




"""simple docstring"""



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

from transformers import BigBirdTokenizerFast


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







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=0     ,					lowercase_=None		)		->						Optional[int]:
 return [" ".join(x.split(" "		)[begin:end]		) for x in answers if len(x.split(" "		)		) > 1]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=False		)		->						Optional[int]:
 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(lowercase_     ,					begin=1		) + get_sub_answers(lowercase_     ,					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(lowercase_		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=1     ,					lowercase_=1_00		)		->						Tuple:
 A__, A__			=							jax.lax.top_k(lowercase_     ,					lowercase_		)
 A__, A__			=							jax.lax.top_k(lowercase_     ,					lowercase_		)

 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(lowercase_		).item()

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







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						List[str]:
 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(lowercase_		)		) 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(lowercase_		)		)

 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(lowercase_		)		) if not html[i]]		)
  if new_answer not in long_targets:
   long_targets.append(lowercase_		)

 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     _SCREAMING_SNAKE_CASE  (				)		->						Dict:
 A__			=							load_from_disk("natural-questions-validation"		)
 A__			=							dataset.map(lowercase_		).remove_columns(["annotations", "document", "id"]		)
 print(lowercase_		)

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

 A__			=							"vasudevgupta/flax-bigbird-natural-questions"
 A__			=							FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase_		)
 A__			=							BigBirdTokenizerFast.from_pretrained(lowercase_		)

 @jax.jit
 def forward(*lowercase_     ,					**lowercase_		):
  A__, A__, A__			=							model(*lowercase_     ,					**lowercase_		)
  return start_logits, end_logits, jnp.argmax(lowercase_     ,					axis=-1		)

 def evaluate(lowercase_		):
  # 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=lowercase_     ,					)

  A__, A__, A__			=							forward(**lowercase_		)

  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=lowercase_		)

  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=lowercase_		)
  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(lowercase_		) > 0

  return example

 A__			=							short_validation_dataset.map(lowercase_		)

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


if __name__ == "__main__":
      main()











"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


if __name__ == "__main__":
      print(f'{solution() = }')





