






from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={"""include_dirs""": np.get_include()})
							from . import algos_graphormer  # noqa E402
def        a_	( _A ,				_A = 512					)     ->     List[str]:







				"""simple docstring"""
				snake_case__													=       x.shape[1] if len(x.shape					) > 1 else 1
				snake_case__													=       1 + np.arange(0 ,				feature_num * offset ,				lowercase__ ,				dtype=np.intaa					)
				snake_case__													=       x + feature_offset
				return x
def        a_	( _A ,				_A=True					)     ->     List[str]:







				"""simple docstring"""
				requires_backends(lowercase__ ,				['cython']					)

				if keep_features and "edge_attr" in item.keys():  # edge_attr
								snake_case__													=       np.asarray(item['edge_attr'] ,				dtype=np.intaa					)
				else:
								snake_case__													=       np.ones((len(item['edge_index'][0]					), 1) ,				dtype=np.intaa					)  # same embedding for all

				if keep_features and "node_feat" in item.keys():  # input_nodes
								snake_case__													=       np.asarray(item['node_feat'] ,				dtype=np.intaa					)
				else:
								snake_case__													=       np.ones((item['num_nodes'], 1) ,				dtype=np.intaa					)  # same embedding for all

				snake_case__													=       np.asarray(item['edge_index'] ,				dtype=np.intaa					)

				snake_case__													=       convert_to_single_emb(lowercase__					) + 1
				snake_case__													=       item["num_nodes"]

				if len(edge_attr.shape					) == 1:
								snake_case__													=       edge_attr[:, None]
				snake_case__													=       np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,				dtype=np.intaa					)
				snake_case__													=       convert_to_single_emb(lowercase__					) + 1

				# node adj matrix [num_nodes, num_nodes] bool
				snake_case__													=       np.zeros([num_nodes, num_nodes] ,				dtype=lowercase__					)
				snake_case__													=       True

				snake_case__													=       algos_graphormer.floyd_warshall(lowercase__					)
				snake_case__													=       np.amax(lowercase__					)

				snake_case__													=       algos_graphormer.gen_edge_input(lowercase__ ,				lowercase__ ,				lowercase__					)
				snake_case__													=       np.zeros([num_nodes + 1, num_nodes + 1] ,				dtype=np.single					)  # with graph token

				# combine
				snake_case__													=       input_nodes + 1  # we shift all indices by one for padding
				snake_case__													=       attn_bias
				snake_case__													=       attn_edge_type
				snake_case__													=       shortest_path_result.astype(np.intaa					) + 1  # we shift all indices by one for padding
				snake_case__													=       np.sum(lowercase__ ,				axis=1					).reshape(-1					) + 1  # we shift all indices by one for padding
				snake_case__													=       item["in_degree"]  # for undirected graph
				snake_case__													=       input_edges + 1  # we shift all indices by one for padding
				if "labels" not in item:
								snake_case__													=       item["y"]

				return item


class 					__SCREAMING_SNAKE_CASE:
						def __init__(       self: Optional[int]    ,		UpperCamelCase: Tuple=20    ,		UpperCamelCase: Optional[int]=False  )						->						Any:







										if not is_cython_available():
														raise ImportError('Graphormer preprocessing needs Cython (pyximport)'  )

										snake_case__													=       spatial_pos_max
										snake_case__													=       on_the_fly_processing


						def __call__(       self: List[Any]    ,		UpperCamelCase: Dict  )						->						List[Any]:







										if self.on_the_fly_processing:
														snake_case__													=       [preprocess_item(UpperCamelCase  ) for i in features]

										if not isinstance(features[0]    ,		UpperCamelCase  ):
														snake_case__													=       [vars(UpperCamelCase  ) for f in features]
										snake_case__													=       {}

