"""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__							:      Any		,      UpperCAmelCase__							:      int = 5_1_2      )->						List[Any]:
						UpperCAmelCase__       						=		x.shape[1] if len(x.shape      ) > 1 else 1
						UpperCAmelCase__       						=		1 + np.arange(0		,      feature_num * offset		,      UpperCAmelCase__		,      dtype=np.intaa      )
						UpperCAmelCase__       						=		x + feature_offset
						return x


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      int=True      )->						str:
						requires_backends(UpperCAmelCase__		,      ["""cython"""]      )

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

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

						UpperCAmelCase__       						=		convert_to_single_emb(UpperCAmelCase__      ) + 1
						UpperCAmelCase__       						=		item["""num_nodes"""]

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

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

						UpperCAmelCase__						,   UpperCAmelCase__       						=		algos_graphormer.floyd_warshall(UpperCAmelCase__      )
						UpperCAmelCase__       						=		np.amax(UpperCAmelCase__      )

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

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

						return item







class       _lowercase		:

				def __init__(  self							:     List[str]							,     _lowercase							:     Any=2_0							,     _lowercase							:     Union[str, Any]=False  ):


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

										UpperCAmelCase__       						=		spatial_pos_max
										UpperCAmelCase__       						=		on_the_fly_processing

				def __call__(  self							:     int							,     _lowercase							:     List[dict]  ):


										'''simple docstring'''
										if self.on_the_fly_processing:
																UpperCAmelCase__       						=		[preprocess_item(_lowercase  ) for i in features]

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

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

										UpperCAmelCase__       						=		torch.zeros(_lowercase							,     max_node_num + 1							,     max_node_num + 1							,     dtype=torch.float  )
										UpperCAmelCase__       						=		torch.zeros(_lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     dtype=torch.long  )
										UpperCAmelCase__       						=		torch.zeros(_lowercase							,     _lowercase							,     _lowercase							,     dtype=torch.long  )
										UpperCAmelCase__       						=		torch.zeros(_lowercase							,     _lowercase							,     dtype=torch.long  )
										UpperCAmelCase__       						=		torch.zeros(_lowercase							,     _lowercase							,     _lowercase							,     dtype=torch.long  )
										UpperCAmelCase__       						=		torch.zeros(
										    _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     dtype=torch.long  )

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

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

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

										UpperCAmelCase__       						=		batch["""in_degree"""]

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

										return batch


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)


if is_vision_available():
						import PIL


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['pixel_values']

				def __init__(  self							:     str							,     _lowercase							:     bool = True							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = PILImageResampling.BILINEAR							,     _lowercase							:     bool = True							,     _lowercase							:     bool = False							,     _lowercase							:     bool = True							,     _lowercase							:     bool = True							,     _lowercase							:     Union[int, float] = 1 / 2_5_5							,     _lowercase							:     bool = True							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     **_lowercase							:     List[str]							,     ):


										'''simple docstring'''
										super().__init__(**_lowercase  )

										UpperCAmelCase__       						=		size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
										if isinstance(_lowercase							,     (tuple, list)  ):
																# The previous feature extractor size parameter was in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )

										UpperCAmelCase__       						=		do_resize
										UpperCAmelCase__       						=		size
										UpperCAmelCase__       						=		resample
										UpperCAmelCase__       						=		do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis
										UpperCAmelCase__       						=		do_pad
										UpperCAmelCase__       						=		do_rescale
										UpperCAmelCase__       						=		rescale_factor
										UpperCAmelCase__       						=		do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase__       						=		image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							A			(  self							:     str							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																UpperCAmelCase__       						=		np.rotaa(_lowercase							,     3  )

										if data_format is not None:
																UpperCAmelCase__       						=		to_channel_dimension_format(_lowercase							,     _lowercase  )

										return image

				def 							A			(  self							:     Tuple							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )

										UpperCAmelCase__       						=		output_width - input_width
										UpperCAmelCase__       						=		output_height - input_height

										if random_padding:
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_height + 1  )
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_width + 1  )
										else:
																UpperCAmelCase__       						=		delta_height // 2
																UpperCAmelCase__       						=		delta_width // 2

