




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

a_      						=   logging.getLogger()




def     __SCREAMING_SNAKE_CASE      (     )				->		Any:

    """simple docstring"""


    __UpperCamelCase     						=  argparse.ArgumentParser()
    parser.add_argument('''-f'''					)
    __UpperCamelCase     						=  parser.parse_args()
    return args.f


class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  logging.StreamHandler(sys.stdout )
          logger.addHandler(snake_case )

          __UpperCamelCase     						=  self.get_auto_remove_tmp_dir()
          __UpperCamelCase     						=  F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

          with patch.object(snake_case       ,				'''argv'''       ,				snake_case ):
              __UpperCamelCase     						=  run_glue_with_pabee.main()
              for value in result.values():
                  self.assertGreaterEqual(snake_case       ,				0.75 )







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__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				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
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

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

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

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

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

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

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

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

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

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

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

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

												return batch




