

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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=0 ,	SCREAMING_SNAKE_CASE_=None )->	Any:



    """simple docstring"""






    return [" ".join(x.split(" " )[begin:end] ) for x in answers if len(x.split(" " ) ) > 1]




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=False )->	Union[str, Any]:



    """simple docstring"""






    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"
        UpperCamelCase_      =	(
            given_answers + get_sub_answers(SCREAMING_SNAKE_CASE_ ,	begin=1 ) + get_sub_answers(SCREAMING_SNAKE_CASE_ ,	end=-1 )
        )
    UpperCamelCase_      =	[]
    for answer in given_answers:
        UpperCamelCase_      =	answer.replace("_" ,	" " ).lower()
        UpperCamelCase_      =	"".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  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=1 ,	SCREAMING_SNAKE_CASE_=1_0_0 )->	Optional[int]:



    """simple docstring"""






    UpperCamelCase_     ,					UpperCamelCase_      =	jax.lax.top_k(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_     ,					UpperCamelCase_      =	jax.lax.top_k(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )

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

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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    UpperCamelCase_      =	sample["question"]["text"]
    UpperCamelCase_      =	sample["document"]["tokens"]["token"]
    UpperCamelCase_      =	sample["document"]["tokens"]["is_html"]
    UpperCamelCase_      =	sample["annotations"]["long_answer"]
    UpperCamelCase_      =	sample["annotations"]["short_answers"]

    UpperCamelCase_      =	" ".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",
            }

    UpperCamelCase_      =	[]
    for s in short_answers:
        short_targets.extend(s["text"] )
    UpperCamelCase_      =	list(set(SCREAMING_SNAKE_CASE_ ) )

    UpperCamelCase_      =	[]
    for s in long_answers:
        if s["start_token"] == -1:
            continue
        UpperCamelCase_      =	context[s["start_token"] : s["end_token"]]
        UpperCamelCase_      =	is_html[s["start_token"] : s["end_token"]]
        UpperCamelCase_      =	" ".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_ )

    UpperCamelCase_      =	"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(			)->	List[str]:



    """simple docstring"""






    UpperCamelCase_      =	load_from_disk("natural-questions-validation" )
    UpperCamelCase_      =	dataset.map(SCREAMING_SNAKE_CASE_ ).remove_columns(["annotations", "document", "id"] )
    print(SCREAMING_SNAKE_CASE_ )

    UpperCamelCase_      =	dataset.filter(lambda SCREAMING_SNAKE_CASE_ : (len(x["question"] ) + len(x["context"] )) < 4 * 4_0_9_6 )
    UpperCamelCase_      =	short_validation_dataset.filter(lambda SCREAMING_SNAKE_CASE_ : x["category"] != "null" )
    short_validation_dataset

    UpperCamelCase_      =	"vasudevgupta/flax-bigbird-natural-questions"
    UpperCamelCase_      =	FlaxBigBirdForNaturalQuestions.from_pretrained(SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	BigBirdTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_ )

    @jax.jit
    def forward(*SCREAMING_SNAKE_CASE_ ,	**SCREAMING_SNAKE_CASE_ ):
        UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	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
        UpperCamelCase_      =	tokenizer(
            example["question"] ,	example["context"] ,	return_tensors="np" ,	max_length=4_0_9_6 ,	padding="max_length" ,	truncation=SCREAMING_SNAKE_CASE_ ,	)

        UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	forward(**SCREAMING_SNAKE_CASE_ )

        UpperCamelCase_      =	CATEGORY_MAPPING[category.item()]

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

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

        UpperCamelCase_      =	3_8 if predicted_category == "short" else 1_0_2_4
        UpperCamelCase_     ,					UpperCamelCase_      =	get_best_valid_start_end_idx(
            start_scores[0] ,	end_scores[0] ,	top_k=8 ,	max_size=SCREAMING_SNAKE_CASE_ )

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

        UpperCamelCase_      =	expand_to_aliases(example["targets"] ,	make_sub_answers=SCREAMING_SNAKE_CASE_ )
        UpperCamelCase_      =	expand_to_aliases(example["output"] )

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

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

        return example

    UpperCamelCase_      =	short_validation_dataset.map(SCREAMING_SNAKE_CASE_ )

    UpperCamelCase_      =	len(SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	len(short_validation_dataset.filter(lambda SCREAMING_SNAKE_CASE_ : x["match"] == 1 ) )
    print("EM score:" ,	(matched / total) * 1_0_0 ,	"%" )


if __name__ == "__main__":
 main()



def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	str:



    """simple docstring"""






    if power < 0:
        return -1
    base %= mod
    UpperCamelCase_      =	1

    while power > 0:
        if power & 1:
            UpperCamelCase_      =	(result * base) % mod
        UpperCamelCase_      =	power >> 1
        UpperCamelCase_      =	(base * base) % mod

    return result




def  lowerCAmelCase(			)->	Tuple:



    """simple docstring"""






    print(modular_exponential(3 ,	2_0_0 ,	1_3 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()
