





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

from transformers import BigBirdTokenizerFast


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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Any      ,						_lowercase    :   Union[str, Any]=0      ,						_lowercase    :   Any=None )      ->       Optional[Any]:




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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Any      ,						_lowercase    :   str=False )      ->       Union[str, 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"
				lowercase__				:			int            =		(
				    given_answers + get_sub_answers(_lowercase      ,						begin=1 ) + get_sub_answers(_lowercase      ,						end=-1 )
				)
		lowercase__				:			str            =		[]
		for answer in given_answers:
				lowercase__				:			List[Any]            =		answer.replace('_'      ,						' ' ).lower()
				lowercase__				:			Optional[Any]            =		''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias )
				answers.append(' '.join(alias.split() ).strip() )
		return set(_lowercase )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[str]      ,						_lowercase    :   Dict      ,						_lowercase    :   Any=1      ,						_lowercase    :   Optional[int]=100 )      ->       str:




		'''simple docstring'''
		lowercase__      ,							lowercase__				:			Union[str, Any]            =		jax.lax.top_k(_lowercase      ,						_lowercase )
		lowercase__      ,							lowercase__				:			Union[str, Any]            =		jax.lax.top_k(_lowercase      ,						_lowercase )

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

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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[Any] )      ->       Union[str, Any]:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		sample['question']['text']
		lowercase__				:			Optional[int]            =		sample['document']['tokens']['token']
		lowercase__				:			Any            =		sample['document']['tokens']['is_html']
		lowercase__				:			Optional[int]            =		sample['annotations']['long_answer']
		lowercase__				:			List[Any]            =		sample['annotations']['short_answers']

		lowercase__				:			Optional[Any]            =		' '.join([context[i] for i in range(len(_lowercase ) ) 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",
						}

		lowercase__				:			Optional[int]            =		[]
		for s in short_answers:
				short_targets.extend(s['text'] )
		lowercase__				:			Optional[Any]            =		list(set(_lowercase ) )

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

		lowercase__				:			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 			SCREAMING_SNAKE_CASE__     (					)      ->       Optional[int]:




		'''simple docstring'''
		lowercase__				:			Tuple            =		load_from_disk('natural-questions-validation' )
		lowercase__				:			Optional[Any]            =		dataset.map(_lowercase ).remove_columns(['annotations', 'document', 'id'] )
		print(_lowercase )

		lowercase__				:			Optional[Any]            =		dataset.filter(lambda _lowercase : (len(x['question'] ) + len(x['context'] )) < 4 * 4_096 )
		lowercase__				:			List[Any]            =		short_validation_dataset.filter(lambda _lowercase : x["category"] != "null" )
		short_validation_dataset

		lowercase__				:			Optional[Any]            =		'vasudevgupta/flax-bigbird-natural-questions'
		lowercase__				:			Tuple            =		FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase )
		lowercase__				:			Optional[Any]            =		BigBirdTokenizerFast.from_pretrained(_lowercase )

		@jax.jit
		def forward(*_lowercase    :   int      ,						**_lowercase    :   List[str] ):
				lowercase__      ,							lowercase__      ,							lowercase__				:			Dict            =		model(*_lowercase      ,						**_lowercase )
				return start_logits, end_logits, jnp.argmax(_lowercase      ,						axis=-1 )

		def evaluate(_lowercase    :   List[str] ):
				# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
				lowercase__				:			Dict            =		tokenizer(
				    example['question']      ,						example['context']      ,						return_tensors='np'      ,						max_length=4_096      ,						padding='max_length'      ,						truncation=_lowercase      ,						)

				lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		forward(**_lowercase )

				lowercase__				:			Union[str, Any]            =		CATEGORY_MAPPING[category.item()]

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

				if predicted_category in ["yes", "no", "null"]:
						lowercase__				:			Optional[int]            =		[predicted_category]
						lowercase__				:			Tuple            =		example['output'] == example['targets']
						lowercase__				:			int            =		predicted_category != 'null'
						return example

				lowercase__				:			List[str]            =		38 if predicted_category == 'short' else 1_024
				lowercase__      ,							lowercase__				:			int            =		get_best_valid_start_end_idx(
				    start_scores[0]      ,						end_scores[0]      ,						top_k=8      ,						max_size=_lowercase )

				lowercase__				:			Optional[int]            =		inputs['input_ids'][0].tolist()
				lowercase__				:			Optional[Any]            =		[tokenizer.decode(input_ids[start_score : end_score + 1] )]

				lowercase__				:			List[str]            =		expand_to_aliases(example['targets']      ,						make_sub_answers=_lowercase )
				lowercase__				:			str            =		expand_to_aliases(example['output'] )

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

				# if there is a common element, it's a exact match
				lowercase__				:			Any            =		len(list(answers & predictions ) ) > 0
				lowercase__				:			Tuple            =		predicted_category != 'null' and len(_lowercase ) > 0

				return example

		lowercase__				:			Tuple            =		short_validation_dataset.map(_lowercase )

		lowercase__				:			Tuple            =		len(_lowercase )
		lowercase__				:			List[Any]            =		len(short_validation_dataset.filter(lambda _lowercase : x["match"] == 1 ) )
		print('EM score:'      ,						(matched / total) * 100      ,						'%' )


if __name__ == "__main__":
		main()









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


