code: 
  
  
  '''simple docstring'''
  
  import inspect
  from typing import Callable, List, Optional, Set, Tuple, Union
  
  import torch
  from packaging import version
  from safetensors.torch import storage_ptr, storage_size
  from torch import nn
  
  from .utils import logging
  
  
  lowerCAmelCase   :Optional[Any]								=    [nn.LayerNorm]
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Union[str, Any]								=    version.parse(version.parse(torch.__version__).base_version)
  
  lowerCAmelCase   :Any								=    parsed_torch_version_base >= version.parse('''2.0''')
  lowerCAmelCase   :Union[str, Any]								=    parsed_torch_version_base >= version.parse('''1.12''')
  lowerCAmelCase   :Optional[int]								=    parsed_torch_version_base >= version.parse('''1.11''')
  lowerCAmelCase   :Dict								=    parsed_torch_version_base < version.parse('''1.11''')
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			List[Any]	,	self       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	from torch import _softmax_backward_data
  
  	if is_torch_less_than_1_11:
  		return _softmax_backward_data(lowerCAmelCase	,	lowerCAmelCase	,	parent.dim	,	self	)
  	else:
  		return _softmax_backward_data(lowerCAmelCase	,	lowerCAmelCase	,	parent.dim	,	self.dtype	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			str = 0	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  index.to(layer.weight.device	)
  	__magic_name__ :					Dict  			=  layer.weight.index_select(lowerCAmelCase	,	lowerCAmelCase	).clone().detach()
  	if layer.bias is not None:
  		if dim == 1:
  			__magic_name__ :					Optional[Any]  			=  layer.bias.clone().detach()
  		else:
  			__magic_name__ :					Tuple  			=  layer.bias[index].clone().detach()
  	__magic_name__ :					List[Any]  			=  list(layer.weight.size()	)
  	__magic_name__ :					Tuple  			=  len(lowerCAmelCase	)
  	__magic_name__ :					str  			=  nn.Linear(new_size[1]	,	new_size[0]	,	bias=layer.bias is not None	).to(layer.weight.device	)
  	__magic_name__ :					Tuple  			=  False
  	new_layer.weight.copy_(W.contiguous()	)
  	__magic_name__ :					Union[str, Any]  			=  True
  	if layer.bias is not None:
  		__magic_name__ :					int  			=  False
  		new_layer.bias.copy_(b.contiguous()	)
  		__magic_name__ :					List[str]  			=  True
  	return new_layer
  class _lowerCamelCase (		nn.Module							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		Union[str, Any]						,  _A	:		int						,  _A	:		List[Any]    )						->				Dict:
  			super().__init__()
  			__magic_name__ :					Dict  			=  nf
  			__magic_name__ :					Optional[Any]  			=  nn.Parameter(torch.empty(UpperCamelCase__						,  UpperCamelCase__    )    )
  			__magic_name__ :					List[Any]  			=  nn.Parameter(torch.zeros(UpperCamelCase__    )    )
  			nn.init.normal_(self.weight						,  std=0.02    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		List[str]    )						->				Dict:
  			__magic_name__ :					Union[str, Any]  			=  x.size()[:-1] + (self.nf,)
  			__magic_name__ :					Dict  			=  torch.addmm(self.bias						,  x.view(-1						,  x.size(-1    )    )						,  self.weight    )
  			__magic_name__ :					Tuple  			=  x.view(UpperCamelCase__    )
  			return x
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Any	,	lowerCAmelCase       :			int = 1	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  index.to(layer.weight.device	)
  	__magic_name__ :					str  			=  layer.weight.index_select(lowerCAmelCase	,	lowerCAmelCase	).clone().detach()
  	if dim == 0:
  		__magic_name__ :					int  			=  layer.bias.clone().detach()
  	else:
  		__magic_name__ :					Optional[int]  			=  layer.bias[index].clone().detach()
  	__magic_name__ :					int  			=  list(layer.weight.size()	)
  	__magic_name__ :					Union[str, Any]  			=  len(lowerCAmelCase	)
  	__magic_name__ :					List[str]  			=  ConvaD(new_size[1]	,	new_size[0]	).to(layer.weight.device	)
  	__magic_name__ :					str  			=  False
  	new_layer.weight.copy_(W.contiguous()	)
  	__magic_name__ :					Optional[int]  			=  True
  	__magic_name__ :					Optional[Any]  			=  False
  	new_layer.bias.copy_(b.contiguous()	)
  	__magic_name__ :					str  			=  True
  	return new_layer
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Any = None	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if isinstance(lowerCAmelCase	,	nn.Linear	):
  		return prune_linear_layer(lowerCAmelCase	,	lowerCAmelCase	,	dim=0 if dim is None else dim	)
  	elif isinstance(lowerCAmelCase	,	lowerCAmelCase	):
  		return prune_convad_layer(lowerCAmelCase	,	lowerCAmelCase	,	dim=1 if dim is None else dim	)
  	else:
  		raise ValueError(f'Can\'t prune layer of class {layer.__class__}'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[str]	,	*lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	assert len(lowerCAmelCase	) > 0, f'{input_tensors} has to be a tuple/list of tensors'
  
