




'''simple docstring'''





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

from transformers import BigBirdTokenizerFast


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





def 			__a			(     _UpperCamelCase:					Dict      ,		_UpperCamelCase:					Union[str, Any]=0      ,		_UpperCamelCase:					Optional[Any]=None					)		->				Any:

     """simple docstring"""




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





def 			__a			(     _UpperCamelCase:					Optional[int]      ,		_UpperCamelCase:					str=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"
          _snake_case							=   (
              given_answers + get_sub_answers(_UpperCamelCase      ,		begin=1					) + get_sub_answers(_UpperCamelCase      ,		end=-1					)
          )
     _snake_case							=   []
     for answer in given_answers:
          _snake_case							=   answer.replace("_"      ,		" "					).lower()
          _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(_UpperCamelCase					)





def 			__a			(     _UpperCamelCase:					Tuple      ,		_UpperCamelCase:					Dict      ,		_UpperCamelCase:					int=1      ,		_UpperCamelCase:					Optional[int]=100					)		->				Any:

     """simple docstring"""




     _snake_case							,      _snake_case							=   jax.lax.top_k(_UpperCamelCase      ,		_UpperCamelCase					)
     _snake_case							,      _snake_case							=   jax.lax.top_k(_UpperCamelCase      ,		_UpperCamelCase					)

     _snake_case							=   best_end_idx[:, None] - best_start_idx[None, :]
     _snake_case							=   jnp.logical_or(widths < 0      ,		widths > max_size					)
     _snake_case							=   (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
     _snake_case							=   jnp.argmax(_UpperCamelCase					).item()

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





def 			__a			(     _UpperCamelCase:					Dict					)		->				int:

     """simple docstring"""




     _snake_case							=   sample["question"]["text"]
     _snake_case							=   sample["document"]["tokens"]["token"]
     _snake_case							=   sample["document"]["tokens"]["is_html"]
     _snake_case							=   sample["annotations"]["long_answer"]
     _snake_case							=   sample["annotations"]["short_answers"]

     _snake_case							=   " ".join([context[i] for i in range(len(_UpperCamelCase					)					) 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",
               }

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

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

     _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 			__a			(     )		->				Optional[Any]:

     """simple docstring"""




     _snake_case							=   load_from_disk("natural-questions-validation"					)
     _snake_case							=   dataset.map(_UpperCamelCase					).remove_columns(["annotations", "document", "id"]					)
     print(_UpperCamelCase					)

     _snake_case							=   dataset.filter(lambda _UpperCamelCase					: (len(x["question"]					) + len(x["context"]					)) < 4 * 4_096					)
     _snake_case							=   short_validation_dataset.filter(lambda _UpperCamelCase					: x["category"] != "null"					)
     short_validation_dataset

     _snake_case							=   "vasudevgupta/flax-bigbird-natural-questions"
     _snake_case							=   FlaxBigBirdForNaturalQuestions.from_pretrained(_UpperCamelCase					)
     _snake_case							=   BigBirdTokenizerFast.from_pretrained(_UpperCamelCase					)

     @jax.jit
     def forward(*_UpperCamelCase:					Any      ,		**_UpperCamelCase:					int					):
          _snake_case							,      _snake_case							,      _snake_case							=   model(*_UpperCamelCase      ,		**_UpperCamelCase					)
          return start_logits, end_logits, jnp.argmax(_UpperCamelCase      ,		axis=-1					)

     def evaluate(_UpperCamelCase:					Optional[Any]					):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          _snake_case							=   tokenizer(
              example["question"]      ,		example["context"]      ,		return_tensors="np"      ,		max_length=4_096      ,		padding="max_length"      ,		truncation=_UpperCamelCase      ,		)

          _snake_case							,      _snake_case							,      _snake_case							=   forward(**_UpperCamelCase					)

          _snake_case							=   CATEGORY_MAPPING[category.item()]

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

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

          _snake_case							=   38 if predicted_category == "short" else 1_024
          _snake_case							,      _snake_case							=   get_best_valid_start_end_idx(
              start_scores[0]      ,		end_scores[0]      ,		top_k=8      ,		max_size=_UpperCamelCase					)

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

          _snake_case							=   expand_to_aliases(example["targets"]      ,		make_sub_answers=_UpperCamelCase					)
          _snake_case							=   expand_to_aliases(example["output"]					)

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

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

          return example

     _snake_case							=   short_validation_dataset.map(_UpperCamelCase					)

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


if __name__ == "__main__":
   main()








'''simple docstring'''





UpperCamelCase_		:    Optional[Any]									=		'''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

UpperCamelCase_		:    List[Any]									=		[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]


