
"""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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase = 512						):
	__lowerCAmelCase     :		Dict		     = x.shape[1] if len(x.shape						) > 1 else 1
	__lowerCAmelCase     :		int		     = 1 + np.arange(0			,    feature_num * offset			,    _UpperCamelCase			,    dtype=np.intaa						)
	__lowerCAmelCase     :		Dict		     = x + feature_offset
	return x
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase=True						):
	requires_backends(_UpperCamelCase			,    ['cython']						)

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

	__lowerCAmelCase     :		str		     = np.asarray(item['edge_index']			,    dtype=np.intaa						)

	__lowerCAmelCase     :		List[str]		     = convert_to_single_emb(_UpperCamelCase						) + 1
	__lowerCAmelCase     :		List[str]		     = item['num_nodes']

	if len(edge_attr.shape						) == 1:
		__lowerCAmelCase     :		str		     = edge_attr[:, None]
	__lowerCAmelCase     :		str		     = np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]			,    dtype=np.intaa						)
	__lowerCAmelCase     :		List[Any]		     = convert_to_single_emb(_UpperCamelCase						) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	__lowerCAmelCase     :		int		     = np.zeros([num_nodes, num_nodes]			,    dtype=_UpperCamelCase						)
	__lowerCAmelCase     :		List[str]		     = True

	__lowerCAmelCase     ,  __lowerCAmelCase     :		Union[str, Any]		     = algos_graphormer.floyd_warshall(_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = np.amax(_UpperCamelCase						)

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

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

	return item




class 					A__			:

		def __init__( self			,						_SCREAMING_SNAKE_CASE=20			,						_SCREAMING_SNAKE_CASE=False					):
			if not is_cython_available():
				raise ImportError('Graphormer preprocessing needs Cython (pyximport)'					)

			__lowerCAmelCase     :		str		     = spatial_pos_max
			__lowerCAmelCase     :		Tuple		     = on_the_fly_processing







		def __call__( self			,						_SCREAMING_SNAKE_CASE					):
			if self.on_the_fly_processing:
				__lowerCAmelCase     :		List[str]		     = [preprocess_item(_SCREAMING_SNAKE_CASE					) for i in features]

			if not isinstance(features[0]			,						_SCREAMING_SNAKE_CASE					):
				__lowerCAmelCase     :		int		     = [vars(_SCREAMING_SNAKE_CASE					) for f in features]
			__lowerCAmelCase     :		str		     = {}

			__lowerCAmelCase     :		str		     = max(len(i['input_nodes']					) for i in features					)
			__lowerCAmelCase     :		Tuple		     = len(features[0]['input_nodes'][0]					)
			__lowerCAmelCase     :		str		     = len(features[0]['attn_edge_type'][0][0]					)
			__lowerCAmelCase     :		Dict		     = max(len(i['input_edges'][0][0]					) for i in features					)
			__lowerCAmelCase     :		Union[str, Any]		     = len(features[0]['input_edges'][0][0][0]					)
			__lowerCAmelCase     :		List[str]		     = len(_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		Dict		     = torch.zeros(_SCREAMING_SNAKE_CASE			,						max_node_num + 1			,						max_node_num + 1			,						dtype=torch.float					)
			__lowerCAmelCase     :		Dict		     = torch.zeros(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						dtype=torch.long					)
			__lowerCAmelCase     :		Dict		     = torch.zeros(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						dtype=torch.long					)
			__lowerCAmelCase     :		Any		     = torch.zeros(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						dtype=torch.long					)
			__lowerCAmelCase     :		Union[str, Any]		     = torch.zeros(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						dtype=torch.long					)
			__lowerCAmelCase     :		Union[str, Any]		     = torch.zeros(
			    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						dtype=torch.long					)

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

				if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]					) > 0:
					__lowerCAmelCase     :		List[Any]		     = float('-inf'					)

				__lowerCAmelCase     :		Optional[Any]		     = f['attn_bias']
				__lowerCAmelCase     :		Optional[int]		     = f[
				    'attn_edge_type'
				]
				__lowerCAmelCase     :		List[Any]		     = f['spatial_pos']
				__lowerCAmelCase     :		Optional[int]		     = f['in_degree']
				__lowerCAmelCase     :		int		     = f['input_nodes']
				__lowerCAmelCase     :		Optional[int]		     = f['input_edges']

			__lowerCAmelCase     :		Dict		     = batch['in_degree']

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

			return batch

"""simple docstring"""







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 DeiTImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						):
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 20, 'width': 20}
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}

			__lowerCAmelCase     :		Any		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Any		     = num_channels
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Tuple		     = min_resolution
			__lowerCAmelCase     :		Union[str, Any]		     = max_resolution
			__lowerCAmelCase     :		Optional[int]		     = do_resize
			__lowerCAmelCase     :		Any		     = size
			__lowerCAmelCase     :		Union[str, Any]		     = do_center_crop
			__lowerCAmelCase     :		List[Any]		     = crop_size
			__lowerCAmelCase     :		Optional[int]		     = do_normalize
			__lowerCAmelCase     :		int		     = image_mean
			__lowerCAmelCase     :		Tuple		     = image_std







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			List[str]							  =      DeiTImageProcessor if is_vision_available() else None
		A_		:			int							  =      True

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = DeiTImageProcessingTester(self					)

		@property
		def 				__lowerCamelCase				( self					):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_normalize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_mean'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_std'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'height': 20, 'width': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		str		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'height': 42, 'width': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Dict		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						Image.Image					)

			# Test not batched input
			__lowerCAmelCase     :		Dict		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		int		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						numpify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						np.ndarray					)

			# Test not batched input
			__lowerCAmelCase     :		str		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Dict		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)







		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Union[str, Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						torchify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						torch.Tensor					)

			# Test not batched input
			__lowerCAmelCase     :		Union[str, Any]		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Optional[int]		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)