






"""simple docstring"""



import argparse
import json
import re
import string
import sys
from collections import Counter
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict       ):





  '''simple docstring'''





  def remove_articles(__snake_case     :    Tuple       ):
    return re.sub(r'\b(a|an|the)\b'						,   ' '						,   __snake_case       )

  def white_space_fix(__snake_case     :    Dict       ):
    return " ".join(text.split()       )

  def remove_punc(__snake_case     :    Optional[Any]       ):
    lowercase				       =			set(string.punctuation       )
    return "".join(ch for ch in text if ch not in exclude       )

  def lower(__snake_case     :    Any       ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(__snake_case       )       )       )       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			normalize_answer(__snake_case       ).split()
  lowercase				       =			normalize_answer(__snake_case       ).split()
  lowercase				       =			Counter(__snake_case       ) & Counter(__snake_case       )
  lowercase				       =			sum(common.values()       )
  if num_same == 0:
    return 0
  lowercase				       =			1.0 * num_same / len(__snake_case       )
  lowercase				       =			1.0 * num_same / len(__snake_case       )
  lowercase				       =			(2 * precision * recall) / (precision + recall)
  return fa
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  return normalize_answer(__snake_case       ) == normalize_answer(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    List[Any]						,   __snake_case     :    Dict       ):





  '''simple docstring'''





  lowercase				       =			[]
  for ground_truth in ground_truths:
    lowercase				       =			metric_fn(__snake_case						,   __snake_case       )
    scores_for_ground_truths.append(__snake_case       )
  return max(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





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

      lowercase				       =			[x['text'] for x in qa['answers']]
      lowercase				       =			predictions[qa['id']]

      lowercase				       =			metric_max_over_ground_truths(__snake_case						,   __snake_case						,   __snake_case       )
      if int(_exact_match       ) == 1:
        correct_ids.append(qa['id']       )
      exact_match += _exact_match

      fa += metric_max_over_ground_truths(__snake_case						,   __snake_case						,   __snake_case       )

  lowercase				       =			exact_match / total
  lowercase				       =			fa / total

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


if __name__ == "__main__":
    _UpperCamelCase						:    int		=						'1.0'
    _UpperCamelCase						:    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.')
    _UpperCamelCase						:    List[str]		=						parser.parse_args()

    with open(args.data_file) as data_file:
        _UpperCamelCase						:    Optional[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,
            )
        _UpperCamelCase						:    int		=						dataset_json['data']

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

    _UpperCamelCase,						_UpperCamelCase						:    Any		=						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowercase:		List[str]    						=  logging.get_logger(__name__)

__lowercase:		List[Any]    						=  {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Optional[int]            =       'plbart'
 _lowerCamelCase			:       Union[str, Any]            =       ['past_key_values']
 _lowerCamelCase			:       Tuple            =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__(			self			:						Optional[Any],       a_			:						Union[str, Any]=5_0005,       a_			:						int=1024,       a_			:						str=6,       a_			:						Union[str, Any]=3072,       a_			:						Optional[int]=12,       a_			:						Optional[Any]=6,       a_			:						Dict=3072,       a_			:						Dict=12,       a_			:						int=0.0,       a_			:						List[Any]=0.0,       a_			:						Optional[Any]=True,       a_			:						List[str]=True,       a_			:						Tuple="gelu",       a_			:						Any=768,       a_			:						Dict=0.1,       a_			:						List[Any]=0.1,       a_			:						Union[str, Any]=0.0,       a_			:						Optional[Any]=0.02,       a_			:						Union[str, Any]=0.0,       a_			:						int=True,       a_			:						Dict=1,       a_			:						Optional[int]=0,       a_			:						Optional[int]=2,       a_			:						Any=2,       **a_			:						Optional[Any],       ):





    """simple docstring"""



    UpperCamelCase__			    =    vocab_size
    UpperCamelCase__			    =    max_position_embeddings
    UpperCamelCase__			    =    d_model
    UpperCamelCase__			    =    encoder_ffn_dim
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    encoder_attention_heads
    UpperCamelCase__			    =    decoder_ffn_dim
    UpperCamelCase__			    =    decoder_layers
    UpperCamelCase__			    =    decoder_attention_heads
    UpperCamelCase__			    =    dropout
    UpperCamelCase__			    =    attention_dropout
    UpperCamelCase__			    =    activation_dropout
    UpperCamelCase__			    =    activation_function
    UpperCamelCase__			    =    init_std
    UpperCamelCase__			    =    encoder_layerdrop
    UpperCamelCase__			    =    decoder_layerdrop
    UpperCamelCase__			    =    classifier_dropout
    UpperCamelCase__			    =    use_cache
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=a_,       bos_token_id=a_,       eos_token_id=a_,       is_encoder_decoder=a_,       forced_eos_token_id=a_,       **a_,       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "sequence"}),
            ("attention_mask", {0: "batch", 1: "sequence"}),
        ]       )

 @property
 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    if self.use_past:
       return OrderedDict(
           [
               ("last_hidden_state", {0: "batch", 1: "sequence"}),
               ("past_keys", {0: "batch", 2: "sequence"}),
               ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
           ]       )
    else:
       return OrderedDict(
           [
               ("last_hidden_state", {0: "batch", 1: "sequence"}),
               ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
           ]       )