





"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:	int							      =logging.get_logger(__name__)

lowerCAmelCase:	Optional[int]							      ={
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}
class lowerCamelCase__  (    __UpperCamelCase				):
			__UpperCAmelCase        =					"""prophetnet"""
			__UpperCAmelCase        =					["""past_key_values"""]
			__UpperCAmelCase        =					{
			    """num_attention_heads""": """num_encoder_attention_heads""",
			}



			def __init__(       self      ,   snake_case = 0.1      ,   snake_case = "gelu"      ,   snake_case = 3_0_5_2_2      ,   snake_case = 1_0_2_4      ,   snake_case = 4_0_9_6      ,   snake_case = 1_2      ,   snake_case = 1_6      ,   snake_case = 4_0_9_6      ,   snake_case = 1_2      ,   snake_case = 1_6      ,   snake_case = 0.1      ,   snake_case = 0.1      ,   snake_case = 5_1_2      ,   snake_case = 0.02      ,   snake_case = True      ,   snake_case = True      ,   snake_case = 0      ,   snake_case = 2      ,   snake_case = 3_2      ,   snake_case = 1_2_8      ,   snake_case = False      ,   snake_case = 0.0      ,   snake_case = True      ,   snake_case = 0      ,   snake_case = 1      ,   snake_case = 2      ,   **snake_case      ,   ) ->    int:







						"""simple docstring"""
						lowercase    :			Tuple  =       vocab_size
						lowercase    :			Tuple  =       hidden_size
						lowercase    :			Union[str, Any]  =       encoder_ffn_dim
						lowercase    :			Tuple  =       num_encoder_layers
						lowercase    :			Optional[Any]  =       num_encoder_attention_heads
						lowercase    :			Tuple  =       decoder_ffn_dim
						lowercase    :			str  =       num_decoder_layers
						lowercase    :			Optional[Any]  =       num_decoder_attention_heads
						lowercase    :			Optional[int]  =       max_position_embeddings
						lowercase    :			List[str]  =       init_std  # Normal(0, this parameter)
						lowercase    :			Any  =       activation_function

						# parameters for prophetnet
						lowercase    :			Union[str, Any]  =       ngram
						lowercase    :			Union[str, Any]  =       num_buckets
						lowercase    :			int  =       relative_max_distance
						lowercase    :			Tuple  =       disable_ngram_loss
						lowercase    :			List[Any]  =       eps

						# 3 Types of Dropout
						lowercase    :			Dict  =       attention_dropout
						lowercase    :			Any  =       activation_dropout
						lowercase    :			Tuple  =       dropout

						lowercase    :			Tuple  =       use_cache

						super().__init__(
						    pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   is_encoder_decoder=snake_case      ,   add_cross_attention=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )



			@property
			def    _UpperCAmelCase	(       self	) ->    int:







						"""simple docstring"""
						return self.num_encoder_layers + self.num_decoder_layers





			@num_hidden_layers.setter
			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Tuple:







						"""simple docstring"""
						raise NotImplementedError(
						    """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
						    """ `num_decoder_layers`."""	)









"""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						(      __A ,__A = 512    )		->				Optional[Any]:
   lowercase    :			Optional[Any]  =       x.shape[1] if len(x.shape    ) > 1 else 1
   lowercase    :			Tuple  =       1 + np.arange(0 ,feature_num * offset ,__A ,dtype=np.intaa    )
   lowercase    :			Any  =       x + feature_offset
   return x




def 			__snake_case						(      __A ,__A=True    )		->				List[Any]:
   requires_backends(__A ,["""cython"""]    )

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

   lowercase    :			str  =       np.asarray(item["""edge_index"""] ,dtype=np.intaa    )

   lowercase    :			Tuple  =       convert_to_single_emb(__A    ) + 1
   lowercase    :			Dict  =       item["""num_nodes"""]

   if len(edge_attr.shape    ) == 1:
      lowercase    :			Dict  =       edge_attr[:, None]
   lowercase    :			Tuple  =       np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,dtype=np.intaa    )
   lowercase    :			List[Any]  =       convert_to_single_emb(__A    ) + 1

   # node adj matrix [num_nodes, num_nodes] bool
   lowercase    :			List[Any]  =       np.zeros([num_nodes, num_nodes] ,dtype=__A    )
   lowercase    :			str  =       True

   lowercase  ,					lowercase    :			List[str]  =       algos_graphormer.floyd_warshall(__A    )
   lowercase    :			str  =       np.amax(__A    )

   lowercase    :			int  =       algos_graphormer.gen_edge_input(__A ,__A ,__A    )
   lowercase    :			Optional[int]  =       np.zeros([num_nodes + 1, num_nodes + 1] ,dtype=np.single    )  # with graph token

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

   return item







class lowerCamelCase__  :



   def __init__(       self      ,   snake_case=2_0      ,   snake_case=False	) ->    List[Any]:







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

      lowercase    :			Any  =       spatial_pos_max
      lowercase    :			Dict  =       on_the_fly_processing





   def __call__(       self      ,   snake_case	) ->    Dict[str, Any]:







      """simple docstring"""
      if self.on_the_fly_processing:
         lowercase    :			Dict  =       [preprocess_item(snake_case	) for i in features]

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

      lowercase    :			Any  =       max(len(i["""input_nodes"""]	) for i in features	)
      lowercase    :			List[str]  =       len(features[0]["""input_nodes"""][0]	)
      lowercase    :			Tuple  =       len(features[0]["""attn_edge_type"""][0][0]	)
      lowercase    :			str  =       max(len(i["""input_edges"""][0][0]	) for i in features	)
      lowercase    :			Dict  =       len(features[0]["""input_edges"""][0][0][0]	)
      lowercase    :			Union[str, Any]  =       len(snake_case	)

      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   max_node_num + 1      ,   max_node_num + 1      ,   dtype=torch.float	)
      lowercase    :			List[Any]  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			int  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Tuple  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Dict  =       torch.zeros(
          snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)

      for ix, f in enumerate(snake_case	):
         for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
            lowercase    :			Optional[int]  =       torch.tensor(f[k]	)

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

         lowercase    :			str  =       f["""attn_bias"""]
         lowercase    :			Union[str, Any]  =       f[
             """attn_edge_type"""
         ]
         lowercase    :			Union[str, Any]  =       f["""spatial_pos"""]
         lowercase    :			int  =       f["""in_degree"""]
         lowercase    :			int  =       f["""input_nodes"""]
         lowercase    :			Any  =       f["""input_edges"""]

      lowercase    :			Dict  =       batch["""in_degree"""]

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

      return batch


