





"""simple docstring"""
import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowerCamelCase_								=		2_0_4_8
lowerCamelCase_								=		4_0_9_6
lowerCamelCase_								=		4_2
lowerCamelCase_								=		os.environ.pop("PROCESS_TRAIN", "false")
lowerCamelCase_								=		{"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}



def      __lowerCamelCase   (							a_     :		str							)			->      List[str]:






      def choose_first(a_     :		str ,   a_     :		List[Any]=False							):
            assert isinstance(SCREAMING_SNAKE_CASE__ ,   SCREAMING_SNAKE_CASE__							)
            if len(SCREAMING_SNAKE_CASE__							) == 1:
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  answer[0]
                  return {k: [answer[k]] for k in answer} if is_long_answer else answer
            for a in answer:
                  if is_long_answer:
                        __SCREAMING_SNAKE_CASE   :Tuple						=  {k: [a[k]] for k in a}
                  if len(a['''start_token''']							) > 0:
                        break
            return a

      __SCREAMING_SNAKE_CASE   :List[Any]						=  {'''id''': example['''id''']}
      __SCREAMING_SNAKE_CASE   :Dict						=  example['''annotations''']
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  annotation['''yes_no_answer''']
      if 0 in yes_no_answer or 1 in yes_no_answer:
            __SCREAMING_SNAKE_CASE   :int						=  ['''yes'''] if 1 in yes_no_answer else ['''no''']
            __SCREAMING_SNAKE_CASE   :Tuple						=  []
            __SCREAMING_SNAKE_CASE   :Tuple						=  []
            __SCREAMING_SNAKE_CASE   :Tuple						=  ['''<cls>''']
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  ['''short''']
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  choose_first(annotation['''short_answers''']							)
            if len(out['''start_token''']							) == 0:
                  # answer will be long if short is not available
                  __SCREAMING_SNAKE_CASE   :Tuple						=  ['''long''']
                  __SCREAMING_SNAKE_CASE   :Dict						=  choose_first(annotation['''long_answer'''] ,   is_long_answer=SCREAMING_SNAKE_CASE__							)
                  __SCREAMING_SNAKE_CASE   :Optional[Any]						=  []
            answer.update(SCREAMING_SNAKE_CASE__							)

      # disregard some samples
      if len(answer['''start_token''']							) > 1 or answer["start_token"] == answer["end_token"]:
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  True
      else:
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  False

      __SCREAMING_SNAKE_CASE   :List[str]						=  ['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
      if not all(isinstance(answer[k] ,   SCREAMING_SNAKE_CASE__							) for k in cols							):
            raise ValueError('''Issue in ID''' ,   example['''id''']							)

      return answer



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[Any]=False							)			->      int:
      __SCREAMING_SNAKE_CASE   :int						=  _get_single_answer(SCREAMING_SNAKE_CASE__							)
      # bytes are of no use
      del answer["start_byte"]
      del answer["end_byte"]

      # handle yes_no answers explicitly
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            __SCREAMING_SNAKE_CASE   :str						=  example['''document''']['''tokens''']
            __SCREAMING_SNAKE_CASE   :str						=  []
            for i in range(len(doc['''token''']							)							):
                  if not doc["is_html"][i]:
                        context.append(doc['''token'''][i]							)
            return {
                "context": " ".join(SCREAMING_SNAKE_CASE__							),
                "answer": {
                    "start_token": -1_00,  # ignore index in cross-entropy
                    "end_token": -1_00,  # ignore index in cross-entropy
                    "category": answer["category"],
                    "span": answer["category"],  # extra
                },
            }

      # later, help in removing all no answers
      if answer["start_token"] == [-1]:
            return {
                "context": "None",
                "answer": {
                    "start_token": -1,
                    "end_token": -1,
                    "category": "null",
                    "span": "None",  # extra
                },
            }

      # handling normal samples

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  ['''start_token''', '''end_token''']
      answer.update({k: answer[k][0] if len(answer[k]							) > 0 else answer[k] for k in cols}							)  # e.g. [10] == 10

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  example['''document''']['''tokens''']
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  answer['''start_token''']
      __SCREAMING_SNAKE_CASE   :List[Any]						=  answer['''end_token''']

      __SCREAMING_SNAKE_CASE   :int						=  []
      for i in range(len(doc['''token''']							)							):
            if not doc["is_html"][i]:
                  context.append(doc['''token'''][i]							)
            else:
                  if answer["start_token"] > i:
                        start_token -= 1
                  if answer["end_token"] > i:
                        end_token -= 1
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  ''' '''.join(context[start_token:end_token]							)

