






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

from transformers import BigBirdTokenizerFast


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





def 		A_			(		lowercase_					,       lowercase_=0					,       lowercase_=None	)							->int:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_=False	)							->Dict:







 """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"
  SCREAMING_SNAKE_CASE							  =						(
      given_answers + get_sub_answers(lowercase_					,       begin=1	) + get_sub_answers(lowercase_					,       end=-1	)
  )
 SCREAMING_SNAKE_CASE							  =						[]
 for answer in given_answers:
  SCREAMING_SNAKE_CASE							  =						answer.replace('_'					,       ' '	).lower()
  SCREAMING_SNAKE_CASE							  =						"".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 		A_			(		lowercase_					,       lowercase_					,       lowercase_=1					,       lowercase_=1_0_0	)							->Dict:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						jax.lax.top_k(lowercase_					,       lowercase_	)
 SCREAMING_SNAKE_CASE							  =						jax.lax.top_k(lowercase_					,       lowercase_	)

 SCREAMING_SNAKE_CASE							  =						best_end_idx[:, None] - best_start_idx[None, :]
 SCREAMING_SNAKE_CASE							  =						jnp.logical_or(widths < 0					,       widths > max_size	)
 SCREAMING_SNAKE_CASE							  =						(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
 SCREAMING_SNAKE_CASE							  =						jnp.argmax(lowercase_	).item()

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





def 		A_			(		lowercase_	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						sample["question"]["text"]
 SCREAMING_SNAKE_CASE							  =						sample["document"]["tokens"]["token"]
 SCREAMING_SNAKE_CASE							  =						sample["document"]["tokens"]["is_html"]
 SCREAMING_SNAKE_CASE							  =						sample["annotations"]["long_answer"]
 SCREAMING_SNAKE_CASE							  =						sample["annotations"]["short_answers"]

 SCREAMING_SNAKE_CASE							  =						" ".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",
   }

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

 SCREAMING_SNAKE_CASE							  =						[]
 for s in long_answers:
  if s["start_token"] == -1:
   continue
  SCREAMING_SNAKE_CASE							  =						context[s["start_token"] : s["end_token"]]
  SCREAMING_SNAKE_CASE							  =						is_html[s["start_token"] : s["end_token"]]
  SCREAMING_SNAKE_CASE							  =						" ".join([answer[i] for i in range(len(lowercase_	)	) if not html[i]]	)
  if new_answer not in long_targets:
   long_targets.append(lowercase_	)

 SCREAMING_SNAKE_CASE							  =						"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_			(		)							->Dict:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE							  =						dataset.filter(lambda lowercase_	: (len(x['question']	) + len(x['context']	)) < 4 * 4_0_9_6	)
 SCREAMING_SNAKE_CASE							  =						short_validation_dataset.filter(lambda lowercase_	: x["category"] != "null"	)
 short_validation_dataset

 SCREAMING_SNAKE_CASE							  =						"vasudevgupta/flax-bigbird-natural-questions"
 SCREAMING_SNAKE_CASE							  =						FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						BigBirdTokenizerFast.from_pretrained(lowercase_	)

 @jax.jit
 def forward(*lowercase_					,       **lowercase_	):
  SCREAMING_SNAKE_CASE							  =						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
  SCREAMING_SNAKE_CASE							  =						tokenizer(
      example['question']					,       example['context']					,       return_tensors='np'					,       max_length=4_0_9_6					,       padding='max_length'					,       truncation=lowercase_					,       )

  SCREAMING_SNAKE_CASE							  =						forward(**lowercase_	)

  SCREAMING_SNAKE_CASE							  =						CATEGORY_MAPPING[category.item()]

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

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

  SCREAMING_SNAKE_CASE							  =						3_8 if predicted_category == "short" else 1_0_2_4
  SCREAMING_SNAKE_CASE							  =						get_best_valid_start_end_idx(
      start_scores[0]					,       end_scores[0]					,       top_k=8					,       max_size=lowercase_	)

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

  SCREAMING_SNAKE_CASE							  =						expand_to_aliases(example['targets']					,       make_sub_answers=lowercase_	)
  SCREAMING_SNAKE_CASE							  =						expand_to_aliases(example['output']	)

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

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

  return example

 SCREAMING_SNAKE_CASE							  =						short_validation_dataset.map(lowercase_	)

 SCREAMING_SNAKE_CASE							  =						len(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						len(short_validation_dataset.filter(lambda lowercase_	: x["match"] == 1	)	)
 print('EM score:'					,       (matched / total) * 1_0_0					,       '%'	)


if __name__ == "__main__":
  main()












import math
import sys
class 					a_:



     """simple docstring"""



     def __init__(						self			:					Optional[int])    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						{}  # To store the pos of node in array





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Optional[int]:

      """simple docstring"""
      return self.cur_size == 0





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.left(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.right(lowerCAmelCase__)
      if lc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						lc
      else:
       SCREAMING_SNAKE_CASE							  =						idx
      if rc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						rc
      if smallest != idx:
       self.swap(lowerCAmelCase__       ,       lowerCAmelCase__)
       self.min_heapify(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					Tuple)    ->					Optional[Any]:

      """simple docstring"""
      # Inserts a node into the Priority Queue
      SCREAMING_SNAKE_CASE							  =						self.cur_size
      self.cur_size += 1
      self.array.append((sys.maxsize, tup[1]))
      self.decrease_key((sys.maxsize, tup[1])       ,       tup[0])





     def   __UpperCamelCase		(						self			:					Dict)    ->					Optional[Any]:

