






"""simple docstring"""





from __future__ import annotations






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       assert len(snake_case_						) == len(snake_case_						)

       lowercase__:							int         =     len(snake_case_						)
       lowercase__:							Dict         =     list(range(snake_case_						)						)
       lowercase__:							Any         =     [-1] * n  # who the donor has donated to
       lowercase__:							List[str]         =     [-1] * n  # who the recipient has received from
       lowercase__:							Optional[Any]         =     [0] * n

       while unmatched_donors:
              lowercase__:							Dict         =     unmatched_donors[0]
              lowercase__:							Optional[Any]         =     donor_pref[donor]
              lowercase__:							Optional[Any]         =     donor_preference[num_donations[donor]]
              num_donations[donor] += 1
              lowercase__:							Any         =     recipient_pref[recipient]
              lowercase__:							Tuple         =     rec_record[recipient]

              if prev_donor != -1:
                     if rec_preference.index(snake_case_						) > rec_preference.index(snake_case_						):
                            lowercase__:							List[Any]         =     donor
                            lowercase__:							int         =     recipient
                            unmatched_donors.append(snake_case_						)
                            unmatched_donors.remove(snake_case_						)
              else:
                     lowercase__:							List[Any]         =     donor
                     lowercase__:							Optional[int]         =     recipient
                     unmatched_donors.remove(snake_case_						)
       return donor_record












'''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 					UpperCamelCase						(	snake_case						,  snake_case=0						,  snake_case=None ):




   '''simple docstring'''

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


def 					UpperCamelCase						(	snake_case						,  snake_case=False ):




   '''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"
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			(
          given_answers + get_sub_answers(snake_case						,  begin=1 ) + get_sub_answers(snake_case						,  end=-1 )
      )
   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for answer in given_answers:
      SCREAMING_SNAKE_CASE__      :					int							    =			answer.replace('''_'''						,  ''' ''' ).lower()
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias )
      answers.append(''' '''.join(alias.split() ).strip() )
   return set(snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case=1						,  snake_case=1_00 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			jax.lax.top_k(snake_case						,  snake_case )
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			jax.lax.top_k(snake_case						,  snake_case )

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			best_end_idx[:, None] - best_start_idx[None, :]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			jnp.logical_or(widths < 0						,  widths > max_size )
   SCREAMING_SNAKE_CASE__      :					Any							    =			(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
   SCREAMING_SNAKE_CASE__      :					str							    =			jnp.argmax(snake_case ).item()

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


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			sample['''question''']['''text''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			sample['''document''']['''tokens''']['''token''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''document''']['''tokens''']['''is_html''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''annotations''']['''long_answer''']
   SCREAMING_SNAKE_CASE__      :					str							    =			sample['''annotations''']['''short_answers''']

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([context[i] for i in range(len(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",
         }

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
   for s in short_answers:
      short_targets.extend(s['''text'''] )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			list(set(snake_case ) )

   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for s in long_answers:
      if s["start_token"] == -1:
         continue
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			context[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					Dict							    =			is_html[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([answer[i] for i in range(len(snake_case ) ) if not html[i]] )
      if new_answer not in long_targets:
         long_targets.append(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			'''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 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			load_from_disk('''natural-questions-validation''' )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dataset.map(snake_case ).remove_columns(['''annotations''', '''document''', '''id'''] )
   print(snake_case )

   SCREAMING_SNAKE_CASE__      :					Dict							    =			dataset.filter(lambda snake_case : (len(x['''question'''] ) + len(x['''context'''] )) < 4 * 40_96 )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			short_validation_dataset.filter(lambda snake_case : x["category"] != "null" )
   short_validation_dataset

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			'''vasudevgupta/flax-bigbird-natural-questions'''
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			FlaxBigBirdForNaturalQuestions.from_pretrained(snake_case )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			BigBirdTokenizerFast.from_pretrained(snake_case )

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

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

      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			forward(**snake_case )

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			CATEGORY_MAPPING[category.item()]

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			example['''long'''] + example['''short''']
      if example["category"] in ["yes", "no", "null"]:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[example['''category''']]
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			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__      :					str							    =			[predicted_category]
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			example['''output'''] == example['''targets''']
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			predicted_category != '''null'''
         return example

      SCREAMING_SNAKE_CASE__      :					Any							    =			38 if predicted_category == '''short''' else 10_24
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_best_valid_start_end_idx(
          start_scores[0]						,  end_scores[0]						,  top_k=8						,  max_size=snake_case )

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

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			expand_to_aliases(example['''targets''']						,  make_sub_answers=snake_case )
      SCREAMING_SNAKE_CASE__      :					Any							    =			expand_to_aliases(example['''output'''] )

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

      # if there is a common element, it's a exact match
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			len(list(answers & predictions ) ) > 0
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			predicted_category != '''null''' and len(snake_case ) > 0

      return example

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			short_validation_dataset.map(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			len(snake_case )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(short_validation_dataset.filter(lambda snake_case : x["match"] == 1 ) )
   print('''EM score:'''						,  (matched / total) * 1_00						,  '''%''' )


if __name__ == "__main__":
       main()

