



"""simple docstring"""
import argparse
import json
import re
import string
import sys
from collections import Counter
def        A_  (      _lowercase     ):







						'''simple docstring'''


						def remove_articles(_lowercase     ):
												return re.sub(r"""\b(a|an|the)\b""", """ """, _lowercase     )

						def white_space_fix(_lowercase     ):
												return " ".join(text.split()     )

						def remove_punc(_lowercase     ):
												snake_case_			:List[str]						=      set(string.punctuation     )
												return "".join(ch for ch in text if ch not in exclude     )

						def lower(_lowercase     ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowercase     )     )     )     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						snake_case_			:List[str]						=      normalize_answer(_lowercase     ).split()
						snake_case_			:Tuple						=      normalize_answer(_lowercase     ).split()
						snake_case_			:Any						=      Counter(_lowercase     ) & Counter(_lowercase     )
						snake_case_			:int						=      sum(common.values()     )
						if num_same == 0:
												return 0
						snake_case_			:Any						=      1.0 * num_same / len(_lowercase     )
						snake_case_			:List[Any]						=      1.0 * num_same / len(_lowercase     )
						snake_case_			:Optional[Any]						=      (2 * precision * recall) / (precision + recall)
						return fa
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						return normalize_answer(_lowercase     ) == normalize_answer(_lowercase     )
def        A_  (      _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


						snake_case_			:Union[str, Any]						=      []
						for ground_truth in ground_truths:
												snake_case_			:Dict						=      metric_fn(_lowercase, _lowercase     )
												scores_for_ground_truths.append(_lowercase     )
						return max(_lowercase     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						snake_case_			:Optional[int]						=      0
						snake_case_			:Any						=      []
						for passage in dataset:
												for qa in passage["qas"]:
																		total += 1
																		if qa["id"] not in predictions:
																								snake_case_			:Tuple						=      f"""Unanswered question {qa['id']} will receive score 0."""
																								print(_lowercase, file=sys.stderr     )
																								continue

																		snake_case_			:Dict						=      [x["""text"""] for x in qa["""answers"""]]
																		snake_case_			:Any						=      predictions[qa["""id"""]]

																		snake_case_			:Tuple						=      metric_max_over_ground_truths(_lowercase, _lowercase, _lowercase     )
																		if int(_exact_match     ) == 1:
																								correct_ids.append(qa["""id"""]     )
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(_lowercase, _lowercase, _lowercase     )

						snake_case_			:Optional[Any]						=      exact_match / total
						snake_case_			:Optional[Any]						=      fa / total

						return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
						__a     =    "1.0"
						__a     =    argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
						parser.add_argument("data_file", help="The dataset file in JSON format.")
						parser.add_argument("pred_file", help="The model prediction file in JSON format.")
						parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
						__a     =    parser.parse_args()

						with open(args.data_file) as data_file:
												__a     =    json.load(data_file)
												if dataset_json["version"] != expected_version:
																		print(
																		    F"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}""",
																		    file=sys.stderr,
																		)
												__a     =    dataset_json["data"]

						with open(args.pred_file) as pred_file:
												__a     =    json.load(pred_file)

						__a   ,					__a     =    evaluate(dataset, predictions)

						if args.output_correct_ids:
												print(F"""Output {len(correct_ids)} correctly answered question IDs.""")
												with open("correct_ids.json", "w") as f:
																		json.dump(correct_ids, f)





"""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, _lowercase = 512     ):







						'''simple docstring'''


						snake_case_			:int						=      x.shape[1] if len(x.shape     ) > 1 else 1
						snake_case_			:List[Any]						=      1 + np.arange(0, feature_num * offset, _lowercase, dtype=np.intaa     )
						snake_case_			:int						=      x + feature_offset
						return x
def        A_  (      _lowercase, _lowercase=True     ):







						'''simple docstring'''


						requires_backends(_lowercase, ["""cython"""]     )

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

						snake_case_			:Any						=      np.asarray(item["""edge_index"""], dtype=np.intaa     )

						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1
						snake_case_			:str						=      item["""num_nodes"""]

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

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

						snake_case_,	snake_case_			:Tuple						=      algos_graphormer.floyd_warshall(_lowercase     )
						snake_case_			:List[str]						=      np.amax(_lowercase     )

						snake_case_			:Dict						=      algos_graphormer.gen_edge_input(_lowercase, _lowercase, _lowercase     )
						snake_case_			:Tuple						=      np.zeros([num_nodes + 1, num_nodes + 1], dtype=np.single     )  # with graph token

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

						return item

class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Dict						,					snake_case:							Optional[int]=20						,					snake_case:							List[Any]=False						)       ->     Union[str, Any]:
											if not is_cython_available():
																	raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""						)

											snake_case_			:List[str]						=      spatial_pos_max
											snake_case_			:int						=      on_the_fly_processing






					def __call__(	self:							str						,					snake_case:							List[dict]						)       ->     Dict[str, Any]:
											if self.on_the_fly_processing:
																	snake_case_			:int						=      [preprocess_item(snake_case						) for i in features]

											if not isinstance(features[0]						,					snake_case						):
																	snake_case_			:Tuple						=      [vars(snake_case						) for f in features]
											snake_case_			:Any						=      {}

											snake_case_			:Dict						=      max(len(i["""input_nodes"""]						) for i in features						)
											snake_case_			:List[Any]						=      len(features[0]["""input_nodes"""][0]						)
											snake_case_			:Dict						=      len(features[0]["""attn_edge_type"""][0][0]						)
											snake_case_			:List[Any]						=      max(len(i["""input_edges"""][0][0]						) for i in features						)
											snake_case_			:Any						=      len(features[0]["""input_edges"""][0][0][0]						)
											snake_case_			:List[str]						=      len(snake_case						)

											snake_case_			:Union[str, Any]						=      torch.zeros(snake_case						,					max_node_num + 1						,					max_node_num + 1						,					dtype=torch.float						)
											snake_case_			:int						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Optional[Any]						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Tuple						=      torch.zeros(snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Dict						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Union[str, Any]						=      torch.zeros(
											    snake_case						,					snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)

											for ix, f in enumerate(snake_case						):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																							snake_case_			:List[str]						=      torch.tensor(f[k]						)

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

																	snake_case_			:Optional[Any]						=      f["""attn_bias"""]
																	snake_case_			:int						=      f[
																	    """attn_edge_type"""
																	]
																	snake_case_			:Dict						=      f["""spatial_pos"""]
																	snake_case_			:Tuple						=      f["""in_degree"""]
																	snake_case_			:List[Any]						=      f["""input_nodes"""]
																	snake_case_			:List[Any]						=      f["""input_edges"""]

											snake_case_			:int						=      batch["""in_degree"""]

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

											return batch
