

"""simple docstring"""


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

from transformers import BigBirdTokenizerFast


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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=0      ,				__SCREAMING_SNAKE_CASE=None				)			->			Tuple:
							return [" ".join(x.split(" "				)[begin:end]				) for x in answers if len(x.split(" "				)				) > 1]






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=False				)			->			Any:
							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"
														__lowerCAmelCase: Optional[int]					   =    (
														    given_answers + get_sub_answers(__SCREAMING_SNAKE_CASE      ,				begin=1				) + get_sub_answers(__SCREAMING_SNAKE_CASE      ,				end=-1				)
														)
							__lowerCAmelCase: Tuple					   =    []
							for answer in given_answers:
														__lowerCAmelCase: Optional[int]					   =    answer.replace("_"      ,				" "				).lower()
														__lowerCAmelCase: str					   =    "".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias				)
														answers.append(" ".join(alias.split()				).strip()				)
							return set(__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=1      ,				__SCREAMING_SNAKE_CASE=1_0_0				)			->			Optional[int]:
							__lowerCAmelCase: str					   =    jax.lax.top_k(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
							__lowerCAmelCase: Any					   =    jax.lax.top_k(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

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

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






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Union[str, Any]:
							__lowerCAmelCase: int					   =    sample["question"]["text"]
							__lowerCAmelCase: Optional[int]					   =    sample["document"]["tokens"]["token"]
							__lowerCAmelCase: str					   =    sample["document"]["tokens"]["is_html"]
							__lowerCAmelCase: Any					   =    sample["annotations"]["long_answer"]
							__lowerCAmelCase: str					   =    sample["annotations"]["short_answers"]

							__lowerCAmelCase: Dict					   =    " ".join([context[i] for i in range(len(__SCREAMING_SNAKE_CASE				)				) 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",
																					}

							__lowerCAmelCase: Tuple					   =    []
							for s in short_answers:
														short_targets.extend(s["text"]				)
							__lowerCAmelCase: Dict					   =    list(set(__SCREAMING_SNAKE_CASE				)				)

							__lowerCAmelCase: Dict					   =    []
							for s in long_answers:
														if s["start_token"] == -1:
																					continue
														__lowerCAmelCase: Union[str, Any]					   =    context[s["start_token"] : s["end_token"]]
														__lowerCAmelCase: Optional[Any]					   =    is_html[s["start_token"] : s["end_token"]]
														__lowerCAmelCase: Any					   =    " ".join([answer[i] for i in range(len(__SCREAMING_SNAKE_CASE				)				) if not html[i]]				)
														if new_answer not in long_targets:
																					long_targets.append(__SCREAMING_SNAKE_CASE				)

							__lowerCAmelCase: Dict					   =    "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 					a__					(					)			->			Dict:
							__lowerCAmelCase: str					   =    load_from_disk("natural-questions-validation"				)
							__lowerCAmelCase: List[Any]					   =    dataset.map(__SCREAMING_SNAKE_CASE				).remove_columns(["annotations", "document", "id"]				)
							print(__SCREAMING_SNAKE_CASE				)

							__lowerCAmelCase: Optional[int]					   =    dataset.filter(lambda __SCREAMING_SNAKE_CASE				: (len(x["question"]				) + len(x["context"]				)) < 4 * 4_0_9_6				)
							__lowerCAmelCase: Union[str, Any]					   =    short_validation_dataset.filter(lambda __SCREAMING_SNAKE_CASE				: x["category"] != "null"				)
							short_validation_dataset

							__lowerCAmelCase: Union[str, Any]					   =    "vasudevgupta/flax-bigbird-natural-questions"
							__lowerCAmelCase: List[Any]					   =    FlaxBigBirdForNaturalQuestions.from_pretrained(__SCREAMING_SNAKE_CASE				)
							__lowerCAmelCase: Dict					   =    BigBirdTokenizerFast.from_pretrained(__SCREAMING_SNAKE_CASE				)

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

							def evaluate(__SCREAMING_SNAKE_CASE				):
														# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
														__lowerCAmelCase: List[Any]					   =    tokenizer(
														    example["question"]      ,				example["context"]      ,				return_tensors="np"      ,				max_length=4_0_9_6      ,				padding="max_length"      ,				truncation=__SCREAMING_SNAKE_CASE      ,				)

														__lowerCAmelCase: int					   =    forward(**__SCREAMING_SNAKE_CASE				)

														__lowerCAmelCase: str					   =    CATEGORY_MAPPING[category.item()]

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

														if predicted_category in ["yes", "no", "null"]:
																					__lowerCAmelCase: Any					   =    [predicted_category]
																					__lowerCAmelCase: Tuple					   =    example["output"] == example["targets"]
																					__lowerCAmelCase: List[Any]					   =    predicted_category != "null"
																					return example

														__lowerCAmelCase: Union[str, Any]					   =    3_8 if predicted_category == "short" else 1_0_2_4
														__lowerCAmelCase: Optional[Any]					   =    get_best_valid_start_end_idx(
														    start_scores[0]      ,				end_scores[0]      ,				top_k=8      ,				max_size=__SCREAMING_SNAKE_CASE				)

														__lowerCAmelCase: int					   =    inputs["input_ids"][0].tolist()
														__lowerCAmelCase: str					   =    [tokenizer.decode(input_ids[start_score : end_score + 1]				)]

														__lowerCAmelCase: Optional[int]					   =    expand_to_aliases(example["targets"]      ,				make_sub_answers=__SCREAMING_SNAKE_CASE				)
														__lowerCAmelCase: int					   =    expand_to_aliases(example["output"]				)

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

														# if there is a common element, it's a exact match
														__lowerCAmelCase: Optional[int]					   =    len(list(answers & predictions				)				) > 0
														__lowerCAmelCase: Dict					   =    predicted_category != "null" and len(__SCREAMING_SNAKE_CASE				) > 0

														return example

							__lowerCAmelCase: List[Any]					   =    short_validation_dataset.map(__SCREAMING_SNAKE_CASE				)

							__lowerCAmelCase: Union[str, Any]					   =    len(__SCREAMING_SNAKE_CASE				)
							__lowerCAmelCase: List[Any]					   =    len(short_validation_dataset.filter(lambda __SCREAMING_SNAKE_CASE				: x["match"] == 1				)				)
							print("EM score:"      ,				(matched / total) * 1_0_0      ,				"%"				)


if __name__ == "__main__":
			main()





"""simple docstring"""


import math






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			list:
		if n <= 0:
				raise ValueError("Only positive integers have prime factors"				)
		__lowerCAmelCase: Tuple					   =    []
		while n % 2 == 0:
				pf.append(2				)
				__lowerCAmelCase: str					   =    int(n / 2				)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				while n % i == 0:
						pf.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: List[Any]					   =    int(n / i				)
		if n > 2:
				pf.append(__SCREAMING_SNAKE_CASE				)
		return pf






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: int					   =    1
		__lowerCAmelCase: str					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Dict					   =    int(n / 2				)
		div *= temp
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: int					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: Tuple					   =    int(n / i				)
				div *= temp
		if n > 1:
				div *= 2
		return div






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: Dict					   =    1
		__lowerCAmelCase: int					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Tuple					   =    int(n / 2				)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: List[Any]					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: List[str]					   =    int(n / i				)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Optional[int]					   =    n
		for x in set(prime_factors(__SCREAMING_SNAKE_CASE				)				):
				s *= (x - 1) / x
		return int(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


