

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 					a				(      A__     :       List[Any]				,       A__     :       Any=0				,       A__     :       Optional[int]=None					)   ->				List[Any]:





      """simple docstring"""



      return [" ".join(x.split(' '					)[begin:end]					) for x in answers if len(x.split(' '					)					) > 1]
def 					a				(      A__     :       List[str]				,       A__     :       Any=False					)   ->				Optional[int]:





      """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"
            _lowercase     	=(
                given_answers + get_sub_answers(A__				,       begin=1					) + get_sub_answers(A__				,       end=-1					)
            )
      _lowercase     	=[]
      for answer in given_answers:
            _lowercase     	=answer.replace('_'				,       ' '					).lower()
            _lowercase     	=''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias					)
            answers.append(' '.join(alias.split()					).strip()					)
      return set(A__					)
def 					a				(      A__     :       str				,       A__     :       str				,       A__     :       Union[str, Any]=1				,       A__     :       Union[str, Any]=100					)   ->				str:





      """simple docstring"""



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

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

      return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
def 					a				(      A__     :       int					)   ->				Any:





      """simple docstring"""



      _lowercase     	=sample['question']['text']
      _lowercase     	=sample['document']['tokens']['token']
      _lowercase     	=sample['document']['tokens']['is_html']
      _lowercase     	=sample['annotations']['long_answer']
      _lowercase     	=sample['annotations']['short_answers']

      _lowercase     	=' '.join([context[i] for i in range(len(A__					)					) 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",
                  }

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

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

      _lowercase     	='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				(      )   ->				str:





      """simple docstring"""



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

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

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

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

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

            _lowercase	,	_lowercase	,	_lowercase     	=forward(**A__					)

            _lowercase     	=CATEGORY_MAPPING[category.item()]

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

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

            _lowercase     	=38 if predicted_category == 'short' else 1024
            _lowercase	,	_lowercase     	=get_best_valid_start_end_idx(
                start_scores[0]				,       end_scores[0]				,       top_k=8				,       max_size=A__					)

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

            _lowercase     	=expand_to_aliases(example['targets']				,       make_sub_answers=A__					)
            _lowercase     	=expand_to_aliases(example['output']					)

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

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

            return example

      _lowercase     	=short_validation_dataset.map(A__					)

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


if __name__ == "__main__":
  main()



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


