



'''simple docstring'''





import os

import jsonlines
import numpy as np
from tqdm import tqdm


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





def 		__snake_case(							_lowerCAmelCase						)						->					Any:
      def choose_first(_lowerCAmelCase	,  _lowerCAmelCase=False						):
            assert isinstance(_lowerCAmelCase	,  _lowerCAmelCase						)
            if len(_lowerCAmelCase						) == 1:
                  snake_case__    :       Dict       		= 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__    :       Any       		= {k: [a[k]] for k in a}
                  if len(a["""start_token"""]						) > 0:
                        break
            return a

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

      # disregard some samples
      if len(answer["""start_token"""]						) > 1 or answer["start_token"] == answer["end_token"]:
            snake_case__    :       Dict       		= True
      else:
            snake_case__    :       List[str]       		= False

      snake_case__    :       Dict       		= ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
      if not all(isinstance(answer[k]	,  _lowerCAmelCase						) for k in cols						):
            raise ValueError("""Issue in ID"""	,  example["""id"""]						)

      return answer





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=False						)						->					Union[str, Any]:
      snake_case__    :       Optional[Any]       		= _get_single_answer(_lowerCAmelCase						)
      # 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__    :       Dict       		= example["""document"""]["""tokens"""]
            snake_case__    :       List[str]       		= []
            for i in range(len(doc["""token"""]						)						):
                  if not doc["is_html"][i]:
                        context.append(doc["""token"""][i]						)
            return {
                "context": " ".join(_lowerCAmelCase						),
                "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__    :       List[str]       		= ["""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__    :       List[Any]       		= example["""document"""]["""tokens"""]
      snake_case__    :       str       		= answer["""start_token"""]
      snake_case__    :       str       		= answer["""end_token"""]

      snake_case__    :       Optional[Any]       		= []
      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__    :       List[str]       		= """ """.join(context[start_token:end_token]						)

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

      return {
          "context": " ".join(_lowerCAmelCase						),
          "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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=2_048	,  _lowerCAmelCase=4_096	,  _lowerCAmelCase=True						)						->					int:
      # overlap will be of doc_stride - q_len

      snake_case__    :       str       		= get_context_and_ans(_lowerCAmelCase	,  assertion=_lowerCAmelCase						)
      snake_case__    :       Tuple       		= 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__    :       str       		= tokenizer(example["""question"""]["""text"""]	,  out["""context"""]						).input_ids
      snake_case__    :       Optional[Any]       		= 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__    :       int       		= []
            snake_case__    :       List[str]       		= []
            snake_case__    :       Optional[int]       		= input_ids[:q_len]
            snake_case__    :       List[Any]       		= range(_lowerCAmelCase	,  len(_lowerCAmelCase						)	,  max_length - doc_stride						)
            for i in doc_start_indices:
                  snake_case__    :       str       		= i + max_length - q_len
                  snake_case__    :       Any       		= 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(_lowerCAmelCase						),
                    "end_token": [-100] * len(_lowerCAmelCase						),
                    "category": category,
                },
            }

      snake_case__    :       Union[str, Any]       		= out["""context"""].split()
      snake_case__    :       List[Any]       		= splitted_context[answer["""end_token"""]]
      snake_case__    :       Optional[int]       		= len(
          tokenizer(
              """ """.join(splitted_context[: answer["""start_token"""]]						)	,  add_special_tokens=_lowerCAmelCase	,  ).input_ids						)
      snake_case__    :       Union[str, Any]       		= len(
          tokenizer(""" """.join(splitted_context[: answer["""end_token"""]]						)	,  add_special_tokens=_lowerCAmelCase						).input_ids						)

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

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

      snake_case__    :       List[str]       		= input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
      snake_case__    :       Dict       		= answer["""start_token"""]
      snake_case__    :       str       		= answer["""end_token"""]

      if assertion:
            snake_case__    :       List[str]       		= tokenizer.decode(_lowerCAmelCase						)
            if answer["span"] != new:
                  print("""ISSUE IN TOKENIZATION"""						)
                  print("""OLD:"""	,  answer["""span"""]						)
                  print("""NEW:"""	,  _lowerCAmelCase	,  end="""\n\n"""						)

      if len(_lowerCAmelCase						) <= 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__    :       str       		= input_ids[:q_len]
      snake_case__    :       List[Any]       		= range(_lowerCAmelCase	,  len(_lowerCAmelCase						)	,  max_length - doc_stride						)

      snake_case__    :       Union[str, Any]       		= []
      snake_case__    :       Dict       		= []
      snake_case__    :       Tuple       		= []
      snake_case__    :       Any       		= []  # null, yes, no, long, short
      for i in doc_start_indices:
            snake_case__    :       Optional[int]       		= i + max_length - q_len
            snake_case__    :       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:
                  snake_case__    :       int       		= start_token - i + q_len
                  snake_case__    :       Optional[Any]       		= end_token - i + q_len
                  answers_category.append(answer["""category"""][0]						)  # ["short"] -> "short"
            else:
                  snake_case__    :       List[Any]       		= -100
                  snake_case__    :       Optional[Any]       		= -100
                  answers_category.append("""null"""						)
            snake_case__    :       Any       		= inputs[-1][start_token : end_token + 1]

            answers_start_token.append(_lowerCAmelCase						)
            answers_end_token.append(_lowerCAmelCase						)
            if assertion:
                  if new != old and new != [tokenizer.cls_token_id]:
                        print("""ISSUE in strided for ID:"""	,  example["""id"""]						)
                        print("""New:"""	,  tokenizer.decode(_lowerCAmelCase						)						)
                        print("""Old:"""	,  tokenizer.decode(_lowerCAmelCase						)	,  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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=2_048	,  _lowerCAmelCase=4_096	,  _lowerCAmelCase=False						)						->					Optional[int]:
      snake_case__    :       Optional[Any]       		= get_strided_contexts_and_ans(
          _lowerCAmelCase	,  _lowerCAmelCase	,  doc_stride=_lowerCAmelCase	,  max_length=_lowerCAmelCase	,  assertion=_lowerCAmelCase	,  )

      return example





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      with jsonlines.open(_lowerCAmelCase	,  """a"""						) as writer:
            for example in tqdm(_lowerCAmelCase	,  total=len(_lowerCAmelCase						)	,  desc="""Saving samples ... """						):
                  snake_case__    :       Tuple       		= 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

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

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

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

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





'''simple docstring'''





# flake8: noqa
# Lint as: python3
__a  			=						[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
