





"""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     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE = 512    ):
						UpperCamelCase							:		Any							      = x.shape[1] if len(x.shape    ) > 1 else 1
						UpperCamelCase							:		Tuple							      = 1 + np.arange(0       ,    feature_num * offset       ,    lowercase_       ,    dtype=np.intaa    )
						UpperCamelCase							:		Dict							      = x + feature_offset
						return x

def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=True    ):
						requires_backends(lowercase_       ,    ["""cython"""]    )

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

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

						UpperCamelCase							:		Union[str, Any]							      = convert_to_single_emb(lowercase_    ) + 1
						UpperCamelCase							:		List[Any]							      = item['num_nodes']

						if len(edge_attr.shape    ) == 1:
												UpperCamelCase							:		Optional[int]							      = edge_attr[:, None]
						UpperCamelCase							:		str							      = np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]       ,    dtype=np.intaa    )
						UpperCamelCase							:		int							      = convert_to_single_emb(lowercase_    ) + 1

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

						UpperCamelCase							:		Tuple							      = algos_graphormer.floyd_warshall(lowercase_    )
						UpperCamelCase							:		str							      = np.amax(lowercase_    )

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

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

						return item






class        lowercase__ :

			"""simple docstring"""







			def __init__(			self	,      _A=2_0	,      _A=False						):






									'''simple docstring'''







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

									UpperCamelCase							:		Optional[Any]							      = spatial_pos_max
									UpperCamelCase							:		Any							      = on_the_fly_processing







			def __call__(			self	,      _A						):






									'''simple docstring'''







									if self.on_the_fly_processing:
															UpperCamelCase							:		Dict							      = [preprocess_item(_A						) for i in features]

									if not isinstance(features[0]	,      _A						):
															UpperCamelCase							:		Dict							      = [vars(_A						) for f in features]
									UpperCamelCase							:		Dict							      = {}

									UpperCamelCase							:		Optional[Any]							      = max(len(i["""input_nodes"""]						) for i in features						)
									UpperCamelCase							:		List[str]							      = len(features[0]["""input_nodes"""][0]						)
									UpperCamelCase							:		Tuple							      = len(features[0]["""attn_edge_type"""][0][0]						)
									UpperCamelCase							:		str							      = max(len(i["""input_edges"""][0][0]						) for i in features						)
									UpperCamelCase							:		Any							      = len(features[0]["""input_edges"""][0][0][0]						)
									UpperCamelCase							:		Union[str, Any]							      = len(_A						)

									UpperCamelCase							:		List[Any]							      = torch.zeros(_A	,      max_node_num + 1	,      max_node_num + 1	,      dtype=torch.float						)
									UpperCamelCase							:		List[str]							      = torch.zeros(_A	,      _A	,      _A	,      _A	,      dtype=torch.long						)
									UpperCamelCase							:		Optional[Any]							      = torch.zeros(_A	,      _A	,      _A	,      dtype=torch.long						)
									UpperCamelCase							:		Union[str, Any]							      = torch.zeros(_A	,      _A	,      dtype=torch.long						)
									UpperCamelCase							:		Tuple							      = torch.zeros(_A	,      _A	,      _A	,      dtype=torch.long						)
									UpperCamelCase							:		Optional[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"]:
																					UpperCamelCase							:		int							      = torch.tensor(f[k]						)

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

															UpperCamelCase							:		List[Any]							      = f['attn_bias']
															UpperCamelCase							:		Optional[int]							      = f[
															    'attn_edge_type'
															]
															UpperCamelCase							:		Union[str, Any]							      = f['spatial_pos']
															UpperCamelCase							:		List[str]							      = f['in_degree']
															UpperCamelCase							:		List[Any]							      = f['input_nodes']
															UpperCamelCase							:		List[str]							      = f['input_edges']

									UpperCamelCase							:		List[str]							      = batch['in_degree']

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

									return batch










"""simple docstring"""


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


__magic_name__					:				Any	= logging.get_logger(__name__)

__magic_name__					:				Dict	= {
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     """yoso"""







			def __init__(			self	,      _A=5_0_2_6_5	,      _A=7_6_8	,      _A=1_2	,      _A=1_2	,      _A=3_0_7_2	,      _A="gelu"	,      _A=0.1	,      _A=0.1	,      _A=4_0_9_6	,      _A=1	,      _A=0.02	,      _A=1e-1_2	,      _A="absolute"	,      _A=True	,      _A=9	,      _A=6_4	,      _A=None	,      _A=True	,      _A=True	,      _A=1	,      _A=0	,      _A=2	,      **_A	,      ):






									'''simple docstring'''







									super().__init__(pad_token_id=_A	,      bos_token_id=_A	,      eos_token_id=_A	,      **_A						)

									UpperCamelCase							:		Any							      = vocab_size
									UpperCamelCase							:		Any							      = max_position_embeddings
									UpperCamelCase							:		int							      = hidden_size
									UpperCamelCase							:		List[Any]							      = num_hidden_layers
									UpperCamelCase							:		Any							      = num_attention_heads
									UpperCamelCase							:		Optional[Any]							      = intermediate_size
									UpperCamelCase							:		Optional[int]							      = hidden_act
									UpperCamelCase							:		List[Any]							      = hidden_dropout_prob
									UpperCamelCase							:		int							      = attention_probs_dropout_prob
									UpperCamelCase							:		Any							      = initializer_range
									UpperCamelCase							:		Optional[Any]							      = type_vocab_size
									UpperCamelCase							:		Any							      = layer_norm_eps
									UpperCamelCase							:		List[Any]							      = position_embedding_type
									UpperCamelCase							:		Optional[Any]							      = use_expectation
									UpperCamelCase							:		Any							      = hash_code_len
									UpperCamelCase							:		Optional[int]							      = num_hash
									UpperCamelCase							:		List[Any]							      = conv_window
									UpperCamelCase							:		Tuple							      = use_fast_hash
									UpperCamelCase							:		Dict							      = lsh_backward



