
"""simple docstring"""




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

from transformers import BigBirdTokenizerFast


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

def __snake_case (      UpperCamelCase						,					UpperCamelCase=0						,					UpperCamelCase=None    )       ->    List[Any]:


		"""simple docstring"""





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

def __snake_case (      UpperCamelCase						,					UpperCamelCase=False    )       ->    Optional[Any]:


		"""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"
				a__   		=     (
				    given_answers + get_sub_answers(UpperCamelCase						,					begin=1    ) + get_sub_answers(UpperCamelCase						,					end=-1    )
				)
		a__   		=     []
		for answer in given_answers:
				a__   		=     answer.replace('''_'''						,					''' '''    ).lower()
				a__   		=     ''''''.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 __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase=1						,					UpperCamelCase=100    )       ->    Optional[Any]:


		"""simple docstring"""





		a__		,	a__   		=     jax.lax.top_k(UpperCamelCase						,					UpperCamelCase    )
		a__		,	a__   		=     jax.lax.top_k(UpperCamelCase						,					UpperCamelCase    )

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

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

def __snake_case (      UpperCamelCase    )       ->    List[str]:


		"""simple docstring"""





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

		a__   		=     ''' '''.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",
						}

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

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

		a__   		=     '''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 __snake_case (      )       ->    Dict:


		"""simple docstring"""





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

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

		a__   		=     '''vasudevgupta/flax-bigbird-natural-questions'''
		a__   		=     FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCamelCase    )
		a__   		=     BigBirdTokenizerFast.from_pretrained(UpperCamelCase    )

		@jax.jit
		def forward(*UpperCamelCase						,					**UpperCamelCase    ):
				a__		,	a__		,	a__   		=     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
				a__   		=     tokenizer(
				    example['''question''']						,					example['''context''']						,					return_tensors='''np'''						,					max_length=4_096						,					padding='''max_length'''						,					truncation=UpperCamelCase						,					)

				a__		,	a__		,	a__   		=     forward(**UpperCamelCase    )

				a__   		=     CATEGORY_MAPPING[category.item()]

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

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

				a__   		=     38 if predicted_category == '''short''' else 1_024
				a__		,	a__   		=     get_best_valid_start_end_idx(
				    start_scores[0]						,					end_scores[0]						,					top_k=8						,					max_size=UpperCamelCase    )

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

				a__   		=     expand_to_aliases(example['''targets''']						,					make_sub_answers=UpperCamelCase    )
				a__   		=     expand_to_aliases(example['''output''']    )

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

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

				return example

		a__   		=     short_validation_dataset.map(UpperCamelCase    )

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


if __name__ == "__main__":
					main()






"""simple docstring"""




def __snake_case (      UpperCamelCase = 10**6    )       ->    int:


		"""simple docstring"""





		if n <= 0:
				raise ValueError('''Please enter an integer greater than 0'''    )

		a__   		=     list(range(n + 1    )    )
		for number in range(2						,					n + 1    ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2						,					n + 1						,					UpperCamelCase    ):
								a__   		=     (phi[multiple] // number) * (number - 1)

		a__   		=     1
		for number in range(1						,					n + 1    ):
				if (answer / phi[answer]) < (number / phi[number]):
						a__   		=     number

		return answer


if __name__ == "__main__":
					print(solution())




