



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

from transformers import BigBirdTokenizerFast


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

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: int=0			,       _UpperCAmelCase							: int=None						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: Optional[Any]=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"
				__UpperCAmelCase   :					Dict													=     (
				    given_answers + get_sub_answers(_UpperCAmelCase			,       begin=1						) + get_sub_answers(_UpperCAmelCase			,       end=-1						)
				)
		__UpperCAmelCase   :					Any													=     []
		for answer in given_answers:
				__UpperCAmelCase   :					Any													=     answer.replace('''_'''			,       ''' '''						).lower()
				__UpperCAmelCase   :					Optional[int]													=     ''''''.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							: str			,       _UpperCAmelCase							: Dict			,       _UpperCAmelCase							: Union[str, Any]=1			,       _UpperCAmelCase							: Union[str, Any]=1_00						):







		'''simple docstring'''





		__UpperCAmelCase							, __UpperCAmelCase   :					Tuple													=     jax.lax.top_k(_UpperCAmelCase			,       _UpperCAmelCase						)
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[Any]													=     jax.lax.top_k(_UpperCAmelCase			,       _UpperCAmelCase						)

		__UpperCAmelCase   :					Any													=     best_end_idx[:, None] - best_start_idx[None, :]
		__UpperCAmelCase   :					Dict													=     jnp.logical_or(widths < 0			,       widths > max_size						)
		__UpperCAmelCase   :					Optional[Any]													=     (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
		__UpperCAmelCase   :					List[Any]													=     jnp.argmax(_UpperCAmelCase						).item()

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

def 	a						(				_UpperCAmelCase							: Union[str, Any]						):







		'''simple docstring'''





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

		__UpperCAmelCase   :					str													=     ''' '''.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",
						}

		__UpperCAmelCase   :					Tuple													=     []
		for s in short_answers:
				short_targets.extend(s['''text''']						)
		__UpperCAmelCase   :					Optional[Any]													=     list(set(_UpperCAmelCase						)						)

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

		__UpperCAmelCase   :					List[str]													=     '''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						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					List[Any]													=     load_from_disk('''natural-questions-validation'''						)
		__UpperCAmelCase   :					Dict													=     dataset.map(_UpperCAmelCase						).remove_columns(['''annotations''', '''document''', '''id''']						)
		print(_UpperCAmelCase						)

		__UpperCAmelCase   :					Dict													=     dataset.filter(lambda _UpperCAmelCase						: (len(x['''question''']						) + len(x['''context''']						)) < 4 * 40_96						)
		__UpperCAmelCase   :					Optional[int]													=     short_validation_dataset.filter(lambda _UpperCAmelCase						: x["category"] != "null"						)
		short_validation_dataset

		__UpperCAmelCase   :					str													=     '''vasudevgupta/flax-bigbird-natural-questions'''
		__UpperCAmelCase   :					Dict													=     FlaxBigBirdForNaturalQuestions.from_pretrained(_UpperCAmelCase						)
		__UpperCAmelCase   :					str													=     BigBirdTokenizerFast.from_pretrained(_UpperCAmelCase						)

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

		def evaluate(_UpperCAmelCase							: Union[str, Any]						):
				# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
				__UpperCAmelCase   :					List[Any]													=     tokenizer(
				    example['''question''']			,       example['''context''']			,       return_tensors='''np'''			,       max_length=40_96			,       padding='''max_length'''			,       truncation=_UpperCAmelCase			,       )

				__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					str													=     forward(**_UpperCAmelCase						)

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

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

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

				__UpperCAmelCase   :					str													=     38 if predicted_category == '''short''' else 10_24
				__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     get_best_valid_start_end_idx(
				    start_scores[0]			,       end_scores[0]			,       top_k=8			,       max_size=_UpperCAmelCase						)

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

				__UpperCAmelCase   :					Optional[Any]													=     expand_to_aliases(example['''targets''']			,       make_sub_answers=_UpperCAmelCase						)
				__UpperCAmelCase   :					str													=     expand_to_aliases(example['''output''']						)

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

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

				return example

		__UpperCAmelCase   :					int													=     short_validation_dataset.map(_UpperCAmelCase						)

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


if __name__ == "__main__":
				main()










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





