


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 __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						int = 512		):
 lowerCamelCase_								=  x.shape[1] if len(x.shape		) > 1 else 1
 lowerCamelCase_								=  1 + np.arange(0 ,    feature_num * offset ,    UpperCAmelCase_ ,    dtype=np.intaa		)
 lowerCamelCase_								=  x + feature_offset
 return x





def __snake_case (				UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						Any=True		):
 requires_backends(UpperCAmelCase_ ,    ["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(UpperCAmelCase_		) + 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(UpperCAmelCase_		) + 1

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

 lowerCamelCase_			,lowerCamelCase_								=  algos_graphormer.floyd_warshall(UpperCAmelCase_		)
 lowerCamelCase_								=  np.amax(UpperCAmelCase_		)

 lowerCamelCase_								=  algos_graphormer.gen_edge_input(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)
 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(UpperCAmelCase_ ,    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 					snake_case   :





  """simple docstring"""




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







   """simple docstring"""
   if self.on_the_fly_processing:
    lowerCamelCase_								=  [preprocess_item(UpperCamelCase					) for i in features]

   if not isinstance(features[0]      ,  UpperCamelCase					):
    lowerCamelCase_								=  [vars(UpperCamelCase					) 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(UpperCamelCase					)

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







'''simple docstring'''

import argparse
import json
import re
import string
import sys
from collections import Counter





def __snake_case (				UpperCAmelCase_						:						str		):

 def remove_articles(UpperCAmelCase_						:						Optional[Any]		):
  return re.sub(r"\b(a|an|the)\b" ,    " " ,    UpperCAmelCase_		)

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

 def remove_punc(UpperCAmelCase_						:						Any		):
  lowerCamelCase_								=  set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(UpperCAmelCase_						:						List[str]		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  Counter(UpperCAmelCase_		) & Counter(UpperCAmelCase_		)
 lowerCamelCase_								=  sum(common.values()		)
 if num_same == 0:
  return 0
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  (2 * precision * recall) / (precision + recall)
 return fa





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  lowerCamelCase_								=  lowerCamelCase_								=  0
 lowerCamelCase_								=  []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    lowerCamelCase_								=  F'''Unanswered question {qa["id"]} will receive score 0.'''
    print(UpperCAmelCase_ ,    file=sys.stderr		)
    continue

   lowerCamelCase_								=  [x["text"] for x in qa["answers"]]
   lowerCamelCase_								=  predictions[qa["id"]]

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

   fa += metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  exact_match / total
 lowerCamelCase_								=  fa / total

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


if __name__ == "__main__":
     a_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			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_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			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_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			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)



