

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        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							int = 512		)				->Any:



							"""simple docstring"""
							lowerCamelCase_								=x.shape[1] if len(x.shape		) > 1 else 1
							lowerCamelCase_								=1 + np.arange(0      ,  feature_num * offset      ,  _A      ,  dtype=np.intaa		)
							lowerCamelCase_								=x + feature_offset
							return x




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							str=True		)				->List[Any]:



							"""simple docstring"""
							requires_backends(_A      ,  ["""cython"""]		)

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

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

							lowerCamelCase_								=convert_to_single_emb(_A		) + 1
							lowerCamelCase_								=item["""num_nodes"""]

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

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

							lowerCamelCase_	,   lowerCamelCase_								=algos_graphormer.floyd_warshall(_A		)
							lowerCamelCase_								=np.amax(_A		)

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

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

							return item






class 			_SCREAMING_SNAKE_CASE      :







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=20     ,						_SCREAMING_SNAKE_CASE=False							)->							Dict:
													if not is_cython_available():
																				raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""							)

													lowerCamelCase_								=spatial_pos_max
													lowerCamelCase_								=on_the_fly_processing







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

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

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

													lowerCamelCase_								=torch.zeros(_SCREAMING_SNAKE_CASE     ,						max_node_num + 1     ,						max_node_num + 1     ,						dtype=torch.float							)
													lowerCamelCase_								=torch.zeros(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.long							)
													lowerCamelCase_								=torch.zeros(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.long							)
													lowerCamelCase_								=torch.zeros(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.long							)
													lowerCamelCase_								=torch.zeros(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.long							)
													lowerCamelCase_								=torch.zeros(
													    _SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.long							)

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

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

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

													lowerCamelCase_								=batch["""in_degree"""]

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

													return batch



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A	:   List[Any]	=					logging.get_logger(__name__)

__A	:   Tuple	=					{
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		"electra"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=3_0522     ,						_SCREAMING_SNAKE_CASE=128     ,						_SCREAMING_SNAKE_CASE=256     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=1024     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=512     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						_SCREAMING_SNAKE_CASE=1E-12     ,						_SCREAMING_SNAKE_CASE="first"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE="absolute"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[int]:
													super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=embedding_size
													lowerCamelCase_								=hidden_size
													lowerCamelCase_								=num_hidden_layers
													lowerCamelCase_								=num_attention_heads
													lowerCamelCase_								=intermediate_size
													lowerCamelCase_								=hidden_act
													lowerCamelCase_								=hidden_dropout_prob
													lowerCamelCase_								=attention_probs_dropout_prob
													lowerCamelCase_								=max_position_embeddings
													lowerCamelCase_								=type_vocab_size
													lowerCamelCase_								=initializer_range
													lowerCamelCase_								=layer_norm_eps

													lowerCamelCase_								=summary_type
													lowerCamelCase_								=summary_use_proj
													lowerCamelCase_								=summary_activation
													lowerCamelCase_								=summary_last_dropout
													lowerCamelCase_								=position_embedding_type
													lowerCamelCase_								=use_cache
													lowerCamelCase_								=classifier_dropout


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):







						@property
						def        _snake_case				(			self							)->							Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																				lowerCamelCase_								={0: """batch""", 1: """choice""", 2: """sequence"""}
													else:
																				lowerCamelCase_								={0: """batch""", 1: """sequence"""}
													return OrderedDict(
													    [
													        ("""input_ids""", dynamic_axis),
													        ("""attention_mask""", dynamic_axis),
													        ("""token_type_ids""", dynamic_axis),
													    ]							)
