'''simple docstring'''




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

from transformers import BigBirdTokenizerFast


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






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






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__=False       )			->					Dict:
    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						=						(
            given_answers + get_sub_answers(UpperCamelCase__       ,						begin=1       ) + get_sub_answers(UpperCamelCase__       ,						end=-1       )
        )
    __lowerCamelCase						=						[]
    for answer in given_answers:
        __lowerCamelCase						=						answer.replace('''_'''       ,						''' '''       ).lower()
        __lowerCamelCase						=						''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias       )
        answers.append(''' '''.join(alias.split()       ).strip()       )
    return set(UpperCamelCase__       )






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__=1       ,						UpperCamelCase__=1_00       )			->					List[str]:
    __lowerCamelCase			,						__lowerCamelCase						=						jax.lax.top_k(UpperCamelCase__       ,						UpperCamelCase__       )
    __lowerCamelCase			,						__lowerCamelCase						=						jax.lax.top_k(UpperCamelCase__       ,						UpperCamelCase__       )

    __lowerCamelCase						=						best_end_idx[:, None] - best_start_idx[None, :]
    __lowerCamelCase						=						jnp.logical_or(widths < 0       ,						widths > max_size       )
    __lowerCamelCase						=						(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
    __lowerCamelCase						=						jnp.argmax(UpperCamelCase__       ).item()

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






def __lowerCAmelCase				(							UpperCamelCase__       )			->					List[str]:
    __lowerCamelCase						=						sample['''question''']['''text''']
    __lowerCamelCase						=						sample['''document''']['''tokens''']['''token''']
    __lowerCamelCase						=						sample['''document''']['''tokens''']['''is_html''']
    __lowerCamelCase						=						sample['''annotations''']['''long_answer''']
    __lowerCamelCase						=						sample['''annotations''']['''short_answers''']

    __lowerCamelCase						=						''' '''.join([context[i] for i in range(len(UpperCamelCase__       )       ) 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						=						[]
    for s in short_answers:
        short_targets.extend(s['''text''']       )
    __lowerCamelCase						=						list(set(UpperCamelCase__       )       )

    __lowerCamelCase						=						[]
    for s in long_answers:
        if s["start_token"] == -1:
            continue
        __lowerCamelCase						=						context[s['''start_token'''] : s['''end_token''']]
        __lowerCamelCase						=						is_html[s['''start_token'''] : s['''end_token''']]
        __lowerCamelCase						=						''' '''.join([answer[i] for i in range(len(UpperCamelCase__       )       ) if not html[i]]       )
        if new_answer not in long_targets:
            long_targets.append(UpperCamelCase__       )

    __lowerCamelCase						=						'''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 __lowerCAmelCase				(							)			->					Optional[Any]:
    __lowerCamelCase						=						load_from_disk('''natural-questions-validation'''       )
    __lowerCamelCase						=						dataset.map(UpperCamelCase__       ).remove_columns(['''annotations''', '''document''', '''id''']       )
    print(UpperCamelCase__       )

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

    __lowerCamelCase						=						'''vasudevgupta/flax-bigbird-natural-questions'''
    __lowerCamelCase						=						FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCamelCase__       )
    __lowerCamelCase						=						BigBirdTokenizerFast.from_pretrained(UpperCamelCase__       )

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

    def evaluate(UpperCamelCase__       ):
        # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
        __lowerCamelCase						=						tokenizer(
            example['''question''']       ,						example['''context''']       ,						return_tensors='''np'''       ,						max_length=40_96       ,						padding='''max_length'''       ,						truncation=UpperCamelCase__       ,						)

        __lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase						=						forward(**UpperCamelCase__       )

        __lowerCamelCase						=						CATEGORY_MAPPING[category.item()]

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

        if predicted_category in ["yes", "no", "null"]:
            __lowerCamelCase						=						[predicted_category]
            __lowerCamelCase						=						example['''output'''] == example['''targets''']
            __lowerCamelCase						=						predicted_category != '''null'''
            return example

        __lowerCamelCase						=						38 if predicted_category == '''short''' else 10_24
        __lowerCamelCase			,						__lowerCamelCase						=						get_best_valid_start_end_idx(
            start_scores[0]       ,						end_scores[0]       ,						top_k=8       ,						max_size=UpperCamelCase__       )

        __lowerCamelCase						=						inputs['''input_ids'''][0].tolist()
        __lowerCamelCase						=						[tokenizer.decode(input_ids[start_score : end_score + 1]       )]

        __lowerCamelCase						=						expand_to_aliases(example['''targets''']       ,						make_sub_answers=UpperCamelCase__       )
        __lowerCamelCase						=						expand_to_aliases(example['''output''']       )

        # some preprocessing to both prediction and answer
        __lowerCamelCase						=						{''''''.join(a.split()       ) for a in answers}
        __lowerCamelCase						=						{''''''.join(p.split()       ) for p in predictions}
        __lowerCamelCase						=						{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

        # if there is a common element, it's a exact match
        __lowerCamelCase						=						len(list(answers & predictions       )       ) > 0
        __lowerCamelCase						=						predicted_category != '''null''' and len(UpperCamelCase__       ) > 0

        return example

    __lowerCamelCase						=						short_validation_dataset.map(UpperCamelCase__       )

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


if __name__ == "__main__":
  main()

'''simple docstring'''




def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    if len(UpperCamelCase__       ) == 1 and series[0] != 0:
        return True
    __lowerCamelCase						=						[]
    __lowerCamelCase						=						len(UpperCamelCase__       )
    for i in range(0       ,						UpperCamelCase__       ):
        if series[i] == 0:
            raise ValueError('''Input series cannot have 0 as an element'''       )
        rec_series.append(1 / series[i]       )
    __lowerCamelCase						=						rec_series[1] - rec_series[0]
    for index in range(2       ,						UpperCamelCase__       ):
        if rec_series[index] - rec_series[index - 1] != common_diff:
            return False
    return True






def __lowerCAmelCase				(							UpperCamelCase__       )			->					float:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    __lowerCamelCase						=						0
    for val in series:
        answer += 1 / val
    return len(UpperCamelCase__       ) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()
