





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ViltImageProcessor







class 	_UpperCamelCase	(							unittest.TestCase	):







					'''simple docstring'''

					def __init__(    self     :	Optional[int]			,       __lowercase     :	Union[str, Any]			,       __lowercase     :	Dict=7			,       __lowercase     :	Tuple=3			,       __lowercase     :	List[Any]=18			,       __lowercase     :	str=30			,       __lowercase     :	int=4_00			,       __lowercase     :	List[str]=True			,       __lowercase     :	Any=None			,       __lowercase     :	Dict=2			,       __lowercase     :	Optional[int]=True			,       __lowercase     :	Dict=[0.5, 0.5, 0.5]			,       __lowercase     :	List[str]=[0.5, 0.5, 0.5]			,       ):






						'''simple docstring'''







						UpperCAmelCase_		    =  size if size is not None else {"""shortest_edge""": 30}
						UpperCAmelCase_		    =  parent
						UpperCAmelCase_		    =  batch_size
						UpperCAmelCase_		    =  num_channels
						UpperCAmelCase_		    =  image_size
						UpperCAmelCase_		    =  min_resolution
						UpperCAmelCase_		    =  max_resolution
						UpperCAmelCase_		    =  do_resize
						UpperCAmelCase_		    =  size
						UpperCAmelCase_		    =  size_divisor
						UpperCAmelCase_		    =  do_normalize
						UpperCAmelCase_		    =  image_mean
						UpperCAmelCase_		    =  image_std





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]		):






						'''simple docstring'''







						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	Union[str, Any]			,       __lowercase     :	Tuple=False		):






						'''simple docstring'''







						if not batched:
							UpperCAmelCase_		    =  self.size["""shortest_edge"""]
							UpperCAmelCase_		    =  image_inputs[0]
							if isinstance(__lowercase			,       Image.Image		):
								UpperCAmelCase_    ,						UpperCAmelCase_		    =  image.size
							else:
								UpperCAmelCase_    ,						UpperCAmelCase_		    =  image.shape[1], image.shape[2]
							UpperCAmelCase_		    =  size / min(__lowercase			,       __lowercase		)
							if h < w:
								UpperCAmelCase_    ,						UpperCAmelCase_		    =  size, scale * w
							else:
								UpperCAmelCase_    ,						UpperCAmelCase_		    =  scale * h, size

							UpperCAmelCase_		    =  int((13_33 / 8_00) * size		)
							if max(__lowercase			,       __lowercase		) > max_size:
								UpperCAmelCase_		    =  max_size / max(__lowercase			,       __lowercase		)
								UpperCAmelCase_		    =  newh * scale
								UpperCAmelCase_		    =  neww * scale

							UpperCAmelCase_    ,						UpperCAmelCase_		    =  int(newh + 0.5		), int(neww + 0.5		)
							UpperCAmelCase_    ,						UpperCAmelCase_		    =  (
							    newh // self.size_divisor * self.size_divisor,
							    neww // self.size_divisor * self.size_divisor,
							)

						else:
							UpperCAmelCase_		    =  []
							for image in image_inputs:
								UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							UpperCAmelCase_		    =  max(__lowercase			,       key=lambda __lowercase		: item[0]		)[0]
							UpperCAmelCase_		    =  max(__lowercase			,       key=lambda __lowercase		: item[1]		)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 	_UpperCamelCase	(							A_    ,							unittest.TestCase	):







					'''simple docstring'''

					lowerCamelCase  :  List[str]           =							ViltImageProcessor if is_vision_available() else None





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  ViltImageProcessingTester(self		)





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						return self.image_processor_tester.prepare_image_processor_dict()





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(__lowercase			,       """image_mean"""		)		)
						self.assertTrue(hasattr(__lowercase			,       """image_std"""		)		)
						self.assertTrue(hasattr(__lowercase			,       """do_normalize"""		)		)
						self.assertTrue(hasattr(__lowercase			,       """do_resize"""		)		)
						self.assertTrue(hasattr(__lowercase			,       """size"""		)		)
						self.assertTrue(hasattr(__lowercase			,       """size_divisor"""		)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size			,       {"""shortest_edge""": 30}		)

						UpperCAmelCase_		    =  self.image_processing_class.from_dict(self.image_processor_dict			,       size=42		)
						self.assertEqual(image_processor.size			,       {"""shortest_edge""": 42}		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						pass





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						UpperCAmelCase_		    =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowercase		)
						for image in image_inputs:
							self.assertIsInstance(__lowercase			,       Image.Image		)

						# Test not batched input
						UpperCAmelCase_		    =  image_processing(image_inputs[0]			,       return_tensors="""pt"""		).pixel_values

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.image_processor_tester.get_expected_values(__lowercase		)
						self.assertEqual(
						    encoded_images.shape			,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,       )

						# Test batched
						UpperCAmelCase_		    =  image_processing(__lowercase			,       return_tensors="""pt"""		).pixel_values

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.image_processor_tester.get_expected_values(__lowercase			,       batched=__lowercase		)
						self.assertEqual(
						    encoded_images.shape			,       (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						UpperCAmelCase_		    =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowercase			,       numpify=__lowercase		)
						for image in image_inputs:
							self.assertIsInstance(__lowercase			,       np.ndarray		)

						# Test not batched input
						UpperCAmelCase_		    =  image_processing(image_inputs[0]			,       return_tensors="""pt"""		).pixel_values

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.image_processor_tester.get_expected_values(__lowercase		)
						self.assertEqual(
						    encoded_images.shape			,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,       )

						# Test batched
						UpperCAmelCase_		    =  image_processing(__lowercase			,       return_tensors="""pt"""		).pixel_values

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.image_processor_tester.get_expected_values(__lowercase			,       batched=__lowercase		)
						self.assertEqual(
						    encoded_images.shape			,       (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						UpperCAmelCase_		    =  prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowercase			,       torchify=__lowercase		)
						for image in image_inputs:
							self.assertIsInstance(__lowercase			,       torch.Tensor		)

						# Test not batched input
						UpperCAmelCase_		    =  image_processing(image_inputs[0]			,       return_tensors="""pt"""		).pixel_values

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.image_processor_tester.get_expected_values(__lowercase		)
						self.assertEqual(
						    encoded_images.shape			,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,       )

						# Test batched
						UpperCAmelCase_		    =  image_processing(__lowercase			,       return_tensors="""pt"""		).pixel_values

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.image_processor_tester.get_expected_values(__lowercase			,       batched=__lowercase		)
						self.assertEqual(
						    encoded_images.shape			,       (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,       )









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_(      A					,   A = 512 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""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(A ) + 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(A ) + 1

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

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

	UpperCAmelCase_		    =  algos_graphormer.gen_edge_input(A					,   A					,   A )
	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(A					,   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 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __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     :	List[str]			,       __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


