


'''simple docstring'''





import os

import jsonlines
import numpy as np
from tqdm import tqdm


UpperCAmelCase    =					2048
UpperCAmelCase    =					4096
UpperCAmelCase    =					42
UpperCAmelCase    =					os.environ.pop("""PROCESS_TRAIN""", """false""")
UpperCAmelCase    =					{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Union[str, Any]:




      """simple docstring"""

      def choose_first(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      ):
            assert isinstance(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
            if len(SCREAMING_SNAKE_CASE      ) == 1:
                  snake_case_ =   answer[0]
                  return {k: [answer[k]] for k in answer} if is_long_answer else answer
            for a in answer:
                  if is_long_answer:
                        snake_case_ =   {k: [a[k]] for k in a}
                  if len(a['''start_token''']      ) > 0:
                        break
            return a

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

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

      snake_case_ =   ['''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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						int:




      """simple docstring"""

      snake_case_ =   _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
            snake_case_ =   example['''document''']['''tokens''']
            snake_case_ =   []
            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": -100,  # ignore index in cross-entropy
                    "end_token": -100,  # 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

      snake_case_ =   ['''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

      snake_case_ =   example['''document''']['''tokens''']
      snake_case_ =   answer['''start_token''']
      snake_case_ =   answer['''end_token''']

      snake_case_ =   []
      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
      snake_case_ =   ''' '''.join(context[start_token:end_token]      )

      # checking above code
      if assertion:
            snake_case_ =   doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
            snake_case_ =   doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
            snake_case_ =   ''' '''.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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=2048      ,      SCREAMING_SNAKE_CASE=4096      ,      SCREAMING_SNAKE_CASE=True      )->						int:




      """simple docstring"""

      snake_case_ =   get_context_and_ans(SCREAMING_SNAKE_CASE      ,      assertion=SCREAMING_SNAKE_CASE      )
      snake_case_ =   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"],
                },
            }

      snake_case_ =   tokenizer(example['''question''']['''text''']      ,      out['''context''']      ).input_ids
      snake_case_ =   input_ids.index(tokenizer.sep_token_id      ) + 1

      # return yes/no
      if answer["category"][0] in ["yes", "no"]:  # category is list with one element
            snake_case_ =   []
            snake_case_ =   []
            snake_case_ =   input_ids[:q_len]
            snake_case_ =   range(SCREAMING_SNAKE_CASE      ,      len(SCREAMING_SNAKE_CASE      )      ,      max_length - doc_stride      )
            for i in doc_start_indices:
                  snake_case_ =   i + max_length - q_len
                  snake_case_ =   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": [-100] * len(SCREAMING_SNAKE_CASE      ),
                    "end_token": [-100] * len(SCREAMING_SNAKE_CASE      ),
                    "category": category,
                },
            }

      snake_case_ =   out['''context'''].split()
      snake_case_ =   splitted_context[answer['''end_token''']]
      snake_case_ =   len(
          tokenizer(
              ''' '''.join(splitted_context[: answer['''start_token''']]      )      ,      add_special_tokens=SCREAMING_SNAKE_CASE      ,      ).input_ids      )
      snake_case_ =   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
      snake_case_ =   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

      snake_case_ =   input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
      snake_case_ =   answer['''start_token''']
      snake_case_ =   answer['''end_token''']

      if assertion:
            snake_case_ =   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"],
                },
            }

      snake_case_ =   input_ids[:q_len]
      snake_case_ =   range(SCREAMING_SNAKE_CASE      ,      len(SCREAMING_SNAKE_CASE      )      ,      max_length - doc_stride      )

      snake_case_ =   []
      snake_case_ =   []
      snake_case_ =   []
      snake_case_ =   []  # null, yes, no, long, short
      for i in doc_start_indices:
            snake_case_ =   i + max_length - q_len
            snake_case_ =   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:
                  snake_case_ =   start_token - i + q_len
                  snake_case_ =   end_token - i + q_len
                  answers_category.append(answer['''category'''][0]      )  # ["short"] -> "short"
            else:
                  snake_case_ =   -100
                  snake_case_ =   -100
                  answers_category.append('''null'''      )
            snake_case_ =   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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=2048      ,      SCREAMING_SNAKE_CASE=4096      ,      SCREAMING_SNAKE_CASE=False      )->						Optional[Any]:




      """simple docstring"""

      snake_case_ =   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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      with jsonlines.open(SCREAMING_SNAKE_CASE      ,      '''a'''      ) as writer:
            for example in tqdm(SCREAMING_SNAKE_CASE      ,      total=len(SCREAMING_SNAKE_CASE      )      ,      desc='''Saving samples ... '''      ):
                  snake_case_ =   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

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

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

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

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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))