




import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


SCREAMING_SNAKE_CASE:    List[Any]											=   {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
SCREAMING_SNAKE_CASE:    Optional[Any]											=   set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''\"''']))


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=0						,	lowerCAmelCase=None   )->  Optional[Any]:
  return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=False   )->  Union[str, Any]:
  if make_sub_answers:
    # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
    # *e.g.* if the correct answer contains a prefix such as "the", or "a"
    SCREAMING_SNAKE_CASE_							=       (
        given_answers + get_sub_answers(lowerCAmelCase						,	begin=1   ) + get_sub_answers(lowerCAmelCase						,	end=-1   )
    )
  SCREAMING_SNAKE_CASE_							=       []
  for answer in given_answers:
    SCREAMING_SNAKE_CASE_							=       answer.replace('_'						,	' '   ).lower()
    SCREAMING_SNAKE_CASE_							=       ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
    answers.append(' '.join(alias.split()   ).strip()   )
  return set(lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=1						,	lowerCAmelCase=100   )->  str:
  SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       jax.lax.top_k(lowerCAmelCase						,	lowerCAmelCase   )
  SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       jax.lax.top_k(lowerCAmelCase						,	lowerCAmelCase   )

  SCREAMING_SNAKE_CASE_							=       best_end_idx[:, None] - best_start_idx[None, :]
  SCREAMING_SNAKE_CASE_							=       jnp.logical_or(widths < 0						,	widths > max_size   )
  SCREAMING_SNAKE_CASE_							=       (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
  SCREAMING_SNAKE_CASE_							=       jnp.argmax(lowerCAmelCase   ).item()

  return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]


def 				_a       (				lowerCAmelCase   )->  Union[str, Any]:
  SCREAMING_SNAKE_CASE_							=       sample['question']['text']
  SCREAMING_SNAKE_CASE_							=       sample['document']['tokens']['token']
  SCREAMING_SNAKE_CASE_							=       sample['document']['tokens']['is_html']
  SCREAMING_SNAKE_CASE_							=       sample['annotations']['long_answer']
  SCREAMING_SNAKE_CASE_							=       sample['annotations']['short_answers']

  SCREAMING_SNAKE_CASE_							=       ' '.join([context[i] for i in range(len(lowerCAmelCase   )   ) if not is_html[i]]   )

  # 0 - No ; 1 - Yes
  for answer in sample["annotations"]["yes_no_answer"]:
    if answer == 0 or answer == 1:
      return {
          "question": question,
          "context": context_string,
          "short": [],
          "long": [],
          "category": "no" if answer == 0 else "yes",
      }

  SCREAMING_SNAKE_CASE_							=       []
  for s in short_answers:
    short_targets.extend(s['text']   )
  SCREAMING_SNAKE_CASE_							=       list(set(lowerCAmelCase   )   )

  SCREAMING_SNAKE_CASE_							=       []
  for s in long_answers:
    if s["start_token"] == -1:
      continue
    SCREAMING_SNAKE_CASE_							=       context[s['start_token'] : s['end_token']]
    SCREAMING_SNAKE_CASE_							=       is_html[s['start_token'] : s['end_token']]
    SCREAMING_SNAKE_CASE_							=       ' '.join([answer[i] for i in range(len(lowerCAmelCase   )   ) if not html[i]]   )
    if new_answer not in long_targets:
      long_targets.append(lowerCAmelCase   )

  SCREAMING_SNAKE_CASE_							=       'long_short' if len(short_targets + long_targets   ) > 0 else 'null'

  return {
      "question": question,
      "context": context_string,
      "short": short_targets,
      "long": long_targets,
      "category": category,
  }


def 				_a       (				)->  Optional[int]:
  SCREAMING_SNAKE_CASE_							=       load_from_disk('natural-questions-validation'   )
  SCREAMING_SNAKE_CASE_							=       dataset.map(lowerCAmelCase   ).remove_columns(['annotations', 'document', 'id']   )
  print(lowerCAmelCase   )

  SCREAMING_SNAKE_CASE_							=       dataset.filter(lambda lowerCAmelCase   : (len(x['question']   ) + len(x['context']   )) < 4 * 4096   )
  SCREAMING_SNAKE_CASE_							=       short_validation_dataset.filter(lambda lowerCAmelCase   : x["category"] != "null"   )
  short_validation_dataset

  SCREAMING_SNAKE_CASE_							=       'vasudevgupta/flax-bigbird-natural-questions'
  SCREAMING_SNAKE_CASE_							=       FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCAmelCase   )
  SCREAMING_SNAKE_CASE_							=       BigBirdTokenizerFast.from_pretrained(lowerCAmelCase   )

  @jax.jit
  def forward(*lowerCAmelCase						,	**lowerCAmelCase   ):
    SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       model(*lowerCAmelCase						,	**lowerCAmelCase   )
    return start_logits, end_logits, jnp.argmax(lowerCAmelCase						,	axis=-1   )

  def evaluate(lowerCAmelCase   ):
    # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
    SCREAMING_SNAKE_CASE_							=       tokenizer(
        example['question']						,	example['context']						,	return_tensors='np'						,	max_length=4096						,	padding='max_length'						,	truncation=lowerCAmelCase						,	)

    SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       forward(**lowerCAmelCase   )

    SCREAMING_SNAKE_CASE_							=       CATEGORY_MAPPING[category.item()]

    SCREAMING_SNAKE_CASE_							=       example['long'] + example['short']
    if example["category"] in ["yes", "no", "null"]:
      SCREAMING_SNAKE_CASE_							=       [example['category']]
    SCREAMING_SNAKE_CASE_							=       example['category'] != 'null'
    # Now target can be: "yes", "no", "null", "list of long & short answers"

    if predicted_category in ["yes", "no", "null"]:
      SCREAMING_SNAKE_CASE_							=       [predicted_category]
      SCREAMING_SNAKE_CASE_							=       example['output'] == example['targets']
      SCREAMING_SNAKE_CASE_							=       predicted_category != 'null'
      return example

    SCREAMING_SNAKE_CASE_							=       38 if predicted_category == 'short' else 1024
    SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       get_best_valid_start_end_idx(
        start_scores[0]						,	end_scores[0]						,	top_k=8						,	max_size=lowerCAmelCase   )

    SCREAMING_SNAKE_CASE_							=       inputs['input_ids'][0].tolist()
    SCREAMING_SNAKE_CASE_							=       [tokenizer.decode(input_ids[start_score : end_score + 1]   )]

    SCREAMING_SNAKE_CASE_							=       expand_to_aliases(example['targets']						,	make_sub_answers=lowerCAmelCase   )
    SCREAMING_SNAKE_CASE_							=       expand_to_aliases(example['output']   )

    # some preprocessing to both prediction and answer
    SCREAMING_SNAKE_CASE_							=       {''.join(a.split()   ) for a in answers}
    SCREAMING_SNAKE_CASE_							=       {''.join(p.split()   ) for p in predictions}
    SCREAMING_SNAKE_CASE_							=       {s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

    # if there is a common element, it's a exact match
    SCREAMING_SNAKE_CASE_							=       len(list(answers & predictions   )   ) > 0
    SCREAMING_SNAKE_CASE_							=       predicted_category != 'null' and len(lowerCAmelCase   ) > 0

    return example

  SCREAMING_SNAKE_CASE_							=       short_validation_dataset.map(lowerCAmelCase   )

  SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
  SCREAMING_SNAKE_CASE_							=       len(short_validation_dataset.filter(lambda lowerCAmelCase   : x["match"] == 1   )   )
  print('EM score:'						,	(matched / total) * 100						,	'%'   )


if __name__ == "__main__":
      main()





from __future__ import annotations

import random
import string



class       lowercase_		:



						def __init__( self  :      str				,       snake_case__  :      str | None = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       passcode or self.__passcode_creator()
								SCREAMING_SNAKE_CASE_							=       self.__make_key_list()
								SCREAMING_SNAKE_CASE_							=       self.__make_shift_key()



						def __str__( self  :      List[Any]     ):

								"""simple docstring"""

								return "".join(self.__passcode     )



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      list[int]     ):

								"""simple docstring"""

								for i in range(1				,       len(snake_case__     )				,       2     ):
										iterlist[i] *= -1
								return iterlist



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       string.ascii_letters + string.digits
								SCREAMING_SNAKE_CASE_							=       [random.choice(snake_case__     ) for _ in range(random.randint(10				,       20     )     )]
								return password



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       (
								    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
								)

								SCREAMING_SNAKE_CASE_							=       []

								# creates points known as breakpoints to break the key_list_options at those
								# points and pivot each substring
								SCREAMING_SNAKE_CASE_							=       sorted(set(self.__passcode     )     )
								SCREAMING_SNAKE_CASE_							=       []

								# algorithm for creating a new shuffled list, keys_l, out of key_list_options
								for i in key_list_options:
										temp_list.extend(snake_case__     )

										# checking breakpoints at which to pivot temporary sublist and add it into
										# keys_l
										if i in breakpoints or i == key_list_options[-1]:
												keys_l.extend(temp_list[::-1]     )
												temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
								return keys_l



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       sum(self.__neg_pos([ord(snake_case__     ) for x in self.__passcode]     )     )
								return num if num > 0 else len(self.__passcode     )



						def   __a				( self  :      str				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

								# decoding shift like Caesar cipher algorithm implementing negative shift or
								# reverse shift or left shift
								for i in encoded_message:
										SCREAMING_SNAKE_CASE_							=       self.__key_list.index(snake_case__     )
										decoded_message += self.__key_list[
										    (position - self.__shift_key) % -len(self.__key_list     )
										]

								return decoded_message






						def   __a				( self  :      Optional[int]				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

								# encoding shift like Caesar cipher algorithm implementing positive shift or
								# forward shift or right shift
								for i in plaintext:
										SCREAMING_SNAKE_CASE_							=       self.__key_list.index(snake_case__     )
										encoded_message += self.__key_list[
										    (position + self.__shift_key) % len(self.__key_list     )
										]

								return encoded_message


def 				_a       (				lowerCAmelCase = "Hello, this is a modified Caesar cipher"   )->  str:
		SCREAMING_SNAKE_CASE_							=       ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(lowerCAmelCase   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()