





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      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase = 512       ):
       _UpperCamelCase														=    x.shape[1] if len(x.shape       ) > 1 else 1
       _UpperCamelCase														=    1 + np.arange(0     ,							feature_num * offset     ,							SCREAMING_SNAKE_CASE__     ,							dtype=np.intaa       )
       _UpperCamelCase														=    x + feature_offset
       return x


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase=True       ):
       requires_backends(SCREAMING_SNAKE_CASE__     ,							['''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(SCREAMING_SNAKE_CASE__       ) + 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(SCREAMING_SNAKE_CASE__       ) + 1

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

       _UpperCamelCase							,		_UpperCamelCase														=    algos_graphormer.floyd_warshall(SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    np.amax(SCREAMING_SNAKE_CASE__       )

       _UpperCamelCase														=    algos_graphormer.gen_edge_input(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__       )
       _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(SCREAMING_SNAKE_CASE__     ,							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 			__A:

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





            """simple docstring"""

            if self.on_the_fly_processing:
                   _UpperCamelCase														=    [preprocess_item(A_   ) for i in features]

            if not isinstance(features[0],							A_   ):
                   _UpperCamelCase														=    [vars(A_   ) 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(A_   )

            _UpperCamelCase														=    torch.zeros(A_,							max_node_num + 1,							max_node_num + 1,							dtype=torch.float   )
            _UpperCamelCase														=    torch.zeros(A_,							A_,							A_,							A_,							dtype=torch.long   )
            _UpperCamelCase														=    torch.zeros(A_,							A_,							A_,							dtype=torch.long   )
            _UpperCamelCase														=    torch.zeros(A_,							A_,							dtype=torch.long   )
            _UpperCamelCase														=    torch.zeros(A_,							A_,							A_,							dtype=torch.long   )
            _UpperCamelCase														=    torch.zeros(
                A_,							A_,							A_,							A_,							A_,							dtype=torch.long   )

            for ix, f in enumerate(A_   ):
                   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(A_   ) == 1:  # one task
                   if isinstance(sample[0],							A_   ):  # 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










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 EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






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



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






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






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'height': 42, 'width': 42}       )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=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       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=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       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=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       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=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.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )





