

import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


lowerCamelCase 	=   {0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
lowerCamelCase 	=   set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					int=0      , _A :					Optional[Any]=None				):





  '''simple docstring'''







  return [" ".join(x.split(''' '''				)[begin:end]				) for x in answers if len(x.split(''' '''				)				) > 1]




def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					str=False				):





  '''simple docstring'''







  if make_sub_answers:
    # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
    # *e.g.* if the correct answer contains a prefix such as "the", or "a"
    a__    =(
        given_answers + get_sub_answers(_A      , begin=1				) + get_sub_answers(_A      , end=-1				)
    )
  a__    =[]
  for answer in given_answers:
    a__    =answer.replace('''_'''      , ''' '''				).lower()
    a__    =''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias				)
    answers.append(''' '''.join(alias.split()				).strip()				)
  return set(_A				)




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					str      , _A :					Optional[Any]=1      , _A :					Optional[int]=1_00				):





  '''simple docstring'''







  a__, a__    =jax.lax.top_k(_A      , _A				)
  a__, a__    =jax.lax.top_k(_A      , _A				)

  a__    =best_end_idx[:, None] - best_start_idx[None, :]
  a__    =jnp.logical_or(widths < 0      , widths > max_size				)
  a__    =(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
  a__    =jnp.argmax(_A				).item()

  return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]




def      UpperCAmelCase__							(     _A :					List[Any]				):





  '''simple docstring'''







  a__    =sample['''question''']['''text''']
  a__    =sample['''document''']['''tokens''']['''token''']
  a__    =sample['''document''']['''tokens''']['''is_html''']
  a__    =sample['''annotations''']['''long_answer''']
  a__    =sample['''annotations''']['''short_answers''']

  a__    =''' '''.join([context[i] for i in range(len(_A				)				) if not is_html[i]]				)

  # 0 - No ; 1 - Yes
  for answer in sample["annotations"]["yes_no_answer"]:
    if answer == 0 or answer == 1:
      return {
          "question": question,
          "context": context_string,
          "short": [],
          "long": [],
          "category": "no" if answer == 0 else "yes",
      }

  a__    =[]
  for s in short_answers:
    short_targets.extend(s['''text''']				)
  a__    =list(set(_A				)				)

  a__    =[]
  for s in long_answers:
    if s["start_token"] == -1:
      continue
    a__    =context[s['''start_token'''] : s['''end_token''']]
    a__    =is_html[s['''start_token'''] : s['''end_token''']]
    a__    =''' '''.join([answer[i] for i in range(len(_A				)				) if not html[i]]				)
    if new_answer not in long_targets:
      long_targets.append(_A				)

  a__    ='''long_short''' if len(short_targets + long_targets				) > 0 else '''null'''

  return {
      "question": question,
      "context": context_string,
      "short": short_targets,
      "long": long_targets,
      "category": category,
  }




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =load_from_disk('''natural-questions-validation'''				)
  a__    =dataset.map(_A				).remove_columns(['''annotations''', '''document''', '''id''']				)
  print(_A				)

  a__    =dataset.filter(lambda _A				: (len(x['''question''']				) + len(x['''context''']				)) < 4 * 40_96				)
  a__    =short_validation_dataset.filter(lambda _A				: x["category"] != "null"				)
  short_validation_dataset

  a__    ='''vasudevgupta/flax-bigbird-natural-questions'''
  a__    =FlaxBigBirdForNaturalQuestions.from_pretrained(_A				)
  a__    =BigBirdTokenizerFast.from_pretrained(_A				)

  @jax.jit
  def forward(*_A :					str      , **_A :					Union[str, Any]				):
    a__, a__, a__    =model(*_A      , **_A				)
    return start_logits, end_logits, jnp.argmax(_A      , axis=-1				)

  def evaluate(_A :					Optional[Any]				):
    # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
    a__    =tokenizer(
        example['''question''']      , example['''context''']      , return_tensors='''np'''      , max_length=40_96      , padding='''max_length'''      , truncation=_A      , )

    a__, a__, a__    =forward(**_A				)

    a__    =CATEGORY_MAPPING[category.item()]

    a__    =example['''long'''] + example['''short''']
    if example["category"] in ["yes", "no", "null"]:
      a__    =[example['''category''']]
    a__    =example['''category'''] != '''null'''
    # Now target can be: "yes", "no", "null", "list of long & short answers"

    if predicted_category in ["yes", "no", "null"]:
      a__    =[predicted_category]
      a__    =example['''output'''] == example['''targets''']
      a__    =predicted_category != '''null'''
      return example

    a__    =38 if predicted_category == '''short''' else 10_24
    a__, a__    =get_best_valid_start_end_idx(
        start_scores[0]      , end_scores[0]      , top_k=8      , max_size=_A				)

    a__    =inputs['''input_ids'''][0].tolist()
    a__    =[tokenizer.decode(input_ids[start_score : end_score + 1]				)]

    a__    =expand_to_aliases(example['''targets''']      , make_sub_answers=_A				)
    a__    =expand_to_aliases(example['''output''']				)

    # some preprocessing to both prediction and answer
    a__    ={''''''.join(a.split()				) for a in answers}
    a__    ={''''''.join(p.split()				) for p in predictions}
    a__    ={s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

    # if there is a common element, it's a exact match
    a__    =len(list(answers & predictions				)				) > 0
    a__    =predicted_category != '''null''' and len(_A				) > 0

    return example

  a__    =short_validation_dataset.map(_A				)

  a__    =len(_A				)
  a__    =len(short_validation_dataset.filter(lambda _A				: x["match"] == 1				)				)
  print('''EM score:'''      , (matched / total) * 1_00      , '''%'''				)


if __name__ == "__main__":
       main()



def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  sequence.append(_A				)

  a__    =0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(_A				):
    return None

  return index


if __name__ == "__main__":
       lowerCamelCase 	=   input('''Enter numbers separated by comma:\n''').strip()
       lowerCamelCase 	=   [int(item) for item in user_input.split(''',''')]

       lowerCamelCase 	=   input('''Enter a single number to be found in the list:\n''')
       lowerCamelCase 	=   int(target_input)
       lowerCamelCase 	=   sentinel_linear_search(sequence, target)
       if result is not None:
              print(f"""{target} found at positions: {result}""")
       else:
              print('''Not found''')
