
"""simple docstring"""






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 		_snake_case			(      lowercase__						:    List[Any] ,	lowercase__						:    int = 5_1_2						)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Optional[int]     	=    x.shape[1] if len(x.shape						) > 1 else 1
			lowerCAmelCase_       :Any     	=    1 + np.arange(0 ,	feature_num * offset ,	lowercase__ ,	dtype=np.intaa						)
			lowerCAmelCase_       :Any     	=    x + feature_offset
			return x






def 		_snake_case			(      lowercase__						:    Tuple ,	lowercase__						:    str=True						)    ->							Optional[Any]:


			'''simple docstring'''





			requires_backends(lowercase__ ,	["""cython"""]						)

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

			lowerCAmelCase_       :Optional[Any]     	=    np.asarray(item["""edge_index"""] ,	dtype=np.intaa						)

			lowerCAmelCase_       :Optional[Any]     	=    convert_to_single_emb(lowercase__						) + 1
			lowerCAmelCase_       :Tuple     	=    item["""num_nodes"""]

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

			# node adj matrix [num_nodes, num_nodes] bool
			lowerCAmelCase_       :List[str]     	=    np.zeros([num_nodes, num_nodes] ,	dtype=lowercase__						)
			lowerCAmelCase_       :Dict     	=    True

			lowerCAmelCase_	,			lowerCAmelCase_       :int     	=    algos_graphormer.floyd_warshall(lowercase__						)
			lowerCAmelCase_       :Tuple     	=    np.amax(lowercase__						)

			lowerCAmelCase_       :List[str]     	=    algos_graphormer.gen_edge_input(lowercase__ ,	lowercase__ ,	lowercase__						)
			lowerCAmelCase_       :Union[str, Any]     	=    np.zeros([num_nodes + 1, num_nodes + 1] ,	dtype=np.single						)  # with graph token

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

			return item




class      _SCREAMING_SNAKE_CASE   :






			def __init__(   self			,  __A=20			,  __A=False			)     ->	List[str]:
						if not is_cython_available():
									raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""			)

						lowerCAmelCase_       :Any     	=    spatial_pos_max
						lowerCAmelCase_       :Any     	=    on_the_fly_processing






			def __call__(   self			,  __A			)     ->	Dict[str, Any]:
						if self.on_the_fly_processing:
									lowerCAmelCase_       :List[str]     	=    [preprocess_item(__A			) for i in features]

						if not isinstance(features[0]			,  __A			):
									lowerCAmelCase_       :Optional[int]     	=    [vars(__A			) for f in features]
						lowerCAmelCase_       :Any     	=    {}

						lowerCAmelCase_       :Optional[Any]     	=    max(len(i["""input_nodes"""]			) for i in features			)
						lowerCAmelCase_       :List[Any]     	=    len(features[0]["""input_nodes"""][0]			)
						lowerCAmelCase_       :List[str]     	=    len(features[0]["""attn_edge_type"""][0][0]			)
						lowerCAmelCase_       :List[str]     	=    max(len(i["""input_edges"""][0][0]			) for i in features			)
						lowerCAmelCase_       :Tuple     	=    len(features[0]["""input_edges"""][0][0][0]			)
						lowerCAmelCase_       :Any     	=    len(__A			)

						lowerCAmelCase_       :Dict     	=    torch.zeros(__A			,  max_node_num + 1			,  max_node_num + 1			,  dtype=torch.float			)
						lowerCAmelCase_       :Tuple     	=    torch.zeros(__A			,  __A			,  __A			,  __A			,  dtype=torch.long			)
						lowerCAmelCase_       :List[Any]     	=    torch.zeros(__A			,  __A			,  __A			,  dtype=torch.long			)
						lowerCAmelCase_       :Optional[Any]     	=    torch.zeros(__A			,  __A			,  dtype=torch.long			)
						lowerCAmelCase_       :Tuple     	=    torch.zeros(__A			,  __A			,  __A			,  dtype=torch.long			)
						lowerCAmelCase_       :List[Any]     	=    torch.zeros(
						    __A			,  __A			,  __A			,  __A			,  __A			,  dtype=torch.long			)

						for ix, f in enumerate(__A			):
									for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
												lowerCAmelCase_       :Union[str, Any]     	=    torch.tensor(f[k]			)

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

									lowerCAmelCase_       :Tuple     	=    f["""attn_bias"""]
									lowerCAmelCase_       :List[Any]     	=    f[
									    """attn_edge_type"""
									]
									lowerCAmelCase_       :int     	=    f["""spatial_pos"""]
									lowerCAmelCase_       :Tuple     	=    f["""in_degree"""]
									lowerCAmelCase_       :str     	=    f["""input_nodes"""]
									lowerCAmelCase_       :Optional[Any]     	=    f["""input_edges"""]

						lowerCAmelCase_       :Optional[int]     	=    batch["""in_degree"""]

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

						return batch








"""simple docstring"""






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
				import torch

__UpperCAmelCase 					=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
				from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






				def 		_snake_case			(      cls						:    Tuple						)    ->							List[Any]:


							'''simple docstring'''





							return cls






def 		_snake_case			(      lowercase__						:    Union[Tuple, List] ,	lowercase__						:    Optional[Union[List["torch.Generator"], "torch.Generator"]] = None ,	lowercase__						:    Optional["torch.device"] = None ,	lowercase__						:    Optional["torch.dtype"] = None ,	lowercase__						:    Optional["torch.layout"] = None ,	)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    device
			lowerCAmelCase_       :Any     	=    shape[0]

			lowerCAmelCase_       :Dict     	=    layout or torch.strided
			lowerCAmelCase_       :List[str]     	=    device or torch.device("""cpu"""						)

			if generator is not None:
						lowerCAmelCase_       :Optional[Any]     	=    generator.device.type if not isinstance(lowercase__ ,	lowercase__						) else generator[0].device.type
						if gen_device_type != device.type and gen_device_type == "cpu":
									lowerCAmelCase_       :Optional[int]     	=    """cpu"""
									if device != "mps":
												logger.info(
												    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
												    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
												    f""" slighly speed up this function by passing a generator that was created on the {device} device."""						)
						elif gen_device_type != device.type and gen_device_type == "cuda":
									raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""						)

			if isinstance(lowercase__ ,	lowercase__						):
						lowerCAmelCase_       :List[str]     	=    (1,) + shape[1:]
						lowerCAmelCase_       :Optional[Any]     	=    [
						    torch.randn(lowercase__ ,	generator=generator[i] ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						)
						    for i in range(lowercase__						)
						]
						lowerCAmelCase_       :str     	=    torch.cat(lowercase__ ,	dim=0						).to(lowercase__						)
			else:
						lowerCAmelCase_       :Dict     	=    torch.randn(lowercase__ ,	generator=lowercase__ ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						).to(lowercase__						)

			return latents






def 		_snake_case			(      lowercase__						:    Union[str, Any]						)    ->							int:


			'''simple docstring'''





			if is_torch_version("""<""" ,	"""2.0.0"""						) or not hasattr(lowercase__ ,	"""_dynamo"""						):
						return False
			return isinstance(lowercase__ ,	torch._dynamo.eval_frame.OptimizedModule						)