										UpperCAmelCase__       						=		delta_height - pad_top
										UpperCAmelCase__       						=		delta_width - pad_left

										UpperCAmelCase__       						=		((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(_lowercase							,     _lowercase							,     data_format=_lowercase  )

				def 							A			(  self							:     Union[str, Any]							,     *_lowercase							:     List[str]							,     **_lowercase							:     int  ):


										'''simple docstring'''
										logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""  )
										return self.pad_image(*_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										# We always resize to the smallest of either the input or output size.
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																UpperCAmelCase__       						=		int(input_width * height / input_height  )
										elif input_width > input_height:
																UpperCAmelCase__       						=		int(input_height * width / input_width  )

										return resize(
										    _lowercase							,     size=(height, width)							,     resample=_lowercase							,     reducing_gap=2.0							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Optional[Any]							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		min(size["""height"""]							,     size["""width"""]  )
										UpperCAmelCase__       						=		get_resize_output_image_size(_lowercase							,     size=_lowercase							,     default_to_square=_lowercase  )
										UpperCAmelCase__       						=		resize(_lowercase							,     size=_lowercase							,     resample=_lowercase							,     data_format=_lowercase							,     **_lowercase  )
										return resized_image

				def 							A			(  self							:     int							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[int, float]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Union[str, Any]							,     ):


										'''simple docstring'''
										return rescale(_lowercase							,     scale=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Tuple							,     ):


										'''simple docstring'''
										return normalize(_lowercase							,     mean=_lowercase							,     std=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     ImageInput							,     _lowercase							:     bool = None							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = False							,     _lowercase							:     bool = None							,     _lowercase							:     float = None							,     _lowercase							:     bool = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[str, TensorType]] = None							,     _lowercase							:     Optional[ChannelDimension] = ChannelDimension.FIRST							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__       						=		size if size is not None else self.size
										if isinstance(_lowercase							,     (tuple, list)  ):
																# Previous feature extractor had size in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		resample if resample is not None else self.resample
										UpperCAmelCase__       						=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										UpperCAmelCase__       						=		do_pad if do_pad is not None else self.do_pad
										UpperCAmelCase__       						=		do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__       						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__       						=		do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__       						=		image_std if image_std is not None else self.image_std

										UpperCAmelCase__       						=		make_list_of_images(_lowercase  )

										if not valid_images(_lowercase  ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

										if do_resize and size is None:
																raise ValueError("""Size must be specified if do_resize is True."""  )

										if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

										if do_pad and size is None:
																raise ValueError("""Size must be specified if do_pad is True."""  )

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

										# All transformations expect numpy arrays.
										UpperCAmelCase__       						=		[to_numpy_array(_lowercase  ) for image in images]

										if do_align_long_axis:
																UpperCAmelCase__       						=		[self.align_long_axis(_lowercase							,     size=_lowercase  ) for image in images]

										if do_resize:
																UpperCAmelCase__       						=		[self.resize(image=_lowercase							,     size=_lowercase							,     resample=_lowercase  ) for image in images]

										if do_thumbnail:
																UpperCAmelCase__       						=		[self.thumbnail(image=_lowercase							,     size=_lowercase  ) for image in images]

										if do_pad:
																UpperCAmelCase__       						=		[self.pad_image(image=_lowercase							,     size=_lowercase							,     random_padding=_lowercase  ) for image in images]

										if do_rescale:
																UpperCAmelCase__       						=		[self.rescale(image=_lowercase							,     scale=_lowercase  ) for image in images]

										if do_normalize:
																UpperCAmelCase__       						=		[self.normalize(image=_lowercase							,     mean=_lowercase							,     std=_lowercase  ) for image in images]

										UpperCAmelCase__       						=		[to_channel_dimension_format(_lowercase							,     _lowercase  ) for image in images]

										UpperCAmelCase__       						=		{"""pixel_values""": images}
										return BatchFeature(data=_lowercase							,     tensor_type=_lowercase  )

