






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


def 					UpperCAmelCase			(				a__    ,			a__=0    ,			a__=None   ):





						'''simple docstring'''






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


def 					UpperCAmelCase			(				a__    ,			a__=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"
												lowerCAmelCase      :Tuple				  = (
												    given_answers + get_sub_answers(a__    ,			begin=1   ) + get_sub_answers(a__    ,			end=-1   )
												)
						lowerCAmelCase      :List[Any]				  = []
						for answer in given_answers:
												lowerCAmelCase      :str				  = answer.replace('_'    ,			' '   ).lower()
												lowerCAmelCase      :Tuple				  = ''.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 					UpperCAmelCase			(				a__    ,			a__    ,			a__=1    ,			a__=1_00   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = jax.lax.top_k(a__    ,			a__   )
						lowerCAmelCase      :Tuple				  = jax.lax.top_k(a__    ,			a__   )

						lowerCAmelCase      :Tuple				  = best_end_idx[:, None] - best_start_idx[None, :]
						lowerCAmelCase      :List[Any]				  = jnp.logical_or(widths < 0    ,			widths > max_size   )
						lowerCAmelCase      :str				  = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
						lowerCAmelCase      :Union[str, Any]				  = jnp.argmax(a__   ).item()

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


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






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

						lowerCAmelCase      :Optional[int]				  = ' '.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",
																		}

						lowerCAmelCase      :Union[str, Any]				  = []
						for s in short_answers:
												short_targets.extend(s['text']   )
						lowerCAmelCase      :int				  = list(set(a__   )   )

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

						lowerCAmelCase      :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'''






						lowerCAmelCase      :Union[str, Any]				  = load_from_disk('natural-questions-validation'   )
						lowerCAmelCase      :Optional[int]				  = dataset.map(a__   ).remove_columns(['annotations', 'document', 'id']   )
						print(a__   )

						lowerCAmelCase      :List[str]				  = dataset.filter(lambda a__   : (len(x['question']   ) + len(x['context']   )) < 4 * 40_96   )
						lowerCAmelCase      :Tuple				  = short_validation_dataset.filter(lambda a__   : x["category"] != "null"   )
						short_validation_dataset

						lowerCAmelCase      :Dict				  = 'vasudevgupta/flax-bigbird-natural-questions'
						lowerCAmelCase      :int				  = FlaxBigBirdForNaturalQuestions.from_pretrained(a__   )
						lowerCAmelCase      :List[Any]				  = BigBirdTokenizerFast.from_pretrained(a__   )

						@jax.jit
						def forward(*a__    ,			**a__   ):
												lowerCAmelCase      :Optional[int]				  = model(*a__    ,			**a__   )
												return start_logits, end_logits, jnp.argmax(a__    ,			axis=-1   )

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

												lowerCAmelCase      :Optional[int]				  = forward(**a__   )

												lowerCAmelCase      :int				  = CATEGORY_MAPPING[category.item()]

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

												if predicted_category in ["yes", "no", "null"]:
																		lowerCAmelCase      :Dict				  = [predicted_category]
																		lowerCAmelCase      :Optional[Any]				  = example['output'] == example['targets']
																		lowerCAmelCase      :List[str]				  = predicted_category != 'null'
																		return example

												lowerCAmelCase      :Tuple				  = 38 if predicted_category == 'short' else 10_24
												lowerCAmelCase      :int				  = get_best_valid_start_end_idx(
												    start_scores[0]    ,			end_scores[0]    ,			top_k=8    ,			max_size=a__   )

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

												lowerCAmelCase      :int				  = expand_to_aliases(example['targets']    ,			make_sub_answers=a__   )
												lowerCAmelCase      :str				  = expand_to_aliases(example['output']   )

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

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

												return example

						lowerCAmelCase      :Optional[int]				  = short_validation_dataset.map(a__   )

						lowerCAmelCase      :Tuple				  = len(a__   )
						lowerCAmelCase      :List[str]				  = len(short_validation_dataset.filter(lambda a__   : x["match"] == 1   )   )
						print('EM score:'    ,			(matched / total) * 1_00    ,			'%'   )


if __name__ == "__main__":
		main()







"""simple docstring"""


import safs




class 	__UpperCamelCase    (    safs.SaFileSystem	):
					pass