
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__    (  lowerCamelCase   ,			lowerCamelCase = 5_12  )     ->		Any:
				UpperCamelCase_:							str		=  x.shape[1] if len(x.shape  ) > 1 else 1
				UpperCamelCase_:							int		=  1 + np.arange(0   ,			feature_num * offset   ,			lowerCamelCase   ,			dtype=np.intaa  )
				UpperCamelCase_:							Union[str, Any]		=  x + feature_offset
				return x







def 				A__    (  lowerCamelCase   ,			lowerCamelCase=True  )     ->		Dict:
				requires_backends(lowerCamelCase   ,			["""cython"""]  )

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

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

				UpperCamelCase_:							Dict		=  convert_to_single_emb(lowerCamelCase  ) + 1
				UpperCamelCase_:							Optional[int]		=  item["""num_nodes"""]

				if len(edge_attr.shape  ) == 1:
								UpperCamelCase_:							Optional[Any]		=  edge_attr[:, None]
				UpperCamelCase_:							Any		=  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]   ,			dtype=np.intaa  )
				UpperCamelCase_:							Tuple		=  convert_to_single_emb(lowerCamelCase  ) + 1

				# node adj matrix [num_nodes, num_nodes] bool
				UpperCamelCase_:							Dict		=  np.zeros([num_nodes, num_nodes]   ,			dtype=lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  True

				UpperCamelCase_,      UpperCamelCase_:							Dict		=  algos_graphormer.floyd_warshall(lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  np.amax(lowerCamelCase  )

				UpperCamelCase_:							Tuple		=  algos_graphormer.gen_edge_input(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
				UpperCamelCase_:							str		=  np.zeros([num_nodes + 1, num_nodes + 1]   ,			dtype=np.single  )  # with graph token

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

				return item





class 				_UpperCamelCase :






			'''simple docstring'''

			def __init__(				self       :				List[Any]  ,		snake_case_       :				int=20  ,		snake_case_       :				Optional[int]=False					):
							if not is_cython_available():
											raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""					)

							UpperCamelCase_:							List[Any]		=  spatial_pos_max
							UpperCamelCase_:							Any		=  on_the_fly_processing




			def __call__(				self       :				Optional[int]  ,		snake_case_       :				List[dict]					):
							if self.on_the_fly_processing:
											UpperCamelCase_:							Optional[Any]		=  [preprocess_item(snake_case_					) for i in features]

							if not isinstance(features[0]  ,		snake_case_					):
											UpperCamelCase_:							Any		=  [vars(snake_case_					) for f in features]
							UpperCamelCase_:							Dict		=  {}

							UpperCamelCase_:							Any		=  max(len(i["""input_nodes"""]					) for i in features					)
							UpperCamelCase_:							List[str]		=  len(features[0]["""input_nodes"""][0]					)
							UpperCamelCase_:							List[Any]		=  len(features[0]["""attn_edge_type"""][0][0]					)
							UpperCamelCase_:							Tuple		=  max(len(i["""input_edges"""][0][0]					) for i in features					)
							UpperCamelCase_:							Optional[Any]		=  len(features[0]["""input_edges"""][0][0][0]					)
							UpperCamelCase_:							str		=  len(snake_case_					)

							UpperCamelCase_:							Dict		=  torch.zeros(snake_case_  ,		max_node_num + 1  ,		max_node_num + 1  ,		dtype=torch.float					)
							UpperCamelCase_:							Any		=  torch.zeros(snake_case_  ,		snake_case_  ,		snake_case_  ,		snake_case_  ,		dtype=torch.long					)
							UpperCamelCase_:							Tuple		=  torch.zeros(snake_case_  ,		snake_case_  ,		snake_case_  ,		dtype=torch.long					)
							UpperCamelCase_:							Tuple		=  torch.zeros(snake_case_  ,		snake_case_  ,		dtype=torch.long					)
							UpperCamelCase_:							Optional[Any]		=  torch.zeros(snake_case_  ,		snake_case_  ,		snake_case_  ,		dtype=torch.long					)
							UpperCamelCase_:							List[Any]		=  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"]:
															UpperCamelCase_:							Tuple		=  torch.tensor(f[k]					)

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

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

							UpperCamelCase_:							List[Any]		=  batch["""in_degree"""]

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

							return batch












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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    ["pixel_values"]







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       PILImageResampling = PILImageResampling.BICUBIC       ,				snake_case    :       bool = True       ,				snake_case    :       Union[int, float] = 1 / 255       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				**snake_case    :       Union[str, Any]       ,				):
          super().__init__(**snake_case )
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCamelCase     						=  get_size_dict(snake_case )

          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  resample
          __UpperCamelCase     						=  do_rescale
          __UpperCamelCase     						=  rescale_factor
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCamelCase     						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  self.size if size is None else size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )

          __UpperCamelCase						,					__UpperCamelCase     						=  get_image_size(snake_case )
          __UpperCamelCase     						=  min(snake_case       ,				snake_case )
          __UpperCamelCase     						=  (size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCamelCase     						=  (size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(snake_case       ,				size=(cropped_height, cropped_width)       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       PILImageResampling = PIL.Image.BICUBIC       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[str]       ,				):
          __UpperCamelCase     						=  get_size_dict(snake_case )
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
          return resize(
              snake_case       ,				size=(size['''height'''], size['''width'''])       ,				resample=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Union[int, float]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Optional[int]       ,				):
          return rescale(snake_case       ,				scale=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Union[float, List[float]]       ,				snake_case    :       Union[float, List[float]]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Any       ,				):
          return normalize(snake_case       ,				mean=snake_case       ,				std=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       ImageInput       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       PILImageResampling = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[float] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[str, TensorType]] = None       ,				snake_case    :       ChannelDimension = ChannelDimension.FIRST       ,				**snake_case    :       Tuple       ,				):
          __UpperCamelCase     						=  do_center_crop if do_center_crop is not None else self.do_center_crop
          __UpperCamelCase     						=  crop_size if crop_size is not None else self.crop_size
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  do_resize if do_resize is not None else self.do_resize
          __UpperCamelCase     						=  size if size is not None else self.size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  resample if resample is not None else self.resample
          __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(snake_case )

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

          if do_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

          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_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.''' )

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

          if do_center_crop:
              __UpperCamelCase     						=  [self.center_crop(snake_case       ,				snake_case       ,				size=snake_case ) for image in images]

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

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

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

          __UpperCamelCase     						=  [to_channel_dimension_format(snake_case       ,				snake_case ) for image in images]

          __UpperCamelCase     						=  {'''pixel_values''': images}
          return BatchFeature(data=snake_case       ,				tensor_type=snake_case )






