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



def 						__magic_name__		(				UpperCamelCase    :		List[str]       ,		UpperCamelCase    :		List[str]=0       ,		UpperCamelCase    :		Union[str, Any]=None    )    ->	str:
    return [" ".join(x.split(' '    )[begin:end]    ) for x in answers if len(x.split(' '    )    ) > 1]



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		Dict=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"
        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 						__magic_name__		(				UpperCamelCase    :		List[Any]       ,		UpperCamelCase    :		Tuple       ,		UpperCamelCase    :		List[Any]=1       ,		UpperCamelCase    :		Any=100    )    ->	List[str]:
    a__    = jax.lax.top_k(lowercase__       ,		lowercase__    )
    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 						__magic_name__		(				UpperCamelCase    :		str    )    ->	Optional[int]:
    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 						__magic_name__		(				)    ->	List[Any]:
    a__    = load_from_disk('natural-questions-validation'    )
    a__    = dataset.map(lowercase__    ).remove_columns(['annotations', 'document', 'id']    )
    print(lowercase__    )

    a__    = dataset.filter(lambda UpperCamelCase    : (len(x['question']    ) + len(x['context']    )) < 4 * 4096    )
    a__    = short_validation_dataset.filter(lambda UpperCamelCase    : 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(*UpperCamelCase    :		Union[str, Any]       ,		**UpperCamelCase    :		int    ):
        a__    = model(*lowercase__       ,		**lowercase__    )
        return start_logits, end_logits, jnp.argmax(lowercase__       ,		axis=-1    )

    def evaluate(UpperCamelCase    :		int    ):
        # 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=lowercase__       ,		)

        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 1024
        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 UpperCamelCase    : x["match"] == 1    )    )
    print('EM score:'       ,		(matched / total) * 100       ,		'%'    )


if __name__ == "__main__":
   main()




"""simple docstring"""




from itertools import combinations, product



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


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


