"""simple docstring"""







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



def       A__    (	lowercase_     :	Optional[int]      , lowercase_     :	Dict=0      , lowercase_     :	Any=None						)  ->							Union[str, Any]:
   return [" ".join(x.split(' '						)[begin:end]						) for x in answers if len(x.split(' '						)						) > 1]



def       A__    (	lowercase_     :	Tuple      , lowercase_     :	Optional[int]=False						)  ->							Union[str, 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"
      __lowerCamelCase    		=					(
          given_answers + get_sub_answers(lowercase_      , begin=1						) + get_sub_answers(lowercase_      , end=-1						)
      )
   __lowerCamelCase    		=					[]
   for answer in given_answers:
      __lowerCamelCase    		=					answer.replace('_'      , ' '						).lower()
      __lowerCamelCase    		=					''.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_     :	Tuple      , lowercase_     :	int      , lowercase_     :	List[str]=1      , lowercase_     :	int=100						)  ->							List[str]:
   __lowerCamelCase		,__lowerCamelCase    		=					jax.lax.top_k(lowercase_      , lowercase_						)
   __lowerCamelCase		,__lowerCamelCase    		=					jax.lax.top_k(lowercase_      , lowercase_						)

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

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



def       A__    (	lowercase_     :	List[str]						)  ->							str:
   __lowerCamelCase    		=					sample['question']['text']
   __lowerCamelCase    		=					sample['document']['tokens']['token']
   __lowerCamelCase    		=					sample['document']['tokens']['is_html']
   __lowerCamelCase    		=					sample['annotations']['long_answer']
   __lowerCamelCase    		=					sample['annotations']['short_answers']

   __lowerCamelCase    		=					' '.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",
         }

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

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

   __lowerCamelCase    		=					'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__    (	)  ->							Dict:
   __lowerCamelCase    		=					load_from_disk('natural-questions-validation'						)
   __lowerCamelCase    		=					dataset.map(lowercase_						).remove_columns(['annotations', 'document', 'id']						)
   print(lowercase_						)

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

   __lowerCamelCase    		=					'vasudevgupta/flax-bigbird-natural-questions'
   __lowerCamelCase    		=					FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					BigBirdTokenizerFast.from_pretrained(lowercase_						)

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

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

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					forward(**lowercase_						)

      __lowerCamelCase    		=					CATEGORY_MAPPING[category.item()]

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

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

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

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

      __lowerCamelCase    		=					expand_to_aliases(example['targets']      , make_sub_answers=lowercase_						)
      __lowerCamelCase    		=					expand_to_aliases(example['output']						)

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

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

      return example

   __lowerCamelCase    		=					short_validation_dataset.map(lowercase_						)

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


if __name__ == "__main__":
     main()







"""simple docstring"""







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'	)
      __lowerCamelCase    		=					'Hugging Face大法好，谁用谁知道。'
      __lowerCamelCase    		=					'Hugging Face大法好,谁用谁知道。<unk>'
      __lowerCamelCase    		=					'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

      __lowerCamelCase    		=					tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokens + [tokenizer.unk_token]

      __lowerCamelCase    		=					[13789, 13283, 1421, 8, 10, 1164, 13608, 16528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokenizer.decode(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)






