




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

from transformers import BigBirdTokenizerFast


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





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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=False					)   -> str:
	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(__lowerCamelCase							,    begin=1					) + get_sub_answers(__lowerCamelCase							,    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(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=1							,    __lowerCamelCase=100					)   -> Optional[int]:
	a       ,				a		       =							jax.lax.top_k(__lowerCamelCase							,    __lowerCamelCase					)
	a       ,				a		       =							jax.lax.top_k(__lowerCamelCase							,    __lowerCamelCase					)

	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(__lowerCamelCase					).item()

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





def 	__A	(			__lowerCamelCase					)   -> Optional[Any]:
	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(__lowerCamelCase					)					) 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(__lowerCamelCase					)					)

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

	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 	__A	(			)   -> Union[str, Any]:
	a		       =							load_from_disk("""natural-questions-validation"""					)
	a		       =							dataset.map(__lowerCamelCase					).remove_columns(["""annotations""", """document""", """id"""]					)
	print(__lowerCamelCase					)

	a		       =							dataset.filter(lambda __lowerCamelCase					: (len(x["""question"""]					) + len(x["""context"""]					)) < 4 * 4096					)
	a		       =							short_validation_dataset.filter(lambda __lowerCamelCase					: x["category"] != "null"					)
	short_validation_dataset

	a		       =							"""vasudevgupta/flax-bigbird-natural-questions"""
	a		       =							FlaxBigBirdForNaturalQuestions.from_pretrained(__lowerCamelCase					)
	a		       =							BigBirdTokenizerFast.from_pretrained(__lowerCamelCase					)

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

	def evaluate(__lowerCamelCase					):
		# 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=4096							,    padding="""max_length"""							,    truncation=__lowerCamelCase							,    )

		a       ,				a       ,				a		       =							forward(**__lowerCamelCase					)

		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 1024
		a       ,				a		       =							get_best_valid_start_end_idx(
		    start_scores[0]							,    end_scores[0]							,    top_k=8							,    max_size=__lowerCamelCase					)

		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=__lowerCamelCase					)
		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(__lowerCamelCase					) > 0

		return example

	a		       =							short_validation_dataset.map(__lowerCamelCase					)

	a		       =							len(__lowerCamelCase					)
	a		       =							len(short_validation_dataset.filter(lambda __lowerCamelCase					: x["match"] == 1					)					)
	print("""EM score:"""							,    (matched / total) * 100							,    """%"""					)


if __name__ == "__main__":
		main()







from itertools import combinations, product





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	a		       =							xa * xa + ya * ya
	a		       =							xa * xa + ya * ya
	a		       =							(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)





def 	__A	(			__lowerCamelCase = 50					)   -> int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)							,    repeat=2					)							,    2					)
	    if is_right(*__lowerCamelCase							,    *__lowerCamelCase					)					)


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