  	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
  	__magic_name__ :					Union[str, Any]  			=  len(inspect.signature(lowerCAmelCase	).parameters	)
  	if num_args_in_forward_chunk_fn != len(lowerCAmelCase	):
  		raise ValueError(
  		    f'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowerCAmelCase	)} input '
  		    'tensors are given'	)
  
  	if chunk_size > 0:
  		__magic_name__ :					Optional[Any]  			=  input_tensors[0].shape[chunk_dim]
  		for input_tensor in input_tensors:
  			if input_tensor.shape[chunk_dim] != tensor_shape:
  				raise ValueError(
  				    f'All input tenors have to be of the same shape: {tensor_shape}, '
  				    f'found shape {input_tensor.shape[chunk_dim]}'	)
  
  		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
  			raise ValueError(
  			    f'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
  			    f'size {chunk_size}'	)
  
  		__magic_name__ :					List[Any]  			=  input_tensors[0].shape[chunk_dim] // chunk_size
  
  		# chunk input tensor into tuples
  		__magic_name__ :					str  			=  tuple(input_tensor.chunk(lowerCAmelCase	,	dim=lowerCAmelCase	) for input_tensor in input_tensors	)
  		# apply forward fn to every tuple
  		__magic_name__ :					Optional[Any]  			=  tuple(forward_fn(*lowerCAmelCase	) for input_tensors_chunk in zip(*lowerCAmelCase	)	)
  		# concatenate output at same dimension
  		return torch.cat(lowerCAmelCase	,	dim=lowerCAmelCase	)
  
  	return forward_fn(*lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  torch.ones(lowerCAmelCase	,	lowerCAmelCase	)
  	__magic_name__ :					Any  			=  set(lowerCAmelCase	) - already_pruned_heads  # Convert to set and remove already pruned heads
  	for head in heads:
  		# Compute how many pruned heads are before the head and move the index accordingly
  		__magic_name__ :					str  			=  head - sum(1 if h < head else 0 for h in already_pruned_heads	)
  		__magic_name__ :					Optional[int]  			=  0
  	__magic_name__ :					str  			=  mask.view(-1	).contiguous().eq(1	)
  	__magic_name__ :					List[Any]  			=  torch.arange(len(lowerCAmelCase	)	)[mask].long()
  	return heads, index
  
  
  
  
  
  def lowerCamelCase    (    *lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Any = None	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	return torch.meshgrid(*lowerCAmelCase	,	indexing=lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	return tensor.device, storage_ptr(lowerCAmelCase	), storage_size(lowerCAmelCase	)
style_context: 
  
  
  '''simple docstring'''
  
  lowerCAmelCase   :int								=    [
      ['''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    (    lowerCAmelCase       :			Tuple	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  set()
  	for row in edge_array:
  		for item in row:
  			distinct_edge.add(item[0]	)
  	return list(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[Any]  			=  ['0'] * len(lowerCAmelCase	)
  	for i, row in enumerate(lowerCAmelCase	):
  		for item in row:
  			if distinct_edge in item[0]:
  				__magic_name__ :					str  			=  '1'
  				break
  	return "".join(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Tuple	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  get_distinct_edge(lowerCAmelCase	)
  	__magic_name__ :					Union[str, Any]  			=  {}
  
  	for item in distinct_edge:
  		__magic_name__ :					Union[str, Any]  			=  get_bitcode(lowerCAmelCase	,	lowerCAmelCase	)
  		# print('bit',bit)
  		# bt=''.join(bit)
  		__magic_name__ :					Dict  			=  bit.count('1'	)
  		__magic_name__ :					Dict  			=  [s, bit]
  	# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
  	__magic_name__ :					Optional[Any]  			=  [
  	    [k, v[0], v[1]]
  	    for k, v in sorted(frequency_table.items()	,	key=lambda lowerCAmelCase	: v[1][0]	,	reverse=lowerCAmelCase	)
  	]
  	return sorted_frequency_table
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Tuple  			=  {}
  	for _, item in enumerate(lowerCAmelCase	):
  		nodes.setdefault(item[2]	,	[]	).append(item[0]	)
  	return nodes
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  {}
  	for key, value in nodes.items():
  		__magic_name__ :					Union[str, Any]  			=  value
  	return cluster
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	return [i * 100 / len(lowerCAmelCase	) for i in cluster]
  
