




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



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Any=0,    __snake_case     :				Optional[int]=None  ):



				'''simple docstring'''





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



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



def 				a (						__snake_case     :				str,    __snake_case     :				List[Any],    __snake_case     :				int=1,    __snake_case     :				str=100  ):



				'''simple docstring'''





				UpperCAmelCase_			,UpperCAmelCase_					:Tuple      			=      jax.lax.top_k(__snake_case,    __snake_case  )
				UpperCAmelCase_			,UpperCAmelCase_					:Optional[Any]      			=      jax.lax.top_k(__snake_case,    __snake_case  )

				UpperCAmelCase_					:Union[str, Any]      			=      best_end_idx[:, None] - best_start_idx[None, :]
				UpperCAmelCase_					:int      			=      jnp.logical_or(widths < 0,    widths > max_size  )
				UpperCAmelCase_					:Union[str, Any]      			=      (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
				UpperCAmelCase_					:Tuple      			=      jnp.argmax(__snake_case  ).item()

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



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      sample['''question''']['''text''']
				UpperCAmelCase_					:Optional[Any]      			=      sample['''document''']['''tokens''']['''token''']
				UpperCAmelCase_					:Union[str, Any]      			=      sample['''document''']['''tokens''']['''is_html''']
				UpperCAmelCase_					:str      			=      sample['''annotations''']['''long_answer''']
				UpperCAmelCase_					:int      			=      sample['''annotations''']['''short_answers''']

				UpperCAmelCase_					:int      			=      ''' '''.join([context[i] for i in range(len(__snake_case  )  ) 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_					:int      			=      []
				for s in short_answers:
								short_targets.extend(s['''text''']  )
				UpperCAmelCase_					:Tuple      			=      list(set(__snake_case  )  )

				UpperCAmelCase_					:Any      			=      []
				for s in long_answers:
								if s["start_token"] == -1:
												continue
								UpperCAmelCase_					:Dict      			=      context[s['''start_token'''] : s['''end_token''']]
								UpperCAmelCase_					:Tuple      			=      is_html[s['''start_token'''] : s['''end_token''']]
								UpperCAmelCase_					:List[Any]      			=      ''' '''.join([answer[i] for i in range(len(__snake_case  )  ) if not html[i]]  )
								if new_answer not in long_targets:
												long_targets.append(__snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      '''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_					:List[Any]      			=      dataset.map(__snake_case  ).remove_columns(['''annotations''', '''document''', '''id''']  )
				print(__snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      dataset.filter(lambda __snake_case  : (len(x['''question''']  ) + len(x['''context''']  )) < 4 * 4096  )
				UpperCAmelCase_					:str      			=      short_validation_dataset.filter(lambda __snake_case  : x["category"] != "null"  )
				short_validation_dataset

				UpperCAmelCase_					:Tuple      			=      '''vasudevgupta/flax-bigbird-natural-questions'''
				UpperCAmelCase_					:Tuple      			=      FlaxBigBirdForNaturalQuestions.from_pretrained(__snake_case  )
				UpperCAmelCase_					:int      			=      BigBirdTokenizerFast.from_pretrained(__snake_case  )

				@jax.jit
				def forward(*__snake_case     :				List[str],    **__snake_case     :				Tuple  ):
								UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_					:List[Any]      			=      model(*__snake_case,    **__snake_case  )
								return start_logits, end_logits, jnp.argmax(__snake_case,    axis=-1  )

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

								UpperCAmelCase_			,UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      forward(**__snake_case  )

								UpperCAmelCase_					:List[Any]      			=      CATEGORY_MAPPING[category.item()]

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

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

								UpperCAmelCase_					:Any      			=      38 if predicted_category == '''short''' else 1024
								UpperCAmelCase_			,UpperCAmelCase_					:Any      			=      get_best_valid_start_end_idx(
								    start_scores[0],    end_scores[0],    top_k=8,    max_size=__snake_case  )

