




"""simple docstring"""



import os

import jsonlines
import numpy as np
from tqdm import tqdm


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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 def choose_first(lowercase_      ,			lowercase_=False						):
  assert isinstance(lowercase_      ,			lowercase_						)
  if len(lowercase_						) == 1:
   A__	=    answer[0]
   return {k: [answer[k]] for k in answer} if is_long_answer else answer
  for a in answer:
   if is_long_answer:
    A__	=    {k: [a[k]] for k in a}
   if len(a["start_token"]						) > 0:
    break
  return a

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

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

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

 return answer







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=False						)						->		Any:
 A__	=    _get_single_answer(lowercase_						)
 # 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
  A__	=    example["document"]["tokens"]
  A__	=    []
  for i in range(len(doc["token"]						)						):
   if not doc["is_html"][i]:
    context.append(doc["token"][i]						)
  return {
      "context": " ".join(lowercase_						),
      "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

 A__	=    ["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

 A__	=    example["document"]["tokens"]
 A__	=    answer["start_token"]
 A__	=    answer["end_token"]

 A__	=    []
 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
 A__	=    " ".join(context[start_token:end_token]						)

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

 return {
     "context": " ".join(lowercase_						),
     "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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_=20_48      ,			lowercase_=40_96      ,			lowercase_=True						)						->		Tuple:
 # overlap will be of doc_stride - q_len

 A__	=    get_context_and_ans(lowercase_      ,			assertion=lowercase_						)
 A__	=    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"],
      },
  }

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

 # return yes/no
 if answer["category"][0] in ["yes", "no"]:  # category is list with one element
  A__	=    []
  A__	=    []
  A__	=    input_ids[:q_len]
  A__	=    range(lowercase_      ,			len(lowercase_						)      ,			max_length - doc_stride						)
  for i in doc_start_indices:
   A__	=    i + max_length - q_len
   A__	=    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(lowercase_						),
          "end_token": [-1_00] * len(lowercase_						),
          "category": category,
      },
  }

 A__	=    out["context"].split()
 A__	=    splitted_context[answer["end_token"]]
 A__	=    len(
     tokenizer(
         " ".join(splitted_context[: answer["start_token"]]						)      ,			add_special_tokens=lowercase_      ,			).input_ids						)
 A__	=    len(
     tokenizer(" ".join(splitted_context[: answer["end_token"]]						)      ,			add_special_tokens=lowercase_						).input_ids						)

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

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

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

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

 if len(lowercase_						) <= 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"],
      },
  }

 A__	=    input_ids[:q_len]
 A__	=    range(lowercase_      ,			len(lowercase_						)      ,			max_length - doc_stride						)

 A__	=    []
 A__	=    []
 A__	=    []
 A__	=    []  # null, yes, no, long, short
 for i in doc_start_indices:
  A__	=    i + max_length - q_len
  A__	=    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:
   A__	=    start_token - i + q_len
   A__	=    end_token - i + q_len
   answers_category.append(answer["category"][0]						)  # ["short"] -> "short"
  else:
   A__	=    -1_00
   A__	=    -1_00
   answers_category.append("null"						)
  A__	=    inputs[-1][start_token : end_token + 1]

  answers_start_token.append(lowercase_						)
  answers_end_token.append(lowercase_						)
  if assertion:
   if new != old and new != [tokenizer.cls_token_id]:
    print("ISSUE in strided for ID:"      ,			example["id"]						)
    print("New:"      ,			tokenizer.decode(lowercase_						)						)
    print("Old:"      ,			tokenizer.decode(lowercase_						)      ,			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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_=20_48      ,			lowercase_=40_96      ,			lowercase_=False						)						->		List[Any]:
 A__	=    get_strided_contexts_and_ans(
     lowercase_      ,			lowercase_      ,			doc_stride=lowercase_      ,			max_length=lowercase_      ,			assertion=lowercase_      ,			)

 return example







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Optional[Any]:
 with jsonlines.open(lowercase_      ,			"a"						) as writer:
  for example in tqdm(lowercase_      ,			total=len(lowercase_						)      ,			desc="Saving samples ... "						):
   A__	=    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

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

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

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

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











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE    =	[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE    =	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





