__						)]

       # 4. Compute previous sample based on the derivatives path
       UpperCAmelCase_							:   int   =      sample + sum(
           coeff * derivative for coeff, derivative in zip(A__      ,		reversed(self.derivatives						)						)						)

       if not return_dict:
            return (prev_sample,)

       return LMSDiscreteSchedulerOutput(prev_sample=A__      ,		pred_original_sample=A__						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      self.sigmas.to(device=original_samples.device      ,		dtype=original_samples.dtype						)
       if original_samples.device.type == "mps" and torch.is_floating_point(A__						):
            # mps does not support float64
            UpperCAmelCase_							:   Optional[int]   =      self.timesteps.to(original_samples.device      ,		dtype=torch.floataa						)
            UpperCAmelCase_							:   Optional[int]   =      timesteps.to(original_samples.device      ,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:   Dict   =      self.timesteps.to(original_samples.device						)
            UpperCAmelCase_							:   int   =      timesteps.to(original_samples.device						)

       UpperCAmelCase_							:   str   =      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

       UpperCAmelCase_							:   Any   =      sigmas[step_indices].flatten()
       while len(sigma.shape						) < len(original_samples.shape						):
            UpperCAmelCase_							:   Dict   =      sigma.unsqueeze(-1						)

       UpperCAmelCase_							:   Dict   =      original_samples + noise * sigma
       return noisy_samples






  def __len__( self						):




       '''simple docstring'''

       return self.config.num_train_timesteps








'''simple docstring'''



UpperCamelCase__					:						Tuple											=     [
    ["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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = set()
   for row in edge_array:
      for item in row:
         distinct_edge.add(item[0]  )
   return list(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = ["""0"""] * len(SCREAMING_SNAKE_CASE_  )
   for i, row in enumerate(SCREAMING_SNAKE_CASE_  ):
      for item in row:
         if distinct_edge in item[0]:
            _SCREAMING_SNAKE_CASE          = """1"""
            break
   return "".join(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = get_distinct_edge(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = {}

   for item in distinct_edge:
      _SCREAMING_SNAKE_CASE          = get_bitcode(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
      # print('bit',bit)
      # bt=''.join(bit)
      _SCREAMING_SNAKE_CASE          = bit.count("""1"""  )
      _SCREAMING_SNAKE_CASE          = [s, bit]
   # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
   _SCREAMING_SNAKE_CASE          = [
       [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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = {}
   for _, item in enumerate(SCREAMING_SNAKE_CASE_  ):
      nodes.setdefault(item[2]			,					[]  ).append(item[0]  )
   return nodes
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = {}
   for key, value in nodes.items():
      _SCREAMING_SNAKE_CASE          = value
   return cluster
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   str:




   """simple docstring"""
   return [i * 1_00 / len(SCREAMING_SNAKE_CASE_  ) for i in cluster]
def       lowerCAmelCase_		( )   ->   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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   for i in cluster[ca]:
      _SCREAMING_SNAKE_CASE          = 0
      _SCREAMING_SNAKE_CASE          = 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:
                  _SCREAMING_SNAKE_CASE          = [nodes[j]]
               count += 1
         if count == 0:
            _SCREAMING_SNAKE_CASE          = ca + 1
         else:
            break
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = cluster[max(cluster.keys()  )]
   _SCREAMING_SNAKE_CASE          = """Header"""
   _SCREAMING_SNAKE_CASE          = {}
   for i in x:
      if (["Header"],) in graph:
         graph[(["Header"],)].append(x[i]  )
      else:
         _SCREAMING_SNAKE_CASE          = [x[i]]
   for i in x:
      _SCREAMING_SNAKE_CASE          = [["""Header"""]]
   _SCREAMING_SNAKE_CASE          = 1
   while i < max(SCREAMING_SNAKE_CASE_  ) - 1:
      create_edge(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = i + 1
   return graph
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=None  )   ->   List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = (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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = 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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = []
   for edges in paths:
      _SCREAMING_SNAKE_CASE          = []
      for j in range(len(SCREAMING_SNAKE_CASE_  ) - 1  ):
         _SCREAMING_SNAKE_CASE          = list(edges[j]  )
         for e in temp:
            _SCREAMING_SNAKE_CASE          = (e[0], e[1])
            el.append(SCREAMING_SNAKE_CASE_  )
      freq_sub_el.append(SCREAMING_SNAKE_CASE_  )
   return freq_sub_el
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   for i in range(len(SCREAMING_SNAKE_CASE_  )  ):
      for j in range(len(edge_array[i]  )  ):
         _SCREAMING_SNAKE_CASE          = edge_array[i][j].split("""-"""  )
         _SCREAMING_SNAKE_CASE          = t


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