  
  
  
  
  def lowerCamelCase    (    ):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	print('\nNodes\n'	)
  	for key, value in nodes.items():
  		print(lowerCAmelCase	,	lowerCAmelCase	)
  	print('\nSupport\n'	)
  	print(lowerCAmelCase	)
  	print('\n Cluster \n'	)
  	for key, value in sorted(cluster.items()	,	reverse=lowerCAmelCase	):
  		print(lowerCAmelCase	,	lowerCAmelCase	)
  	print('\n Graph\n'	)
  	for key, value in graph.items():
  		print(lowerCAmelCase	,	lowerCAmelCase	)
  	print('\n Edge List of Frequent subgraphs \n'	)
  	for edge_list in freq_subgraph_edge_list:
  		print(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for i in cluster[ca]:
  		__magic_name__ :					Tuple  			=  0
  		__magic_name__ :					Any  			=  ca + 1
  		while ca < max(cluster.keys()	):
  			for j in cluster[ca]:
  				if int(lowerCAmelCase	,	2	) & int(lowerCAmelCase	,	2	) == int(lowerCAmelCase	,	2	):
  					if tuple(nodes[i]	) in graph:
  						graph[tuple(nodes[i]	)].append(nodes[j]	)
  					else:
  						__magic_name__ :					List[Any]  			=  [nodes[j]]
  					count += 1
  			if count == 0:
  				__magic_name__ :					Tuple  			=  ca + 1
  			else:
  				break
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Any	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  cluster[max(cluster.keys()	)]
  	__magic_name__ :					Dict  			=  'Header'
  	__magic_name__ :					Optional[Any]  			=  {}
  	for i in x:
  		if (["Header"],) in graph:
  			graph[(["Header"],)].append(x[i]	)
  		else:
  			__magic_name__ :					List[str]  			=  [x[i]]
  	for i in x:
  		__magic_name__ :					Union[str, Any]  			=  [['Header']]
  	__magic_name__ :					List[str]  			=  1
  	while i < max(lowerCAmelCase	) - 1:
  		create_edge(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  		__magic_name__ :					Dict  			=  i + 1
  	return graph
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			Union[str, Any]=None	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  (path or []) + [start]
  	if start == end:
  		paths.append(lowerCAmelCase	)
  	for node in graph[start]:
  		if tuple(lowerCAmelCase	) not in path:
  			my_dfs(lowerCAmelCase	,	tuple(lowerCAmelCase	)	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Union[str, Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Union[str, Any]  			=  int(s / 100 * (len(lowerCAmelCase	) - 1)	)
  	for i in cluster[k]:
  		my_dfs(lowerCAmelCase	,	tuple(cluster[k][i]	)	,	(['Header'],)	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  []
  	for edges in paths:
  		__magic_name__ :					List[Any]  			=  []
  		for j in range(len(lowerCAmelCase	) - 1	):
  			__magic_name__ :					List[str]  			=  list(edges[j]	)
  			for e in temp:
  				__magic_name__ :					Optional[int]  			=  (e[0], e[1])
  				el.append(lowerCAmelCase	)
  		freq_sub_el.append(lowerCAmelCase	)
  	return freq_sub_el
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for i in range(len(lowerCAmelCase	)	):
  		for j in range(len(edge_array[i]	)	):
  			__magic_name__ :					Any  			=  edge_array[i][j].split('-'	)
  			__magic_name__ :					Tuple  			=  t
  
  
  if __name__ == "__main__":
  							preprocess(edge_array)
  							lowerCAmelCase   :List[str]								=    get_frequency_table(edge_array)
  							lowerCAmelCase   :List[str]								=    get_nodes(frequency_table)
  							lowerCAmelCase   :Optional[int]								=    get_cluster(nodes)
  							lowerCAmelCase   :Tuple								=    get_support(cluster)
  							lowerCAmelCase   :Tuple								=    construct_graph(cluster, nodes)
  							find_freq_subgraph_given_support(6_0, cluster, graph)
  							lowerCAmelCase   :list								=    []
  							lowerCAmelCase   :str								=    freq_subgraphs_edge_list(paths)
  							print_all()