      # checking above code
      if assertion:
            __SCREAMING_SNAKE_CASE   :List[str]						=  doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
            __SCREAMING_SNAKE_CASE   :int						=  doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
            __SCREAMING_SNAKE_CASE   :Any						=  ''' '''.join([old[i] for i in range(len(SCREAMING_SNAKE_CASE__							)							) if not is_html[i]]							)
            if new != old:
                  print('''ID:''' ,   example['''id''']							)
                  print('''New:''' ,   SCREAMING_SNAKE_CASE__ ,   end='''\n'''							)
                  print('''Old:''' ,   SCREAMING_SNAKE_CASE__ ,   end='''\n\n'''							)

      return {
          "context": " ".join(SCREAMING_SNAKE_CASE__							),
          "answer": {
              "start_token": start_token,
              "end_token": end_token - 1,  # this makes it inclusive
              "category": answer["category"],  # either long or short
              "span": new,  # extra
          },
      }



def      __lowerCamelCase   (							a_     :		Optional[int] ,   a_     :		Optional[Any] ,   a_     :		Optional[Any]=20_48 ,   a_     :		str=40_96 ,   a_     :		Optional[int]=True							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  get_context_and_ans(SCREAMING_SNAKE_CASE__ ,   assertion=SCREAMING_SNAKE_CASE__							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  out['''answer''']

      # later, removing these samples
      if answer["start_token"] == -1:
            return {
                "example_id": example["id"],
                "input_ids": [[-1]],
                "labels": {
                    "start_token": [-1],
                    "end_token": [-1],
                    "category": ["null"],
                },
            }

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  tokenizer(example['''question''']['''text'''] ,   out['''context''']							).input_ids
      __SCREAMING_SNAKE_CASE   :Dict						=  input_ids.index(tokenizer.sep_token_id							) + 1

      # return yes/no
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  []
            __SCREAMING_SNAKE_CASE   :int						=  []
            __SCREAMING_SNAKE_CASE   :int						=  input_ids[:q_len]
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  range(SCREAMING_SNAKE_CASE__ ,   len(SCREAMING_SNAKE_CASE__							) ,   max_length - doc_stride							)
            for i in doc_start_indices:
                  __SCREAMING_SNAKE_CASE   :int						=  i + max_length - q_len
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  input_ids[i:end_index]
                  inputs.append(q_indices + slice							)
                  category.append(answer['''category'''][0]							)
                  if slice[-1] == tokenizer.sep_token_id:
                        break

            return {
                "example_id": example["id"],
                "input_ids": inputs,
                "labels": {
                    "start_token": [-1_00] * len(SCREAMING_SNAKE_CASE__							),
                    "end_token": [-1_00] * len(SCREAMING_SNAKE_CASE__							),
                    "category": category,
                },
            }

      __SCREAMING_SNAKE_CASE   :int						=  out['''context'''].split()
      __SCREAMING_SNAKE_CASE   :int						=  splitted_context[answer['''end_token''']]
      __SCREAMING_SNAKE_CASE   :Dict						=  len(
          tokenizer(
              ''' '''.join(splitted_context[: answer['''start_token''']]							) ,   add_special_tokens=SCREAMING_SNAKE_CASE__ ,   ).input_ids							)
      __SCREAMING_SNAKE_CASE   :str						=  len(
          tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]							) ,   add_special_tokens=SCREAMING_SNAKE_CASE__							).input_ids							)

      answer["start_token"] += q_len
      answer["end_token"] += q_len

      # fixing end token
      __SCREAMING_SNAKE_CASE   :int						=  len(tokenizer(SCREAMING_SNAKE_CASE__ ,   add_special_tokens=SCREAMING_SNAKE_CASE__							).input_ids							)
      if num_sub_tokens > 1:
            answer["end_token"] += num_sub_tokens - 1

      __SCREAMING_SNAKE_CASE   :int						=  input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
      __SCREAMING_SNAKE_CASE   :List[str]						=  answer['''start_token''']
      __SCREAMING_SNAKE_CASE   :Tuple						=  answer['''end_token''']

      if assertion:
            __SCREAMING_SNAKE_CASE   :Any						=  tokenizer.decode(SCREAMING_SNAKE_CASE__							)
            if answer["span"] != new:
                  print('''ISSUE IN TOKENIZATION'''							)
                  print('''OLD:''' ,   answer['''span''']							)
                  print('''NEW:''' ,   SCREAMING_SNAKE_CASE__ ,   end='''\n\n'''							)

      if len(SCREAMING_SNAKE_CASE__							) <= max_length:
            return {
                "example_id": example["id"],
                "input_ids": [input_ids],
                "labels": {
                    "start_token": [answer["start_token"]],
                    "end_token": [answer["end_token"]],
                    "category": answer["category"],
                },
            }

      __SCREAMING_SNAKE_CASE   :Dict						=  input_ids[:q_len]
      __SCREAMING_SNAKE_CASE   :Tuple						=  range(SCREAMING_SNAKE_CASE__ ,   len(SCREAMING_SNAKE_CASE__							) ,   max_length - doc_stride							)

