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

from transformers import BigBirdTokenizerFast


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





def     UpperCAmelCase						(   lowercase							,      lowercase=0							,      lowercase=None							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							,      lowercase=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"
								__lowercase			      =							(
								    given_answers + get_sub_answers(lowercase							,      begin=1							) + get_sub_answers(lowercase							,      end=-1							)
								)
				__lowercase			      =							[]
				for answer in given_answers:
								__lowercase			      =							answer.replace('''_'''							,      ''' '''							).lower()
								__lowercase			      =							''''''.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     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=1							,      lowercase=100							):







				"""simple docstring"""



				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)
				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)

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

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





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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

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

				__lowercase			      =							[]
				for s in long_answers:
								if s["start_token"] == -1:
												continue
								__lowercase			      =							context[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							is_html[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							''' '''.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			      =							'''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     UpperCAmelCase						(   ):







				"""simple docstring"""



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

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

				__lowercase			      =							'''vasudevgupta/flax-bigbird-natural-questions'''
				__lowercase			      =							FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase							)
				__lowercase			      =							BigBirdTokenizerFast.from_pretrained(lowercase							)

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

				def evaluate(lowercase							):
								# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
								__lowercase			      =							tokenizer(
								    example['''question''']							,      example['''context''']							,      return_tensors='''np'''							,      max_length=4096							,      padding='''max_length'''							,      truncation=lowercase							,      )

								__lowercase      ,					__lowercase      ,					__lowercase			      =							forward(**lowercase							)

								__lowercase			      =							CATEGORY_MAPPING[category.item()]

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

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

								__lowercase			      =							38 if predicted_category == '''short''' else 1024
								__lowercase      ,					__lowercase			      =							get_best_valid_start_end_idx(
								    start_scores[0]							,      end_scores[0]							,      top_k=8							,      max_size=lowercase							)

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

								__lowercase			      =							expand_to_aliases(example['''targets''']							,      make_sub_answers=lowercase							)
								__lowercase			      =							expand_to_aliases(example['''output''']							)

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

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

								return example

				__lowercase			      =							short_validation_dataset.map(lowercase							)

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


if __name__ == "__main__":
		main()
__a  :					Union[str, Any]          =  [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							name
							__lowercase			      =							[]
							__lowercase			      =							[]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.inbound.append(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.outbound.append(lowerCAmelCase__						)





			def __repr__(       self						)					-> Tuple:

							'''simple docstring'''


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





def     UpperCAmelCase						(   lowercase							,      lowercase=3							,      lowercase=0.85							):







				"""simple docstring"""



				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							1

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

				for i in range(lowercase							):
								print(F"======= Iteration {i + 1} ======="							)
								for _, node in enumerate(lowercase							):
												__lowercase			      =							(1 - d) + d * sum(
												    ranks[ib] / outbounds[ib] for ib in node.inbound							)
								print(lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							list(input('''Enter Names of the Nodes: '''							).split()							)

				__lowercase			      =							[Node(lowercase							) for name in names]

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

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

				page_rank(lowercase							)


if __name__ == "__main__":
		main()