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

								UpperCAmelCase_					:Union[str, Any]      			=      expand_to_aliases(example['''targets'''],    make_sub_answers=__snake_case  )
								UpperCAmelCase_					:Dict      			=      expand_to_aliases(example['''output''']  )

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

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

								return example

				UpperCAmelCase_					:List[str]      			=      short_validation_dataset.map(__snake_case  )

				UpperCAmelCase_					:Union[str, Any]      			=      len(__snake_case  )
				UpperCAmelCase_					:int      			=      len(short_validation_dataset.filter(lambda __snake_case  : x["match"] == 1  )  )
				print('''EM score:''',    (matched / total) * 100,    '''%'''  )


if __name__ == "__main__":
						main()








"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      key
								UpperCAmelCase_					:str      			=      freq





				def __str__(							self      :  Optional[Any]     ):
								return f'Node(key={self.key}, freq={self.freq})'



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[int],    __snake_case     :				Tuple,    __snake_case     :				Any,    __snake_case     :				Dict,    __snake_case     :				List[str]  ):



				'''simple docstring'''





				if i > j or i < 0 or j > len(__snake_case  ) - 1:
								return

				UpperCAmelCase_					:List[str]      			=      root[i][j]
				if parent == -1:  # root does not have a parent
								print(f'{key[node]} is the root of the binary search tree.'  )
				elif is_left:
								print(f'{key[node]} is the left child of key {parent}.'  )
				else:
								print(f'{key[node]} is the right child of key {parent}.'  )

				print_binary_search_tree(__snake_case,    __snake_case,    __snake_case,    node - 1,    key[node],    __snake_case  )
				print_binary_search_tree(__snake_case,    __snake_case,    node + 1,    __snake_case,    key[node],    __snake_case  )



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





				nodes.sort(key=lambda __snake_case  : node.key  )

				UpperCAmelCase_					:List[str]      			=      len(__snake_case  )

				UpperCAmelCase_					:Any      			=      [nodes[i].key for i in range(__snake_case  )]
				UpperCAmelCase_					:int      			=      [nodes[i].freq for i in range(__snake_case  )]

				# This 2D array stores the overall tree cost (which's as minimized as possible);
				# for a single key, cost is equal to frequency of the key.
				UpperCAmelCase_					:Dict      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
				# array
				UpperCAmelCase_					:Optional[Any]      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# stores tree roots that will be used later for constructing binary search tree
				UpperCAmelCase_					:int      			=      [[i if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]

				for interval_length in range(2,    n + 1  ):
								for i in range(n - interval_length + 1  ):
												UpperCAmelCase_					:Union[str, Any]      			=      i + interval_length - 1

												UpperCAmelCase_					:Optional[int]      			=      sys.maxsize  # set the value to "infinity"
												UpperCAmelCase_					:List[Any]      			=      total[i][j - 1] + freqs[j]

												# Apply Knuth's optimization
												# Loop without optimization: for r in range(i, j + 1):
												for r in range(root[i][j - 1],    root[i + 1][j] + 1  ):  # r is a temporal root
																UpperCAmelCase_					:Any      			=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																UpperCAmelCase_					:Optional[int]      			=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																UpperCAmelCase_					:Union[str, Any]      			=      left + total[i][j] + right

																if dp[i][j] > cost:
																				UpperCAmelCase_					:Optional[Any]      			=      cost
																				UpperCAmelCase_					:Optional[int]      			=      r

				print('''Binary search tree nodes:'''  )
				for node in nodes:
								print(__snake_case  )

				print(f'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'  )
				print_binary_search_tree(__snake_case,    __snake_case,    0,    n - 1,    -1,    __snake_case  )



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      [Node(__snake_case,    randint(1,    50  )  ) for i in range(10,    0,    -1  )]
				find_optimal_binary_search_tree(__snake_case  )


if __name__ == "__main__":
						main()


