
'''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   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str] = 5_12      )					->       List[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							x.shape[1] if len(x.shape      ) > 1 else 1
			lowerCAmelCase__								=							1 + np.arange(0						,	feature_num * offset						,	UpperCamelCase_						,	dtype=np.intaa      )
			lowerCAmelCase__								=							x + feature_offset
			return x







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     int=True      )					->       Union[str, Any]:







			'''simple docstring'''



			requires_backends(UpperCamelCase_						,	["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(UpperCamelCase_      ) + 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(UpperCamelCase_      ) + 1

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

			lowerCAmelCase__	,lowerCAmelCase__								=							algos_graphormer.floyd_warshall(UpperCamelCase_      )
			lowerCAmelCase__								=							np.amax(UpperCamelCase_      )

			lowerCAmelCase__								=							algos_graphormer.gen_edge_input(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )
			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(UpperCamelCase_						,	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    A     :





	def __init__(					self			:       List[Any]      ,							__magic_name__			:       Optional[Any]=20      ,							__magic_name__			:       str=False							):


				"""simple docstring"""

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

				lowerCAmelCase__								=							spatial_pos_max
				lowerCAmelCase__								=							on_the_fly_processing







	def __call__(					self			:       List[Any]      ,							__magic_name__			:       List[dict]							):


				"""simple docstring"""

				if self.on_the_fly_processing:
							lowerCAmelCase__								=							[preprocess_item(lowerCAmelCase_							) for i in features]

				if not isinstance(features[0]      ,							lowerCAmelCase_							):
							lowerCAmelCase__								=							[vars(lowerCAmelCase_							) 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(lowerCAmelCase_							)

				lowerCAmelCase__								=							torch.zeros(lowerCAmelCase_      ,							max_node_num + 1      ,							max_node_num + 1      ,							dtype=torch.float							)
				lowerCAmelCase__								=							torch.zeros(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							dtype=torch.long							)
				lowerCAmelCase__								=							torch.zeros(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							dtype=torch.long							)
				lowerCAmelCase__								=							torch.zeros(lowerCAmelCase_      ,							lowerCAmelCase_      ,							dtype=torch.long							)
				lowerCAmelCase__								=							torch.zeros(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							dtype=torch.long							)
				lowerCAmelCase__								=							torch.zeros(
				    lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							dtype=torch.long							)

				for ix, f in enumerate(lowerCAmelCase_							):
							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(lowerCAmelCase_							) == 1:  # one task
							if isinstance(sample[0]      ,							lowerCAmelCase_							):  # 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


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   : Any					=		{
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__   : str					=		["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
		from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
		from .tokenization_fsmt import FSMTTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
		import sys

		UpperCAmelCase__   : int					=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