										snake_case__													=       max(len(i['input_nodes']  ) for i in features  )
										snake_case__													=       len(features[0]['input_nodes'][0]  )
										snake_case__													=       len(features[0]['attn_edge_type'][0][0]  )
										snake_case__													=       max(len(i['input_edges'][0][0]  ) for i in features  )
										snake_case__													=       len(features[0]['input_edges'][0][0][0]  )
										snake_case__													=       len(UpperCamelCase  )

										snake_case__													=       torch.zeros(UpperCamelCase    ,		max_node_num + 1    ,		max_node_num + 1    ,		dtype=torch.float  )
										snake_case__													=       torch.zeros(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		dtype=torch.long  )
										snake_case__													=       torch.zeros(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		dtype=torch.long  )
										snake_case__													=       torch.zeros(UpperCamelCase    ,		UpperCamelCase    ,		dtype=torch.long  )
										snake_case__													=       torch.zeros(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		dtype=torch.long  )
										snake_case__													=       torch.zeros(
										    UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		dtype=torch.long  )

										for ix, f in enumerate(UpperCamelCase  ):
														for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																		snake_case__													=       torch.tensor(f[k]  )

														if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]  ) > 0:
																		snake_case__													=       float('-inf'  )

														snake_case__													=       f["attn_bias"]
														snake_case__													=       f[
														    "attn_edge_type"
														]
														snake_case__													=       f["spatial_pos"]
														snake_case__													=       f["in_degree"]
														snake_case__													=       f["input_nodes"]
														snake_case__													=       f["input_edges"]

										snake_case__													=       batch["in_degree"]

										snake_case__													=       features[0]["labels"]
										if len(UpperCamelCase  ) == 1:  # one task
														if isinstance(sample[0]    ,		UpperCamelCase  ):  # regression
																		snake_case__													=       torch.from_numpy(np.concatenate([i['labels'] for i in features]  )  )
														else:  # binary classification
																		snake_case__													=       torch.from_numpy(np.concatenate([i['labels'] for i in features]  )  )
										else:  # multi task classification, left to float to keep the NaNs
														snake_case__													=       torch.from_numpy(np.stack([i['labels'] for i in features]    ,		axis=0  )  )

										return batch












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   :						int							=  [nn.LayerNorm]

__UpperCamelCase   :						List[str]							=  logging.get_logger(__name__)

__UpperCamelCase   :						str							=  version.parse(version.parse(torch.__version__).base_version)

__UpperCamelCase   :						List[str]							=  parsed_torch_version_base >= version.parse("""2.0""")
__UpperCamelCase   :						Dict							=  parsed_torch_version_base >= version.parse("""1.12""")
__UpperCamelCase   :						Tuple							=  parsed_torch_version_base >= version.parse("""1.11""")
__UpperCamelCase   :						Union[str, Any]							=  parsed_torch_version_base < version.parse("""1.11""")
def        a_	( _A ,				_A ,				_A ,				_A ,				self					)     ->     Any:







				"""simple docstring"""
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(_A ,				_A ,				parent.dim ,				self					)
				else:
								return _softmax_backward_data(_A ,				_A ,				parent.dim ,				self.dtype					)
def        a_	( _A ,				_A ,				_A = 0					)     ->     nn.Linear:







				"""simple docstring"""
				snake_case__													=       index.to(layer.weight.device					)
				snake_case__													=       layer.weight.index_select(_A ,				_A					).clone().detach()
				if layer.bias is not None:
								if dim == 1:
												snake_case__													=       layer.bias.clone().detach()
								else:
												snake_case__													=       layer.bias[index].clone().detach()
				snake_case__													=       list(layer.weight.size()					)
				snake_case__													=       len(_A					)
				snake_case__													=       nn.Linear(new_size[1] ,				new_size[0] ,				bias=layer.bias is not None					).to(layer.weight.device					)
				snake_case__													=       False
				new_layer.weight.copy_(W.contiguous()					)
				snake_case__													=       True
				if layer.bias is not None:
								snake_case__													=       False
								new_layer.bias.copy_(b.contiguous()					)
								snake_case__													=       True
				return new_layer


class 					__SCREAMING_SNAKE_CASE(		nn.Module		):
							def __init__(       self: Union[str, Any]    ,		UpperCamelCase: Dict    ,		UpperCamelCase: List[Any]  )						->						Any:
											super().__init__()
											snake_case__													=       nf
											snake_case__													=       nn.Parameter(torch.empty(UpperCamelCase    ,		UpperCamelCase  )  )
											snake_case__													=       nn.Parameter(torch.zeros(UpperCamelCase  )  )
											nn.init.normal_(self.weight    ,		std=0.02  )


