'''simple docstring'''
from math import sqrt







def 			_UpperCamelCase			(  _a						:						Tuple      ):




    """simple docstring"""



    __UpperCamelCase						:						List[Any]		     =	[1]
    for i in range(2					,  int(sqrt(UpperCAmelCase__      )      ) + 1					,  1      ):
        if number % i == 0:
            values.append(UpperCAmelCase__      )
            if int(number // i      ) != i:
                values.append(int(number // i      )      )
    return sorted(UpperCAmelCase__      )







def 			_UpperCamelCase			(  _a						:						Union[str, Any]      ):




    """simple docstring"""



    return sum(factors(UpperCAmelCase__      )      ) > n







def 			_UpperCamelCase			(  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	factors(UpperCAmelCase__      )
    __UpperCamelCase						:						Tuple		     =	len(UpperCAmelCase__      )
    __UpperCamelCase						:						str		     =	[[0 for i in range(number + 1      )] for j in range(r + 1      )]
    for i in range(r + 1      ):
        __UpperCamelCase						:						Dict		     =	True

    for i in range(1					,  number + 1      ):
        __UpperCamelCase						:						int		     =	False

    for i in range(1					,  r + 1      ):
        for j in range(1					,  number + 1      ):
            if j < values[i - 1]:
                __UpperCamelCase						:						Optional[int]		     =	subset[i - 1][j]
            else:
                __UpperCamelCase						:						List[str]		     =	subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

    return subset[r][number] != 0







def 			_UpperCamelCase			(  _a						:						Optional[int]      ):




    """simple docstring"""



    return abundant(UpperCAmelCase__      ) and not semi_perfect(UpperCAmelCase__      )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  for number in (6_9, 7_0, 7_1):
    print(F"""{number} is {"" if weird(number) else "not "}weird.""")






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

from transformers import BigBirdTokenizerFast


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


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=0		,       UpperCAmelCase__=None   ):

					"""simple docstring"""






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


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=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       =				(
										    given_answers + get_sub_answers(UpperCAmelCase__		,       begin=1   ) + get_sub_answers(UpperCAmelCase__		,       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(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=1		,       UpperCAmelCase__=100   ):

					"""simple docstring"""






					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )
					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )

					__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(UpperCAmelCase__   ).item()

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


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__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(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",
															}

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

					__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(UpperCAmelCase__   )   ) if not html[i]]   )
										if new_answer not in long_targets:
															long_targets.append(UpperCAmelCase__   )

					__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 							__lowercase     (	):

					"""simple docstring"""






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

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

					__lowerCAmelCase       =				'vasudevgupta/flax-bigbird-natural-questions'
					__lowerCAmelCase       =				FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase__   )
					__lowerCAmelCase       =				BigBirdTokenizerFast.from_pretrained(UpperCAmelCase__   )

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

					def evaluate(UpperCAmelCase__   ):
										# 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=4_096		,       padding='max_length'		,       truncation=UpperCAmelCase__		,       )

										__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				forward(**UpperCAmelCase__   )

										__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 1_024
										__lowerCAmelCase,						__lowerCAmelCase       =				get_best_valid_start_end_idx(
										    start_scores[0]		,       end_scores[0]		,       top_k=8		,       max_size=UpperCAmelCase__   )

										__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=UpperCAmelCase__   )
										__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(UpperCAmelCase__   ) > 0

										return example

					__lowerCAmelCase       =				short_validation_dataset.map(UpperCAmelCase__   )

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


if __name__ == "__main__":
		main()






