






"""simple docstring"""







__a							:						Union[str, Any]   							=			[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Tuple					,		__A:  Tuple     ):

												'''simple docstring'''

												a__ 							=	name
												a__ 							=	[]
												a__ 							=	[]




							def 			lowercase		( self:  Dict					,		__A:  List[str]     ):

												'''simple docstring'''

												self.inbound.append(__A     )




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												self.outbound.append(__A     )





							def __repr__( self:  Optional[int]     ):

												'''simple docstring'''

												return F'<node={self.name} inbound={self.inbound} outbound={self.outbound}>'





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=3			,  lowerCamelCase_=0.85):
					a__ 							=	{}
					for node in nodes:
										a__ 							=	1

					a__ 							=	{}
					for node in nodes:
										a__ 							=	len(node.outbound)

					for i in range(lowerCamelCase_):
										print(f'======= Iteration {i + 1} =======')
										for _, node in enumerate(lowerCamelCase_):
															a__ 							=	(1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound)
										print(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   ):
					a__ 							=	list(input('''Enter Names of the Nodes: ''').split())

					a__ 							=	[Node(lowerCamelCase_) for name in names]

					for ri, row in enumerate(lowerCamelCase_):
										for ci, col in enumerate(lowerCamelCase_):
															if col == 1:
																				nodes[ci].add_inbound(names[ri])
																				nodes[ri].add_outbound(names[ci])

					print('''======= Nodes =======''')
					for node in nodes:
										print(lowerCamelCase_)

					page_rank(lowerCamelCase_)


if __name__ == "__main__":
	main()










"""simple docstring"""







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

from transformers import BigBirdTokenizerFast


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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=0			,  lowerCamelCase_=None):
					return [" ".join(x.split(''' ''')[begin:end]) for x in answers if len(x.split(''' ''')) > 1]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=False):
					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(lowerCamelCase_			,  begin=1) + get_sub_answers(lowerCamelCase_			,  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(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_=1			,  lowerCamelCase_=100):
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)

					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(lowerCamelCase_).item()

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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					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(lowerCamelCase_)) 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(lowerCamelCase_))

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

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

					a__ 							=	dataset.filter(lambda lowerCamelCase_: (len(x['''question''']) + len(x['''context'''])) < 4 * 4096)
					a__ 							=	short_validation_dataset.filter(lambda lowerCamelCase_: x["category"] != "null")
					short_validation_dataset

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

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

					def evaluate(lowerCamelCase_):
										# 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=4096			,  padding='''max_length'''			,  truncation=lowerCamelCase_			,  )

										a__							,a__							,a__ 							=	forward(**lowerCamelCase_)

										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 1024
										a__							,a__ 							=	get_best_valid_start_end_idx(
										    start_scores[0]			,  end_scores[0]			,  top_k=8			,  max_size=lowerCamelCase_)

										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=lowerCamelCase_)
										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(lowerCamelCase_) > 0

										return example

					a__ 							=	short_validation_dataset.map(lowerCamelCase_)

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


if __name__ == "__main__":
	main()