							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: int  )						->						List[Any]:
											snake_case__													=       x.size()[:-1] + (self.nf,)
											snake_case__													=       torch.addmm(self.bias    ,		x.view(-1    ,		x.size(-1  )  )    ,		self.weight  )
											snake_case__													=       x.view(UpperCamelCase  )
											return x


def        a_	( _A ,				_A ,				_A = 1					)     ->     ConvaD:







				"""simple docstring"""
				snake_case__													=       index.to(layer.weight.device					)
				snake_case__													=       layer.weight.index_select(_A ,				_A					).clone().detach()
				if dim == 0:
								snake_case__													=       layer.bias.clone().detach()
				else:
								snake_case__													=       layer.bias[index].clone().detach()
				snake_case__													=       list(layer.weight.size()					)
				snake_case__													=       len(_A					)
				snake_case__													=       ConvaD(new_size[1] ,				new_size[0]					).to(layer.weight.device					)
				snake_case__													=       False
				new_layer.weight.copy_(W.contiguous()					)
				snake_case__													=       True
				snake_case__													=       False
				new_layer.bias.copy_(b.contiguous()					)
				snake_case__													=       True
				return new_layer
def        a_	( _A ,				_A ,				_A = None					)     ->     Union[nn.Linear, ConvaD]:







				"""simple docstring"""
				if isinstance(_A ,				nn.Linear					):
								return prune_linear_layer(_A ,				_A ,				dim=0 if dim is None else dim					)
				elif isinstance(_A ,				_A					):
								return prune_convad_layer(_A ,				_A ,				dim=1 if dim is None else dim					)
				else:
								raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''					)
def        a_	( _A ,				_A ,				_A ,				*_A					)     ->     torch.Tensor:







				"""simple docstring"""
				assert len(_A					) > 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
				snake_case__													=       len(inspect.signature(_A					).parameters					)
				if num_args_in_forward_chunk_fn != len(_A					):
								raise ValueError(
								    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_A					)} input '''
								    'tensors are given'					)

				if chunk_size > 0:
								snake_case__													=       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}'''					)

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

								# chunk input tensor into tuples
								snake_case__													=       tuple(input_tensor.chunk(_A ,				dim=_A					) for input_tensor in input_tensors					)
								# apply forward fn to every tuple
								snake_case__													=       tuple(forward_fn(*_A					) for input_tensors_chunk in zip(*_A					)					)
								# concatenate output at same dimension
								return torch.cat(_A ,				dim=_A					)

				return forward_fn(*_A					)
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     Tuple[Set[int], torch.LongTensor]:







				"""simple docstring"""
				snake_case__													=       torch.ones(_A ,				_A					)
				snake_case__													=       set(_A					) - 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
								snake_case__													=       head - sum(1 if h < head else 0 for h in already_pruned_heads					)
								snake_case__													=       0
				snake_case__													=       mask.view(-1					).contiguous().eq(1					)
				snake_case__													=       torch.arange(len(_A					)					)[mask].long()
				return heads, index
def        a_	( *_A ,				_A = None					)     ->     Tuple[torch.Tensor, ...]:







				"""simple docstring"""
				return torch.meshgrid(*_A ,				indexing=_A					)


def        a_	( _A					)     ->     Tuple[torch.device, int, int]:







				"""simple docstring"""
				return tensor.device, storage_ptr(_A					), storage_size(_A					)




