





'''simple docstring'''

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

from transformers import BigBirdTokenizerFast


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





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





def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   List[str]=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"
        _SCREAMING_SNAKE_CASE     =      (
            given_answers + get_sub_answers(__lowerCamelCase ,	begin=1   ) + get_sub_answers(__lowerCamelCase ,	end=-1   )
        )
    _SCREAMING_SNAKE_CASE     =      []
    for answer in given_answers:
        _SCREAMING_SNAKE_CASE     =      answer.replace("""_""" ,	""" """   ).lower()
        _SCREAMING_SNAKE_CASE     =      """""".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 lowerCamelCase						(					__lowerCamelCase			:   Any ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Optional[int]=1 ,	__lowerCamelCase			:   str=100   )						->Any:
    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      jax.lax.top_k(__lowerCamelCase ,	__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      jax.lax.top_k(__lowerCamelCase ,	__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      best_end_idx[:, None] - best_start_idx[None, :]
    _SCREAMING_SNAKE_CASE     =      jnp.logical_or(widths < 0 ,	widths > max_size   )
    _SCREAMING_SNAKE_CASE     =      (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
    _SCREAMING_SNAKE_CASE     =      jnp.argmax(__lowerCamelCase   ).item()

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





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->Any:
    _SCREAMING_SNAKE_CASE     =      sample["""question"""]["""text"""]
    _SCREAMING_SNAKE_CASE     =      sample["""document"""]["""tokens"""]["""token"""]
    _SCREAMING_SNAKE_CASE     =      sample["""document"""]["""tokens"""]["""is_html"""]
    _SCREAMING_SNAKE_CASE     =      sample["""annotations"""]["""long_answer"""]
    _SCREAMING_SNAKE_CASE     =      sample["""annotations"""]["""short_answers"""]

    _SCREAMING_SNAKE_CASE     =      """ """.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",
            }

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

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

    _SCREAMING_SNAKE_CASE     =      """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[int]:
    _SCREAMING_SNAKE_CASE     =      load_from_disk("""natural-questions-validation"""   )
    _SCREAMING_SNAKE_CASE     =      dataset.map(__lowerCamelCase   ).remove_columns(["""annotations""", """document""", """id"""]   )
    print(__lowerCamelCase   )

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

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

    @jax.jit
    def forward(*__lowerCamelCase			:   List[Any] ,	**__lowerCamelCase			:   List[str]   ):
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      model(*__lowerCamelCase ,	**__lowerCamelCase   )
        return start_logits, end_logits, jnp.argmax(__lowerCamelCase ,	axis=-1   )

    def evaluate(__lowerCamelCase			:   Optional[int]   ):
        # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
        _SCREAMING_SNAKE_CASE     =      tokenizer(
            example["""question"""] ,	example["""context"""] ,	return_tensors="""np""" ,	max_length=4096 ,	padding="""max_length""" ,	truncation=__lowerCamelCase ,	)

        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      forward(**__lowerCamelCase   )

        _SCREAMING_SNAKE_CASE     =      CATEGORY_MAPPING[category.item()]

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

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

        _SCREAMING_SNAKE_CASE     =      38 if predicted_category == """short""" else 1024
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      get_best_valid_start_end_idx(
            start_scores[0] ,	end_scores[0] ,	top_k=8 ,	max_size=__lowerCamelCase   )

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

        _SCREAMING_SNAKE_CASE     =      expand_to_aliases(example["""targets"""] ,	make_sub_answers=__lowerCamelCase   )
        _SCREAMING_SNAKE_CASE     =      expand_to_aliases(example["""output"""]   )

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

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

        return example

    _SCREAMING_SNAKE_CASE     =      short_validation_dataset.map(__lowerCamelCase   )

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


if __name__ == "__main__":
      main()










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int   )						->int:
    barcode //= 10  # exclude the last digit
    _SCREAMING_SNAKE_CASE     =      False
    _SCREAMING_SNAKE_CASE     =      0

    # extract and check each digit
    while barcode != 0:
        _SCREAMING_SNAKE_CASE     =      1 if checker else 3
        s += mult * (barcode % 10)
        barcode //= 10
        _SCREAMING_SNAKE_CASE     =      not checker

    return (10 - (s % 10)) % 10





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->bool:
    return len(str(__lowerCamelCase   )   ) == 13 and get_check_digit(__lowerCamelCase   ) == barcode % 10





def lowerCamelCase						(					__lowerCamelCase			:   str   )						->int:
    if str(__lowerCamelCase   ).isalpha():
        _SCREAMING_SNAKE_CASE     =      F'Barcode \'{barcode}\' has alphabetic characters.'
        raise ValueError(__lowerCamelCase   )
    elif int(__lowerCamelCase   ) < 0:
        raise ValueError("""The entered barcode has a negative value. Try again."""   )
    else:
        return int(__lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase_         =			get_barcode(input("""Barcode: """).strip())

      if is_valid(barcode):
            print(f"""'{barcode}' is a valid barcode.""")
      else:
            print(f"""'{barcode}' is NOT a valid barcode.""")



