


import re






def 		_A		(		_lowercase   )				->   List[str]:


     """simple docstring"""


     def docstring_decorator(_lowercase   ):
          __UpperCamelCase   = fn.__doc__
          __UpperCamelCase   = func_doc.split('\n'   )
          __UpperCamelCase   = 0
          while i < len(_lowercase   ) and re.search(r'^\s*Examples?:\s*$'				,     lines[i]   ) is None:
               i += 1
          if i < len(_lowercase   ):
               __UpperCamelCase   = example_docstring
               __UpperCamelCase   = "\n".join(_lowercase   )
          else:
               raise ValueError(
                   f'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
                   f'''current docstring is:\n{func_doc}'''   )
          __UpperCamelCase   = func_doc
          return fn

     return docstring_decorator






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

from transformers import BigBirdTokenizerFast


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






def 		_A		(		_lowercase				,     _lowercase=0				,     _lowercase=None   )				->   Union[str, Any]:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase=False   )				->   Tuple:


     """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(_lowercase				,     begin=1   ) + get_sub_answers(_lowercase				,     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(_lowercase   )






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=1				,     _lowercase=1_00   )				->   Any:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
     __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )

     __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(_lowercase   ).item()

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






def 		_A		(		_lowercase   )				->   List[str]:


     """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(_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",
               }

     __UpperCamelCase   = []
     for s in short_answers:
          short_targets.extend(s['text']   )
     __UpperCamelCase   = list(set(_lowercase   )   )

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

     __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 		_A		(		)				->   Optional[int]:


     """simple docstring"""


     __UpperCamelCase   = load_from_disk('natural-questions-validation'   )
     __UpperCamelCase   = dataset.map(_lowercase   ).remove_columns(['annotations', 'document', 'id']   )
     print(_lowercase   )

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

     __UpperCamelCase   = 'vasudevgupta/flax-bigbird-natural-questions'
     __UpperCamelCase   = FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase   )
     __UpperCamelCase   = BigBirdTokenizerFast.from_pretrained(_lowercase   )

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

     def evaluate(_lowercase   ):
          # 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=40_96				,     padding='max_length'				,     truncation=_lowercase				,     )

          __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = forward(**_lowercase   )

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

          __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=_lowercase   )
          __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(_lowercase   ) > 0

          return example

     __UpperCamelCase   = short_validation_dataset.map(_lowercase   )

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


if __name__ == "__main__":
    main()


