


'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


a_  :    str      =			logging.get_logger(__name__)

a_  :    Optional[int]      =			{
    """flaubert/flaubert_small_cased""": """https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json""",
    """flaubert/flaubert_base_uncased""": """https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json""",
    """flaubert/flaubert_base_cased""": """https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json""",
    """flaubert/flaubert_large_cased""": """https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json""",
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "flaubert"
  _lowerCamelCase		   =  {
      "hidden_size": "emb_dim",
      "num_attention_heads": "n_heads",
      "num_hidden_layers": "n_layers",
      "n_words": "vocab_size",  # For backward compatibility
  }






  def __init__(   self      ,  UpperCamelCase=False      ,  UpperCamelCase=0.0      ,  UpperCamelCase=3_0145      ,  UpperCamelCase=2048      ,  UpperCamelCase=12      ,  UpperCamelCase=16      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=True      ,  UpperCamelCase=False      ,  UpperCamelCase=False      ,  UpperCamelCase=False      ,  UpperCamelCase=1      ,  UpperCamelCase=True      ,  UpperCamelCase=512      ,  UpperCamelCase=2048**-0.5      ,  UpperCamelCase=1e-12      ,  UpperCamelCase=0.02      ,  UpperCamelCase=0      ,  UpperCamelCase=1      ,  UpperCamelCase=2      ,  UpperCamelCase=3      ,  UpperCamelCase=5      ,  UpperCamelCase=True      ,  UpperCamelCase="first"      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  UpperCamelCase=True      ,  UpperCamelCase=0.1      ,  UpperCamelCase=5      ,  UpperCamelCase=5      ,  UpperCamelCase=0      ,  UpperCamelCase=0      ,  UpperCamelCase=2      ,  UpperCamelCase=0      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  pre_norm
   lowerCamelCase_								=  layerdrop
   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  emb_dim
   lowerCamelCase_								=  n_layers
   lowerCamelCase_								=  n_heads
   lowerCamelCase_								=  dropout
   lowerCamelCase_								=  attention_dropout
   lowerCamelCase_								=  gelu_activation
   lowerCamelCase_								=  sinusoidal_embeddings
   lowerCamelCase_								=  causal
   lowerCamelCase_								=  asm
   lowerCamelCase_								=  n_langs
   lowerCamelCase_								=  use_lang_emb
   lowerCamelCase_								=  layer_norm_eps
   lowerCamelCase_								=  bos_index
   lowerCamelCase_								=  eos_index
   lowerCamelCase_								=  pad_index
   lowerCamelCase_								=  unk_index
   lowerCamelCase_								=  mask_index
   lowerCamelCase_								=  is_encoder
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  embed_init_std
   lowerCamelCase_								=  init_std
   lowerCamelCase_								=  summary_type
   lowerCamelCase_								=  summary_use_proj
   lowerCamelCase_								=  summary_activation
   lowerCamelCase_								=  summary_proj_to_labels
   lowerCamelCase_								=  summary_first_dropout
   lowerCamelCase_								=  start_n_top
   lowerCamelCase_								=  end_n_top
   lowerCamelCase_								=  mask_token_id
   lowerCamelCase_								=  lang_id

   if "n_words" in kwargs:
    lowerCamelCase_								=  kwargs["n_words"]

   super().__init__(pad_token_id=UpperCamelCase      ,  bos_token_id=UpperCamelCase      ,  **UpperCamelCase					)





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @property
  def   snake_case		(   self					):







   """simple docstring"""
   if self.task == "multiple-choice":
    lowerCamelCase_								=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    lowerCamelCase_								=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]					)







'''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)



