


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a_  :    int      =			{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

a_  :    Any      =			datasets.load_dataset("""xnli""", split="""test+validation""")

a_  :    int      =			0
a_  :    Union[str, Any]      =			0
a_  :    Tuple      =			0
a_  :    str      =			0





def __snake_case (				UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						Optional[int]		):
 if spm_diff == list(reversed(__UpperCamelCase		)		):
  # AAA -> AA+A vs A+AA case.
  return True
 elif len(__UpperCamelCase		) == len(__UpperCamelCase		) and fast.decode(__UpperCamelCase		) == fast.decode(__UpperCamelCase		):
  # Second order OK
  # Barrich -> Barr + ich vs Bar + rich
  return True
 lowerCamelCase_								=  slow.encode(slow.decode(__UpperCamelCase		)		)
 lowerCamelCase_								=  fast.encode(fast.decode(__UpperCamelCase		)		)
 if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
  # Type 3 error.
  # Snehagatha ->
  #       Sne, h, aga, th, a
  #       Sne, ha, gat, ha
  # Encoding the wrong with sp does not even recover what spm gave us
  # It fits tokenizer however...
  return True
 return False





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_								=  fast.encode_plus(__UpperCamelCase		)[0]
 lowerCamelCase_								=  enc.offsets
 lowerCamelCase_			,lowerCamelCase_								=  offsets[idx], offsets[idx - 1]
 if curr is not None and line[curr[0] : curr[1]] == "\u200f":
  return True
 if prev is not None and line[prev[0] : prev[1]] == "\u200f":
  return True





def __snake_case (				UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Union[str, Any]		):
 # Encoding can be the same with same result AAA -> A + AA vs AA + A
 # We can check that we use at least exactly the same number of tokens.
 for i, (spm_id, tok_id) in enumerate(zip(__UpperCamelCase ,    __UpperCamelCase		)		):
  if spm_id != tok_id:
   break
 lowerCamelCase_								=  i
 for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCamelCase		) ,    reversed(__UpperCamelCase		)		)		):
  if spm_id != tok_id:
   break
 lowerCamelCase_								=  len(__UpperCamelCase		) - i

 lowerCamelCase_								=  spm_ids[first:last]
 lowerCamelCase_								=  tok_ids[first:last]

 if check_diff(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase		):
  return True

 if check_LTR_mark(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase		):
  return True

 if last - first > 5:
  # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
  lowerCamelCase_								=  Counter(spm_ids[first:last]		)
  lowerCamelCase_								=  Counter(tok_ids[first:last]		)

  lowerCamelCase_								=  {spm_ for (spm_, si) in spms.items() if toks.get(spm_ ,    0		) == si}
  lowerCamelCase_								=  3
  for i in range(last - first - min_width		):
   if all(spm_ids[first + i + j] in removable_tokens for j in range(__UpperCamelCase		)		):
    lowerCamelCase_								=  [
        k
        for k in range(last - first - min_width		)
        if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
    ]
    for j in possible_matches:
     if check_diff(spm_ids[first : first + i] ,    tok_ids[first : first + j] ,    __UpperCamelCase ,    __UpperCamelCase		) and check_details(
         __UpperCamelCase ,    spm_ids[first + i : last] ,    tok_ids[first + j : last] ,    __UpperCamelCase ,    __UpperCamelCase ,    ):
      return True

 print(F'''Spm: {[fast.decode([spm_ids[i]]		) for i in range(__UpperCamelCase ,    __UpperCamelCase		)]}'''		)
 try:
  print(F'''Tok: {[fast.decode([tok_ids[i]]		) for i in range(__UpperCamelCase ,    __UpperCamelCase		)]}'''		)
 except Exception:
  pass

 lowerCamelCase_								=  fast.decode(spm_ids[:first]		)
 lowerCamelCase_								=  fast.decode(spm_ids[last:]		)
 lowerCamelCase_								=  fast.decode(spm_ids[first:last]		)
 print()
 print(__UpperCamelCase		)
 return False





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Optional[Any]		):
 global perfect
 global imperfect
 global wrong
 global total

 lowerCamelCase_								=  slow.encode(__UpperCamelCase		)
 lowerCamelCase_								=  fast.encode(__UpperCamelCase		)

 lowerCamelCase_								=  False
 total += 1

 if slow_ids != fast_ids:
  if check_details(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase		):
   lowerCamelCase_								=  True
   imperfect += 1
  else:
   wrong += 1
 else:
  perfect += 1

 if total % 10000 == 0:
  print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''		)

 if skip_assert:
  return

 assert (
     slow_ids == fast_ids
 ), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__UpperCamelCase		)}\n{fast.tokenize(__UpperCamelCase		)}'''





def __snake_case (				UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						str		):
 global batch_total
 for i in range(len(__UpperCamelCase		)		):
  # premise, all languages
  for text in dataset[i]["premise"].values():
   test_string(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase		)

  # hypothesis, all languages
  for text in dataset[i]["hypothesis"]["translation"]:
   test_string(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase		)


if __name__ == "__main__":
     for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
          a_  :    List[Any]      =			list(slow_class.max_model_input_sizes.keys())
          for checkpoint in checkpoint_names:
               a_  :    Any      =			0
               a_  :    List[str]      =			0
               a_  :    Any      =			0
               a_  :    str      =			0

               print(f'''========================== Checking {name}: {checkpoint} ==========================''')
               a_  :    Dict      =			slow_class.from_pretrained(checkpoint, force_download=True)
               a_  :    List[Any]      =			fast_class.from_pretrained(checkpoint, force_download=True)
               test_tokenizer(slow, fast)
               print(f'''Accuracy {perfect * 100 / total:.2f}''')







'''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)