      __SCREAMING_SNAKE_CASE   :str						=  []
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[Any]						=  []  # null, yes, no, long, short
      for i in doc_start_indices:
            __SCREAMING_SNAKE_CASE   :int						=  i + max_length - q_len
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  input_ids[i:end_index]
            inputs.append(q_indices + slice							)
            assert len(inputs[-1]							) <= max_length, "Issue in truncating length"

            if start_token >= i and end_token <= end_index - 1:
                  __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  start_token - i + q_len
                  __SCREAMING_SNAKE_CASE   :int						=  end_token - i + q_len
                  answers_category.append(answer['''category'''][0]							)  # ["short"] -> "short"
            else:
                  __SCREAMING_SNAKE_CASE   :Optional[Any]						=  -1_00
                  __SCREAMING_SNAKE_CASE   :Tuple						=  -1_00
                  answers_category.append('''null'''							)
            __SCREAMING_SNAKE_CASE   :Tuple						=  inputs[-1][start_token : end_token + 1]

            answers_start_token.append(SCREAMING_SNAKE_CASE__							)
            answers_end_token.append(SCREAMING_SNAKE_CASE__							)
            if assertion:
                  if new != old and new != [tokenizer.cls_token_id]:
                        print('''ISSUE in strided for ID:''' ,   example['''id''']							)
                        print('''New:''' ,   tokenizer.decode(SCREAMING_SNAKE_CASE__							)							)
                        print('''Old:''' ,   tokenizer.decode(SCREAMING_SNAKE_CASE__							) ,   end='''\n\n'''							)
            if slice[-1] == tokenizer.sep_token_id:
                  break

      return {
          "example_id": example["id"],
          "input_ids": inputs,
          "labels": {
              "start_token": answers_start_token,
              "end_token": answers_end_token,
              "category": answers_category,
          },
      }



def      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		List[Any] ,   a_     :		Optional[int]=20_48 ,   a_     :		Optional[int]=40_96 ,   a_     :		Tuple=False							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  get_strided_contexts_and_ans(
          SCREAMING_SNAKE_CASE__ ,   SCREAMING_SNAKE_CASE__ ,   doc_stride=SCREAMING_SNAKE_CASE__ ,   max_length=SCREAMING_SNAKE_CASE__ ,   assertion=SCREAMING_SNAKE_CASE__ ,   )

      return example



def      __lowerCamelCase   (							a_     :		Optional[Any] ,   a_     :		List[str]							)			->      Optional[Any]:






      with jsonlines.open(SCREAMING_SNAKE_CASE__ ,   '''a'''							) as writer:
            for example in tqdm(SCREAMING_SNAKE_CASE__ ,   total=len(SCREAMING_SNAKE_CASE__							) ,   desc='''Saving samples ... '''							):
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  example['''labels''']
                  for ids, start, end, cat in zip(
                      example['''input_ids'''] ,   labels['''start_token'''] ,   labels['''end_token'''] ,   labels['''category'''] ,   ):
                        if start == -1 and end == -1:
                              continue  # leave waste samples with no answer
                        if cat == "null" and np.random.rand() < 0.6:
                              continue  # removing 50 % samples
                        writer.write(
                            {
                                '''input_ids''': ids,
                                '''start_token''': start,
                                '''end_token''': end,
                                '''category''': CATEGORY_MAPPING[cat],
                            }							)


if __name__ == "__main__":
  from datasets import load_dataset

  from transformers import BigBirdTokenizer

  lowerCamelCase_								=		load_dataset("natural_questions")
  lowerCamelCase_								=		BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

  lowerCamelCase_								=		data["train" if PROCESS_TRAIN == "true" else "validation"]

  lowerCamelCase_								=		{
      "tokenizer": tokenizer,
      "doc_stride": DOC_STRIDE,
      "max_length": MAX_LENGTH,
      "assertion": False,
  }
  lowerCamelCase_								=		data.map(prepare_inputs, fn_kwargs=fn_kwargs)
  lowerCamelCase_								=		data.remove_columns(["annotations", "document", "id", "question"])
  print(data)

  np.random.seed(SEED)
  lowerCamelCase_								=		"nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
  save_to_disk(data, file_name=cache_file_name)


def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[0] * (w + 1) for _ in range(n + 1      )]

    for i in range(1    ,			n + 1      ):
        for w_ in range(1    ,			w + 1      ):
            if wt[i - 1] <= w_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            'The number of weights must be the same as the number of values.\n'
            f'But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__      )} values'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )
    for i in range(SCREAMING_SNAKE_CASE__      ):
        if not isinstance(wt[i]    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowercase_		,			lowercase_       		=  knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)

