"""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__    (	lowercase_     :	List[Any]      , lowercase_     :	int = 512						)  ->							List[Any]:
   __lowerCamelCase    		=					x.shape[1] if len(x.shape						) > 1 else 1
   __lowerCamelCase    		=					1 + np.arange(0      , feature_num * offset      , lowercase_      , dtype=np.intaa						)
   __lowerCamelCase    		=					x + feature_offset
   return x



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]=True						)  ->							str:
   requires_backends(lowercase_      , ['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(lowercase_						) + 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(lowercase_						) + 1

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

   __lowerCamelCase		,__lowerCamelCase    		=					algos_graphormer.floyd_warshall(lowercase_						)
   __lowerCamelCase    		=					np.amax(lowercase_						)

   __lowerCamelCase    		=					algos_graphormer.gen_edge_input(lowercase_      , lowercase_      , lowercase_						)
   __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(lowercase_      , 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      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE=20 ,				_SCREAMING_SNAKE_CASE=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 ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if self.on_the_fly_processing:
         __lowerCamelCase    		=					[preprocess_item(_SCREAMING_SNAKE_CASE	) for i in features]

      if not isinstance(features[0] ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					[vars(_SCREAMING_SNAKE_CASE	) 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(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				max_node_num + 1 ,				max_node_num + 1 ,				dtype=torch.float	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					torch.zeros(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				dtype=torch.long	)
      __lowerCamelCase    		=					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    		=					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(_SCREAMING_SNAKE_CASE	) == 1:  # one task
         if isinstance(sample[0] ,				_SCREAMING_SNAKE_CASE	):  # 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








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 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

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


						def    A__       (  self	)  ->str:
									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 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(A  , 'do_resize'	)	)
									self.assertTrue(hasattr(A  , 'size'	)	)
									self.assertTrue(hasattr(A  , 'do_center_crop'	)	)
									self.assertTrue(hasattr(A  , 'center_crop'	)	)
									self.assertTrue(hasattr(A  , 'do_normalize'	)	)
									self.assertTrue(hasattr(A  , 'image_mean'	)	)
									self.assertTrue(hasattr(A  , 'image_std'	)	)


						def    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    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}	)

									UpperCAmelCase__		:List[Any] 		=    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    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , Image.Image	)

									# Test not batched input
									UpperCAmelCase__		:int 		=    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
									UpperCAmelCase__		:List[str] 		=    image_processing(A  , 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    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A  , numpify=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , np.ndarray	)

									# Test not batched input
									UpperCAmelCase__		:List[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
									UpperCAmelCase__		:Optional[Any] 		=    image_processing(A  , 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    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A  , torchify=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , torch.Tensor	)

									# Test not batched input
									UpperCAmelCase__		:List[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
									UpperCAmelCase__		:Optional[int] 		=    image_processing(A  , 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'],
									    )  , )





