





from __future__ import annotations


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_    ,snake_case_		:      Optional[Any]           =						'', '', ''
	snake_case_		:      Optional[Any]           =						[]

	for character in message_part:
		tmp.append(character_to_number[character] )

	for each in tmp:
		one += each[0]
		two += each[1]
		three += each[2]

	return one + two + three


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_		:      Optional[int]           =						'', ''
	snake_case_		:      Dict           =						[]

	for character in message_part:
		this_part += character_to_number[character]

	for digit in this_part:
		tmp += digit
		if len(__a ) == len(__a ):
			result.append(__a )
			snake_case_		:      Optional[Any]           =						''

	return result[0], result[1], result[2]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	# Validate message and alphabet, set to upper and remove spaces
	snake_case_		:      Optional[int]           =						alphabet.replace(' ' ,			'' ).upper()
	snake_case_		:      Tuple           =						message.replace(' ' ,			'' ).upper()

	# Check length and characters
	if len(__a ) != 27:
		raise KeyError('Length of alphabet has to be 27.' )
	for each in message:
		if each not in alphabet:
			raise ValueError('Each message character has to be included in alphabet!' )

    # Generate dictionares
	snake_case_		:      Dict           =						(
	    '111',
	    '112',
	    '113',
	    '121',
	    '122',
	    '123',
	    '131',
	    '132',
	    '133',
	    '211',
	    '212',
	    '213',
	    '221',
	    '222',
	    '223',
	    '231',
	    '232',
	    '233',
	    '311',
	    '312',
	    '313',
	    '321',
	    '322',
	    '323',
	    '331',
	    '332',
	    '333',
	)
	snake_case_		:      Union[str, Any]           =						{}
	snake_case_		:      Tuple           =						{}
	for letter, number in zip(__a ,			__a ):
		snake_case_		:      Optional[int]           =						number
		snake_case_		:      Optional[Any]           =						letter

	return message, alphabet, character_to_number, number_to_character


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,			__a = 5 ):
	snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      int           =						__prepare(
	    __a ,			__a )
	snake_case_    ,snake_case_		:      Optional[Any]           =						'', ''

	for i in range(0 ,			len(__a ) + 1 ,			__a ):
		encrypted_numeric += __encrypt_part(
		    message[i : i + period] ,			__a )

	for i in range(0 ,			len(__a ) ,			3 ):
		encrypted += number_to_character[encrypted_numeric[i : i + 3]]

	return encrypted


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,			__a = 5 ):
	snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Union[str, Any]           =						__prepare(
	    __a ,			__a )
	snake_case_		:      Tuple           =						[]
	snake_case_		:      Optional[Any]           =						''

	for i in range(0 ,			len(__a ) + 1 ,			__a ):
		snake_case_    ,snake_case_    ,snake_case_		:      List[Any]           =						__decrypt_part(message[i : i + period] ,			__a )

		for j in range(0 ,			len(__a ) ):
			decrypted_numeric.append(a[j] + b[j] + c[j] )

	for each in decrypted_numeric:
		decrypted += number_to_character[each]

	return decrypted


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		"""DEFEND THE EAST WALL OF THE CASTLE."""
						_SCREAMING_SNAKE_CASE   						=		encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
						_SCREAMING_SNAKE_CASE   						=		decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
						print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')







'''simple docstring'''





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

from transformers import BigBirdTokenizerFast


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


def 					UpperCamelCase						(	snake_case						,  snake_case=0						,  snake_case=None ):




   '''simple docstring'''

   return [" ".join(x.split(''' ''' )[begin:end] ) for x in answers if len(x.split(''' ''' ) ) > 1]


def 					UpperCamelCase						(	snake_case						,  snake_case=False ):




   '''simple docstring'''

   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__      :					Tuple							    =			(
          given_answers + get_sub_answers(snake_case						,  begin=1 ) + get_sub_answers(snake_case						,  end=-1 )
      )
   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for answer in given_answers:
      SCREAMING_SNAKE_CASE__      :					int							    =			answer.replace('''_'''						,  ''' ''' ).lower()
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias )
      answers.append(''' '''.join(alias.split() ).strip() )
   return set(snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case=1						,  snake_case=1_00 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			jax.lax.top_k(snake_case						,  snake_case )
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			jax.lax.top_k(snake_case						,  snake_case )

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			best_end_idx[:, None] - best_start_idx[None, :]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			jnp.logical_or(widths < 0						,  widths > max_size )
   SCREAMING_SNAKE_CASE__      :					Any							    =			(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
   SCREAMING_SNAKE_CASE__      :					str							    =			jnp.argmax(snake_case ).item()

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


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			sample['''question''']['''text''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			sample['''document''']['''tokens''']['''token''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''document''']['''tokens''']['''is_html''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''annotations''']['''long_answer''']
   SCREAMING_SNAKE_CASE__      :					str							    =			sample['''annotations''']['''short_answers''']

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([context[i] for i in range(len(snake_case ) ) 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__      :					Optional[Any]							    =			[]
   for s in short_answers:
      short_targets.extend(s['''text'''] )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			list(set(snake_case ) )

   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for s in long_answers:
      if s["start_token"] == -1:
         continue
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			context[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					Dict							    =			is_html[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([answer[i] for i in range(len(snake_case ) ) if not html[i]] )
      if new_answer not in long_targets:
         long_targets.append(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			'''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 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			load_from_disk('''natural-questions-validation''' )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dataset.map(snake_case ).remove_columns(['''annotations''', '''document''', '''id'''] )
   print(snake_case )

   SCREAMING_SNAKE_CASE__      :					Dict							    =			dataset.filter(lambda snake_case : (len(x['''question'''] ) + len(x['''context'''] )) < 4 * 40_96 )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			short_validation_dataset.filter(lambda snake_case : x["category"] != "null" )
   short_validation_dataset

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			'''vasudevgupta/flax-bigbird-natural-questions'''
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			FlaxBigBirdForNaturalQuestions.from_pretrained(snake_case )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			BigBirdTokenizerFast.from_pretrained(snake_case )

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

   def evaluate(snake_case ):
      # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
      SCREAMING_SNAKE_CASE__      :					Any							    =			tokenizer(
          example['''question''']						,  example['''context''']						,  return_tensors='''np'''						,  max_length=40_96						,  padding='''max_length'''						,  truncation=snake_case						,  )

      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			forward(**snake_case )

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			CATEGORY_MAPPING[category.item()]

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			example['''long'''] + example['''short''']
      if example["category"] in ["yes", "no", "null"]:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[example['''category''']]
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			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__      :					str							    =			[predicted_category]
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			example['''output'''] == example['''targets''']
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			predicted_category != '''null'''
         return example

      SCREAMING_SNAKE_CASE__      :					Any							    =			38 if predicted_category == '''short''' else 10_24
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_best_valid_start_end_idx(
          start_scores[0]						,  end_scores[0]						,  top_k=8						,  max_size=snake_case )

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

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			expand_to_aliases(example['''targets''']						,  make_sub_answers=snake_case )
      SCREAMING_SNAKE_CASE__      :					Any							    =			expand_to_aliases(example['''output'''] )

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

      # if there is a common element, it's a exact match
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			len(list(answers & predictions ) ) > 0
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			predicted_category != '''null''' and len(snake_case ) > 0

      return example

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			short_validation_dataset.map(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			len(snake_case )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(short_validation_dataset.filter(lambda snake_case : x["match"] == 1 ) )
   print('''EM score:'''						,  (matched / total) * 1_00						,  '''%''' )


if __name__ == "__main__":
       main()

