





"""simple docstring"""




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

from transformers import BigBirdTokenizerFast


lowerCamelCase_	:		Union[str, Any]         =   {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"}
lowerCamelCase_	:		Optional[int]         =   set(''.join(['‘', '’', '´', '`', '.', ',', '-', '\"']))





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase=0					,   _UpperCAmelCase=None   ):






  """simple docstring"""





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





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase=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_    :			Dict									=    (
        given_answers + get_sub_answers(__A					,   begin=1   ) + get_sub_answers(__A					,   end=-1   )
    )
  A_    :			Tuple									=    []
  for answer in given_answers:
    A_    :			int									=    answer.replace('_'					,   ' '   ).lower()
    A_    :			Union[str, Any]									=    """""".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__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=1					,   _UpperCAmelCase=100   ):






  """simple docstring"""





  A_    :			List[Any]									=    jax.lax.top_k(__A					,   __A   )
  A_    :			int									=    jax.lax.top_k(__A					,   __A   )

  A_    :			List[Any]									=    best_end_idx[:, None] - best_start_idx[None, :]
  A_    :			Optional[Any]									=    jnp.logical_or(widths < 0					,   widths > max_size   )
  A_    :			Union[str, Any]									=    (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
  A_    :			Tuple									=    jnp.argmax(__A   ).item()

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





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    sample["""question"""]["""text"""]
  A_    :			Dict									=    sample["""document"""]["""tokens"""]["""token"""]
  A_    :			Optional[int]									=    sample["""document"""]["""tokens"""]["""is_html"""]
  A_    :			Optional[int]									=    sample["""annotations"""]["""long_answer"""]
  A_    :			Optional[int]									=    sample["""annotations"""]["""short_answers"""]

  A_    :			List[str]									=    """ """.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_    :			str									=    []
  for s in short_answers:
    short_targets.extend(s['text']   )
  A_    :			List[str]									=    list(set(__A   )   )

  A_    :			Optional[int]									=    []
  for s in long_answers:
    if s["start_token"] == -1:
      continue
    A_    :			Tuple									=    context[s["""start_token"""] : s["""end_token"""]]
    A_    :			List[Any]									=    is_html[s["""start_token"""] : s["""end_token"""]]
    A_    :			List[str]									=    """ """.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_    :			int									=    """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_    :			Optional[int]									=    load_from_disk('natural-questions-validation'   )
  A_    :			Optional[int]									=    dataset.map(__A   ).remove_columns(['annotations', 'document', 'id']   )
  print(__A   )

  A_    :			Optional[Any]									=    dataset.filter(lambda _UpperCAmelCase   : (len(x['question']   ) + len(x['context']   )) < 4 * 4096   )
  A_    :			Union[str, Any]									=    short_validation_dataset.filter(lambda _UpperCAmelCase   : x["category"] != "null"   )
  short_validation_dataset

  A_    :			Any									=    """vasudevgupta/flax-bigbird-natural-questions"""
  A_    :			Any									=    FlaxBigBirdForNaturalQuestions.from_pretrained(__A   )
  A_    :			Union[str, Any]									=    BigBirdTokenizerFast.from_pretrained(__A   )

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

  def evaluate(_UpperCAmelCase   ):
    # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
    A_    :			Dict									=    tokenizer(
        example['question']					,   example['context']					,   return_tensors='np'					,   max_length=4096					,   padding='max_length'					,   truncation=__A					,   )

    A_    :			Optional[int]									=    forward(**__A   )

    A_    :			Tuple									=    CATEGORY_MAPPING[category.item()]

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

    if predicted_category in ["yes", "no", "null"]:
      A_    :			Union[str, Any]									=    [predicted_category]
      A_    :			List[str]									=    example["""output"""] == example["""targets"""]
      A_    :			str									=    predicted_category != """null"""
      return example

    A_    :			Dict									=    38 if predicted_category == """short""" else 1024
    A_    :			List[Any]									=    get_best_valid_start_end_idx(
        start_scores[0]					,   end_scores[0]					,   top_k=8					,   max_size=__A   )

    A_    :			List[Any]									=    inputs["""input_ids"""][0].tolist()
    A_    :			List[Any]									=    [tokenizer.decode(input_ids[start_score : end_score + 1]   )]

    A_    :			List[str]									=    expand_to_aliases(example['targets']					,   make_sub_answers=__A   )
    A_    :			Optional[Any]									=    expand_to_aliases(example['output']   )

    # some preprocessing to both prediction and answer
    A_    :			int									=    {"""""".join(a.split()   ) for a in answers}
    A_    :			Optional[Any]									=    {"""""".join(p.split()   ) for p in predictions}
    A_    :			Dict									=    {s for s in predictions if s not in ["""``""", """''""", """`""", """'"""]}

    # if there is a common element, it's a exact match
    A_    :			Union[str, Any]									=    len(list(answers & predictions   )   ) > 0
    A_    :			List[str]									=    predicted_category != """null""" and len(__A   ) > 0

    return example

  A_    :			int									=    short_validation_dataset.map(__A   )

  A_    :			List[Any]									=    len(__A   )
  A_    :			List[str]									=    len(short_validation_dataset.filter(lambda _UpperCAmelCase   : x["match"] == 1   )   )
  print('EM score:'					,   (matched / total) * 100					,   '%'   )


if __name__ == "__main__":
   main()
from __future__ import annotations

import sys







def     A__	(       __A						):







    '''simple docstring'''
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""						)
    return _fib(__A						)[0]







def     A__	(       __A						):







    '''simple docstring'''
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    _fib(n // 2						)
    _lowerCamelCase  :		List[Any]			    =    a * (b * 2 - a)
    _lowerCamelCase  :		Optional[int]			    =    a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=int(sys.argv[1])
    print(F"""fibonacci({n}) is {fibonacci(n)}""")




