





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[int]       	=			[nn.LayerNorm]

UpperCamelCase__ :			int       	=			logging.get_logger(__name__)

UpperCamelCase__ :			Dict       	=			version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase__ :			str       	=			parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase__ :			Any       	=			parsed_torch_version_base < version.parse("""1.11""")




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







class 	_UpperCamelCase	(							nn.Module	):







					'''simple docstring'''

					def __init__(    self     :	Union[str, Any]			,       __lowercase     :	List[Any]			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_		    =  nf
						UpperCAmelCase_		    =  nn.Parameter(torch.empty(__lowercase			,       __lowercase		)		)
						UpperCAmelCase_		    =  nn.Parameter(torch.zeros(__lowercase		)		)
						nn.init.normal_(self.weight			,       std=0.02		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Tuple		):






						'''simple docstring'''







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




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




def  A_(      A					,   A					,   A = None ):
	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 ):
	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
	UpperCAmelCase_		    =  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:
		UpperCAmelCase_		    =  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}""" )

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

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




def  A_(      *A					,   A = None ):
	return torch.meshgrid(*A					,   indexing=A )




def  A_(      A ):
	return tensor.device, storage_ptr(A ), storage_size(A )









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 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""cython"""] )

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

	UpperCAmelCase_		    =  np.asarray(item["""edge_index"""]					,   dtype=np.intaa )

	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1
	UpperCAmelCase_		    =  item["""num_nodes"""]

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

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

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

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

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

	return item







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __lowercase     :	Union[str, Any]=False		):






						'''simple docstring'''







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

						UpperCAmelCase_		    =  spatial_pos_max
						UpperCAmelCase_		    =  on_the_fly_processing





					def __call__(    self     :	List[str]			,       __lowercase     :	List[dict]		):






						'''simple docstring'''







						if self.on_the_fly_processing:
							UpperCAmelCase_		    =  [preprocess_item(__lowercase		) for i in features]

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

						UpperCAmelCase_		    =  max(len(i["""input_nodes"""]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_nodes"""][0]		)
						UpperCAmelCase_		    =  len(features[0]["""attn_edge_type"""][0][0]		)
						UpperCAmelCase_		    =  max(len(i["""input_edges"""][0][0]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_edges"""][0][0][0]		)
						UpperCAmelCase_		    =  len(__lowercase		)

						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       max_node_num + 1			,       max_node_num + 1			,       dtype=torch.float		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(
						    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)

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

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

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

						UpperCAmelCase_		    =  batch["""in_degree"""]

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

						return batch


