


'''simple docstring'''

from __future__ import annotations

from queue import Queue

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						a ^= b
						b ^= a
						a ^= b
						return a, b

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					1
						while (1 << j) < max_node:
												for i in range(1,					max_node + 1):
																		__lowerCAmelCase											=					parent[j - 1][parent[j - 1][i]]
												j += 1
						return parent

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						# u must be deeper in the tree than v
						if level[u] < level[v]:
												__lowerCAmelCase							,       __lowerCAmelCase											=					swap(lowerCamelCase,					lowerCamelCase)
						# making depth of u same as depth of v
						for i in range(1_8,					-1,					-1):
												if level[u] - (1 << i) >= level[v]:
																		__lowerCAmelCase											=					parent[i][u]
    # at the same depth if u==v that mean lca is found
						if u == v:
												return u
						# moving both nodes upwards till lca in found
						for i in range(1_8,					-1,					-1):
												if parent[i][u] not in [0, parent[i][v]]:
																		__lowerCAmelCase							,       __lowerCAmelCase											=					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
						return parent[0][u]

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 1,					):
						__lowerCAmelCase											=					0
						__lowerCAmelCase											=					Queue(maxsize=lowerCamelCase)
						q.put(lowerCamelCase)
						while q.qsize() != 0:
												__lowerCAmelCase											=					q.get()
												for v in graph[u]:
																		if level[v] == -1:
																								__lowerCAmelCase											=					level[u] + 1
																								q.put(lowerCamelCase)
																								__lowerCAmelCase											=					u
						return level, parent

def 				__magic_name__(    ):
						__lowerCAmelCase											=					1_3
						# initializing with 0
						__lowerCAmelCase											=					[[0 for _ in range(max_node + 1_0)] for _ in range(2_0)]
						# initializing with -1 which means every node is unvisited
						__lowerCAmelCase											=					[-1 for _ in range(max_node + 1_0)]
						__lowerCAmelCase											=					{
						    1: [2, 3, 4],
						    2: [5],
						    3: [6, 7],
						    4: [8],
						    5: [9, 1_0],
						    6: [1_1],
						    7: [],
						    8: [1_2, 1_3],
						    9: [],
						    1_0: [],
						    1_1: [],
						    1_2: [],
						    1_3: [],
						}
						__lowerCAmelCase							,       __lowerCAmelCase											=					breadth_first_search(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					1)
						__lowerCAmelCase											=					create_sparse(lowerCamelCase,					lowerCamelCase)
						print('''LCA of node 1 and 3 is: ''',					lowest_common_ancestor(1,					3,					lowerCamelCase,					lowerCamelCase))
						print('''LCA of node 5 and 6 is: ''',					lowest_common_ancestor(5,					6,					lowerCamelCase,					lowerCamelCase))
						print('''LCA of node 7 and 11 is: ''',					lowest_common_ancestor(7,					1_1,					lowerCamelCase,					lowerCamelCase))
						print('''LCA of node 6 and 7 is: ''',					lowest_common_ancestor(6,					7,					lowerCamelCase,					lowerCamelCase))
						print('''LCA of node 4 and 12 is: ''',					lowest_common_ancestor(4,					1_2,					lowerCamelCase,					lowerCamelCase))
						print('''LCA of node 8 and 8 is: ''',					lowest_common_ancestor(8,					8,					lowerCamelCase,					lowerCamelCase))


if __name__ == "__main__":
	main()





'''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


_UpperCAmelCase					:			Optional[Any]				     =					[nn.LayerNorm]

_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			Tuple				     =					version.parse(version.parse(torch.__version__).base_version)

_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""2.0""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base >= version.parse("""1.12""")
_UpperCAmelCase					:			List[str]				     =					parsed_torch_version_base >= version.parse("""1.11""")
_UpperCAmelCase					:			Optional[int]				     =					parsed_torch_version_base < version.parse("""1.11""")

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					self):
						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 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 0):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		__lowerCAmelCase											=					layer.bias.clone().detach()
												else:
																		__lowerCAmelCase											=					layer.bias[index].clone().detach()
						__lowerCAmelCase											=					list(layer.weight.size())
						__lowerCAmelCase											=					len(lowerCamelCase)
						__lowerCAmelCase											=					nn.Linear(new_size[1],					new_size[0],					bias=layer.bias is not None).to(layer.weight.device)
						__lowerCAmelCase											=					False
						new_layer.weight.copy_(W.contiguous())
						__lowerCAmelCase											=					True
						if layer.bias is not None:
												__lowerCAmelCase											=					False
												new_layer.bias.copy_(b.contiguous())
												__lowerCAmelCase											=					True
						return new_layer


class    a__					(			nn.Module   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase		):
										super().__init__()
										__lowerCAmelCase											=					nf
										__lowerCAmelCase											=					nn.Parameter(torch.empty(__lowercase							,      __lowercase		)		)
										__lowerCAmelCase											=					nn.Parameter(torch.zeros(__lowercase		)		)
										nn.init.normal_(self.weight							,      std=0.0_2		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					x.size()[:-1] + (self.nf,)
										__lowerCAmelCase											=					torch.addmm(self.bias							,      x.view(-1							,      x.size(-1		)		)							,      self.weight		)
										__lowerCAmelCase											=					x.view(__lowercase		)
										return x

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = 1):
						__lowerCAmelCase											=					index.to(layer.weight.device)
						__lowerCAmelCase											=					layer.weight.index_select(lowerCamelCase,					lowerCamelCase).clone().detach()
						if dim == 0:
												__lowerCAmelCase											=					layer.bias.clone().detach()
						else:
												__lowerCAmelCase											=					layer.bias[index].clone().detach()
						__lowerCAmelCase											=					list(layer.weight.size())
						__lowerCAmelCase											=					len(lowerCamelCase)
						__lowerCAmelCase											=					ConvaD(new_size[1],					new_size[0]).to(layer.weight.device)
						__lowerCAmelCase											=					False
						new_layer.weight.copy_(W.contiguous())
						__lowerCAmelCase											=					True
						__lowerCAmelCase											=					False
						new_layer.bias.copy_(b.contiguous())
						__lowerCAmelCase											=					True
						return new_layer

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase = None):
						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 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					*lowerCamelCase):
						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
						__lowerCAmelCase											=					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:
												__lowerCAmelCase											=					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}""")

												__lowerCAmelCase											=					input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												__lowerCAmelCase											=					tuple(input_tensor.chunk(lowerCamelCase,					dim=lowerCamelCase) for input_tensor in input_tensors)
												# apply forward fn to every tuple
												__lowerCAmelCase											=					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 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					torch.ones(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					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
												__lowerCAmelCase											=					head - sum(1 if h < head else 0 for h in already_pruned_heads)
												__lowerCAmelCase											=					0
						__lowerCAmelCase											=					mask.view(-1).contiguous().eq(1)
						__lowerCAmelCase											=					torch.arange(len(lowerCamelCase))[mask].long()
						return heads, index

def 				__magic_name__(    *lowerCamelCase,					lowerCamelCase = None):
						return torch.meshgrid(*lowerCamelCase,					indexing=lowerCamelCase)




def 				__magic_name__(    lowerCamelCase):
						return tensor.device, storage_ptr(lowerCamelCase), storage_size(lowerCamelCase)