      """simple docstring"""
      # Removes and returns the min element at top of priority queue
      SCREAMING_SNAKE_CASE							  =						self.array[0][1]
      SCREAMING_SNAKE_CASE							  =						self.array[self.cur_size - 1]
      self.cur_size -= 1
      self.min_heapify(1)
      del self.pos[min_node]
      return min_node





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Dict)    ->					int:

      """simple docstring"""
      # returns the index of left child
      return 2 * i + 1





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					List[str])    ->					Optional[Any]:

      """simple docstring"""
      # returns the index of right child
      return 2 * i + 2





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					List[Any])    ->					Dict:

      """simple docstring"""
      # returns the index of parent
      return math.floor(i / 2)





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

      """simple docstring"""
      # swaps array elements at indices i and j
      # update the pos{}
      SCREAMING_SNAKE_CASE							  =						j
      SCREAMING_SNAKE_CASE							  =						i
      SCREAMING_SNAKE_CASE							  =						self.array[i]
      SCREAMING_SNAKE_CASE							  =						self.array[j]
      SCREAMING_SNAKE_CASE							  =						temp





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					Optional[Any])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      SCREAMING_SNAKE_CASE							  =						(new_d, tup[1])
      while idx > 0 and self.array[self.par(lowerCAmelCase__)][0] > self.array[idx][0]:
       self.swap(lowerCAmelCase__       ,       self.par(lowerCAmelCase__))
       SCREAMING_SNAKE_CASE							  =						self.par(lowerCAmelCase__)

class 					a_:



     """simple docstring"""



     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					str)    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						{}  # To store graph: u -> (v,w)
      SCREAMING_SNAKE_CASE							  =						num  # Number of nodes in graph
      # To store the distance from source vertex
      SCREAMING_SNAKE_CASE							  =						[0] * self.num_nodes
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes  # To store the path





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					List[Any])    ->					Tuple:

      """simple docstring"""
      #  Edge going from node u to v and v to u with weight w
      # u (w)-> v, v (w) -> u
      # Check if u already in graph
      if u in self.adjList:
       self.adjList[u].append((v, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(v, w)]

      # Assuming undirected graph
      if v in self.adjList:
       self.adjList[v].append((u, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(u, w)]





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Tuple:

      """simple docstring"""
      # u -> v(w)
      for u in self.adjList:
       print(lowerCAmelCase__       ,       '->'       ,       ' -> '.join(str(f'''{v}({w})''') for v, w in self.adjList[u]))





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					List[str])    ->					List[Any]:

      """simple docstring"""
      # Flush old junk values in par[]
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes
      # src is the source node
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						PriorityQueue()
      q.insert((0, src))  # (dist from src, node)
      for u in self.adjList:
       if u != src:
        SCREAMING_SNAKE_CASE							  =						sys.maxsize  # Infinity
        SCREAMING_SNAKE_CASE							  =						-1

      while not q.is_empty():
       SCREAMING_SNAKE_CASE							  =						q.extract_min()  # Returns node with the min dist from source
       # Update the distance of all the neighbours of u and
       # if their prev dist was INFINITY then push them in Q
       for v, w in self.adjList[u]:
        SCREAMING_SNAKE_CASE							  =						self.dist[u] + w
        if self.dist[v] > new_dist:
         if self.dist[v] == sys.maxsize:
          q.insert((new_dist, v))
         else:
          q.decrease_key((self.dist[v], v)       ,       lowerCAmelCase__)
         SCREAMING_SNAKE_CASE							  =						new_dist
         SCREAMING_SNAKE_CASE							  =						u

        # Show the shortest distances from src
      self.show_distances(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					List[str])    ->					Tuple:

      """simple docstring"""
      print(f'''Distance from node: {src}''')
      for u in range(self.num_nodes):
       print(f'''Node {u} has distance: {self.dist[u]}''')





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Any)    ->					Optional[int]:

      """simple docstring"""
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
       path.append(lowerCAmelCase__)
       if temp != src:
        for v, w in self.adjList[temp]:
         if v == self.par[temp]:
          cost += w
          break
       SCREAMING_SNAKE_CASE							  =						self.par[temp]
      path.append(lowerCAmelCase__)
      path.reverse()

      print(f'''----Path to reach {dest} from {src}----''')
      for u in path:
       print(f'''{u}'''       ,       end=' ')
       if u != dest:
        print('-> '       ,       end='')

      print('\nTotal cost of path: '       ,       lowerCAmelCase__)


if __name__ == "__main__":
      __UpperCAmelCase      				=							Graph(9)
      graph.add_edge(0, 1, 4)
      graph.add_edge(0, 7, 8)
      graph.add_edge(1, 2, 8)
      graph.add_edge(1, 7, 1_1)
      graph.add_edge(2, 3, 7)
      graph.add_edge(2, 8, 2)
      graph.add_edge(2, 5, 4)
      graph.add_edge(3, 4, 9)
      graph.add_edge(3, 5, 1_4)
      graph.add_edge(4, 5, 1_0)
      graph.add_edge(5, 6, 2)
      graph.add_edge(6, 7, 1)
      graph.add_edge(6, 8, 6)
      graph.add_edge(7, 8, 7)
      graph.show_graph()
      graph.dijkstra(0)
      graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21




