




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 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ = 512		):
    a__  			=							x.shape[1] if len(x.shape		) > 1 else 1
    a__  			=							1 + np.arange(0 ,     feature_num * offset ,     UpperCAmelCase_ ,     dtype=np.intaa		)
    a__  			=							x + feature_offset
    return x
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_=True		):
    requires_backends(UpperCAmelCase_ ,     ["""cython"""]		)

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

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

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

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

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

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

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

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

    return item







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(   self			:							List[str]    ,					A			:							str=20    ,					A			:							List[Any]=False     )      ->      List[Any]:






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

         a__  			=							spatial_pos_max
         a__  			=							on_the_fly_processing






     def __call__(   self			:							List[Any]    ,					A			:							List[dict]     )      ->      Dict[str, Any]:






         """simple docstring"""
         if self.on_the_fly_processing:
             a__  			=							[preprocess_item(A     ) for i in features]

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

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

         a__  			=							torch.zeros(A    ,					max_node_num + 1    ,					max_node_num + 1    ,					dtype=torch.float     )
         a__  			=							torch.zeros(A    ,					A    ,					A    ,					A    ,					dtype=torch.long     )
         a__  			=							torch.zeros(A    ,					A    ,					A    ,					dtype=torch.long     )
         a__  			=							torch.zeros(A    ,					A    ,					dtype=torch.long     )
         a__  			=							torch.zeros(A    ,					A    ,					A    ,					dtype=torch.long     )
         a__  			=							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"]:
                 a__  			=							torch.tensor(f[k]     )

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

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

         a__  			=							batch["""in_degree"""]

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

         return batch












import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


SCREAMING_SNAKE_CASE_    :       Tuple       =							logging.get_logger(__name__)







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							int     )      ->      Union[str, Any]:






         """simple docstring"""
         super().__init__()
         if version.parse(torch.__version__     ) < version.parse("""1.12.0"""     ):
             raise ImportError(
                 f"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
                 """PytorchGELUTanh. Please upgrade torch."""     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.gelu(A    ,					approximate="""tanh"""     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3.0     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							bool = False     )      ->      Optional[Any]:






         """simple docstring"""
         super().__init__()
         if use_gelu_python:
             a__  			=							self._gelu_python
         else:
             a__  			=							nn.functional.gelu

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0     )     ))






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1.0 + torch.tanh(input * 0.7_9_7_8_8_4_5_6_0_8 * (1.0 + 0.0_4_4_7_1_5 * input * input)     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.sigmoid(1.7_0_2 * input     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple    ,					A			:							float    ,					A			:							float     )      ->      Optional[Any]:






         """simple docstring"""
         if min > max:
             raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""     )

         super().__init__()
         a__  			=							min
         a__  			=							max






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return torch.clip(gelu(A     )    ,					self.min    ,					self.max     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple     )      ->      List[Any]:






         """simple docstring"""
         super().__init__()
         a__  			=							math.sqrt(2 / math.pi     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.silu(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]     )      ->      List[str]:






         """simple docstring"""
         super().__init__()
         if version.parse(torch.__version__     ) < version.parse("""1.9.0"""     ):
             a__  			=							self._mish_python
         else:
             a__  			=							nn.functional.mish

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.tanh(nn.functional.softplus(A     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							str    ,					A			:							Dict=0.7_0_7_1_0_7    ,					A			:							Union[str, Any]=0.2_8_2_0_9_5     )      ->      int:






         """simple docstring"""
         a__  			=							(input - mu).div(sigma * math.sqrt(2.0     )     )
         return 0.5 * (1.0 + torch.erf(A     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							nn.functional.relu(A     )
         a__  			=							torch.square(A     )
         return squared







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def __getitem__(   self			:							List[str]    ,					A			:							int     )      ->      Any:






         """simple docstring"""
         a__  			=							super().__getitem__(A     )
         a__			,		a__  			=							content if isinstance(A    ,					A     ) else (content, {})
         return cls(**A     )






SCREAMING_SNAKE_CASE_    :       List[Any]       =							{
    '''gelu''': GELUActivation,
    '''gelu_10''': (ClippedGELUActivation, {'''min''': -10, '''max''': 10}),
    '''gelu_fast''': FastGELUActivation,
    '''gelu_new''': NewGELUActivation,
    '''gelu_python''': (GELUActivation, {'''use_gelu_python''': True}),
    '''gelu_pytorch_tanh''': PytorchGELUTanh,
    '''gelu_accurate''': AccurateGELUActivation,
    '''laplace''': LaplaceActivation,
    '''linear''': LinearActivation,
    '''mish''': MishActivation,
    '''quick_gelu''': QuickGELUActivation,
    '''relu''': nn.ReLU,
    '''relu2''': ReLUSquaredActivation,
    '''relu6''': nn.ReLUa,
    '''sigmoid''': nn.Sigmoid,
    '''silu''': SiLUActivation,
    '''swish''': SiLUActivation,
    '''tanh''': nn.Tanh,
}
SCREAMING_SNAKE_CASE_    :       Any       =							ClassInstantier(ACTaCLS)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if activation_string in ACTaFN:
        return ACTaFN[activation_string]
    else:
        raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}"""		)


# For backwards compatibility with: from activations import gelu_python
SCREAMING_SNAKE_CASE_    :       Optional[int]       =							get_activation('''gelu_python''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''gelu_new''')
SCREAMING_SNAKE_CASE_    :       str       =							get_activation('''gelu''')
SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							get_activation('''gelu_fast''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''quick_gelu''')
SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''silu''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''mish''')







SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''linear''')






