
'''simple docstring'''







import os

import jsonlines
import numpy as np
from tqdm import tqdm


_snake_case				:   Optional[Any]     =				2_048
_snake_case				:   Any     =				4_096
_snake_case				:   List[str]     =				42
_snake_case				:   int     =				os.environ.pop("""PROCESS_TRAIN""", """false""")
_snake_case				:   List[str]     =				{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
      def choose_first(_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False  ):
            assert isinstance(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
            if len(_SCREAMING_SNAKE_CASE  ) == 1:
                  _SCREAMING_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:
                        _SCREAMING_SNAKE_CASE				= {k: [a[k]] for k in a}
                  if len(a["start_token"]  ) > 0:
                        break
            return a

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

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

      _SCREAMING_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 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False  ):
      _SCREAMING_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
            _SCREAMING_SNAKE_CASE				= example["document"]["tokens"]
            _SCREAMING_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

      _SCREAMING_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

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

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

      # checking above code
      if assertion:
            _SCREAMING_SNAKE_CASE				= doc["is_html"][answer["start_token"] : answer["end_token"]]
            _SCREAMING_SNAKE_CASE				= doc["token"][answer["start_token"] : answer["end_token"]]
            _SCREAMING_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 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   int=2048	,			_SCREAMING_SNAKE_CASE		:   Tuple=4096	,			_SCREAMING_SNAKE_CASE		:   str=True  ):
      # overlap will be of doc_stride - q_len

      _SCREAMING_SNAKE_CASE				= get_context_and_ans(_SCREAMING_SNAKE_CASE	,			assertion=_SCREAMING_SNAKE_CASE  )
      _SCREAMING_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"],
                },
            }

      _SCREAMING_SNAKE_CASE				= tokenizer(example["question"]["text"]	,			out["context"]  ).input_ids
      _SCREAMING_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
            _SCREAMING_SNAKE_CASE				= []
            _SCREAMING_SNAKE_CASE				= []
            _SCREAMING_SNAKE_CASE				= input_ids[:q_len]
            _SCREAMING_SNAKE_CASE				= range(_SCREAMING_SNAKE_CASE	,			len(_SCREAMING_SNAKE_CASE  )	,			max_length - doc_stride  )
            for i in doc_start_indices:
                  _SCREAMING_SNAKE_CASE				= i + max_length - q_len
                  _SCREAMING_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,
                },
            }

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

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

      if assertion:
            _SCREAMING_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"],
                },
            }

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

      _SCREAMING_SNAKE_CASE				= []
      _SCREAMING_SNAKE_CASE				= []
      _SCREAMING_SNAKE_CASE				= []
      _SCREAMING_SNAKE_CASE				= []  # null, yes, no, long, short
      for i in doc_start_indices:
            _SCREAMING_SNAKE_CASE				= i + max_length - q_len
            _SCREAMING_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:
                  _SCREAMING_SNAKE_CASE				= start_token - i + q_len
                  _SCREAMING_SNAKE_CASE				= end_token - i + q_len
                  answers_category.append(answer["category"][0]  )  # ["short"] -> "short"
            else:
                  _SCREAMING_SNAKE_CASE				= -100
                  _SCREAMING_SNAKE_CASE				= -100
                  answers_category.append("null"  )
            _SCREAMING_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 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Dict=2048	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=4096	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False  ):
      _SCREAMING_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 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   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				= 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

      _snake_case				:   Union[str, Any]     =				load_dataset("""natural_questions""")
      _snake_case				:   Any     =				BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

      _snake_case				:   Optional[Any]     =				data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

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

      np.random.seed(SEED)
      _snake_case				:   List[str]     =				"""nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
      save_to_disk(data, file_name=cache_file_name)

'''simple docstring'''







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812



def 			_a		(	):
						print("Making key files..."  )
						make_key_files("rsa"	,			1024  )
						print("Key files generation successful."  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						print("Generating prime p..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						print("Generating prime q..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= p * q

						print("Generating e that is relatively prime to (p - 1) * (q - 1)..."  )
						while True:
												_SCREAMING_SNAKE_CASE				= random.randrange(2 ** (key_size - 1)	,			2 ** (key_size)  )
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  ) == 1:
																		break

						print("Calculating d that is mod inverse of e..."  )
						_SCREAMING_SNAKE_CASE				= cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  )

						_SCREAMING_SNAKE_CASE				= (n, e)
						_SCREAMING_SNAKE_CASE				= (n, d)
						return (public_key, private_key)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   int  ):
						if os.path.exists(F'{name}_pubkey.txt'  ) or os.path.exists(F'{name}_privkey.txt'  ):
												print("\nWARNING:"  )
												print(
												    F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= generate_key(_SCREAMING_SNAKE_CASE  )
						print(F'\nWriting public key to file {name}_pubkey.txt...'  )
						with open(F'{name}_pubkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'  )

						print(F'Writing private key to file {name}_privkey.txt...'  )
						with open(F'{name}_privkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{private_key[0]},{private_key[1]}'  )


if __name__ == "__main__":
						main()