




'''simple docstring'''

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

from transformers import BigBirdTokenizerFast


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




def    snake_case__     (  a       ,      a=0       ,      a=None	)			->       Any:





			'''simple docstring'''






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




def    snake_case__     (  a       ,      a=False	)			->       Dict:





			'''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"
						snake_case__          =						(
						    given_answers + get_sub_answers(a       ,      begin=1	) + get_sub_answers(a       ,      end=-1	)
						)
			snake_case__          =						[]
			for answer in given_answers:
						snake_case__          =						answer.replace("""_"""       ,      """ """	).lower()
						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(a	)




def    snake_case__     (  a       ,      a       ,      a=1       ,      a=100	)			->       str:





			'''simple docstring'''






			snake_case__    ,			snake_case__          =						jax.lax.top_k(a       ,      a	)
			snake_case__    ,			snake_case__          =						jax.lax.top_k(a       ,      a	)

			snake_case__          =						best_end_idx[:, None] - best_start_idx[None, :]
			snake_case__          =						jnp.logical_or(widths < 0       ,      widths > max_size	)
			snake_case__          =						(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
			snake_case__          =						jnp.argmax(a	).item()

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




def    snake_case__     (  a	)			->       Any:





			'''simple docstring'''






			snake_case__          =						sample["""question"""]["""text"""]
			snake_case__          =						sample["""document"""]["""tokens"""]["""token"""]
			snake_case__          =						sample["""document"""]["""tokens"""]["""is_html"""]
			snake_case__          =						sample["""annotations"""]["""long_answer"""]
			snake_case__          =						sample["""annotations"""]["""short_answers"""]

			snake_case__          =						""" """.join([context[i] for i in range(len(a	)	) 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",
									}

			snake_case__          =						[]
			for s in short_answers:
						short_targets.extend(s["""text"""]	)
			snake_case__          =						list(set(a	)	)

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

			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    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						load_from_disk("""natural-questions-validation"""	)
			snake_case__          =						dataset.map(a	).remove_columns(["""annotations""", """document""", """id"""]	)
			print(a	)

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

			snake_case__          =						"""vasudevgupta/flax-bigbird-natural-questions"""
			snake_case__          =						FlaxBigBirdForNaturalQuestions.from_pretrained(a	)
			snake_case__          =						BigBirdTokenizerFast.from_pretrained(a	)

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

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

						snake_case__    ,			snake_case__    ,			snake_case__          =						forward(**a	)

						snake_case__          =						CATEGORY_MAPPING[category.item()]

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

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

						snake_case__          =						38 if predicted_category == """short""" else 1024
						snake_case__    ,			snake_case__          =						get_best_valid_start_end_idx(
						    start_scores[0]       ,      end_scores[0]       ,      top_k=8       ,      max_size=a	)

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

						snake_case__          =						expand_to_aliases(example["""targets"""]       ,      make_sub_answers=a	)
						snake_case__          =						expand_to_aliases(example["""output"""]	)

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

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

						return example

			snake_case__          =						short_validation_dataset.map(a	)

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


if __name__ == "__main__":
		main()
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version("tokenizers>=0.13.3")

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						lowerCAmelCase__  	=      None

lowerCAmelCase__  	=      logging.get_logger(__name__)
lowerCAmelCase__  	=      {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__	,					lowerCAmelCase__  	=      "[INST]", "[/INST]"
lowerCAmelCase__	,					lowerCAmelCase__  	=      "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase__  	=      "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE              =					LlamaTokenizer
	__SCREAMING_SNAKE_CASE              =					'left'
	__SCREAMING_SNAKE_CASE              =					['input_ids', 'attention_mask']


	def __init__(   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_="<s>"       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=False       ,  **lowerCAmelCase_       ,  ):
		super().__init__(
		    vocab_file=lowerCAmelCase_       ,  tokenizer_file=lowerCAmelCase_       ,  clean_up_tokenization_spaces=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  bos_token=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )
		_lowercase   =add_bos_token
		_lowercase   =add_eos_token
		self.update_post_processor()

		_lowercase   =vocab_file
		_lowercase   =False if not self.vocab_file else True


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.bos_token
		_lowercase   =self.bos_token_id

		_lowercase   =self.eos_token
		_lowercase   =self.eos_token_id

		_lowercase   =F'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
		_lowercase   =F'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

		_lowercase   =[]
		if self.add_bos_token:
			special_tokens.append((bos, bos_token_id)       )
		if self.add_eos_token:
			special_tokens.append((eos, eos_token_id)       )
		_lowercase   =processors.TemplateProcessing(
		    single=lowerCAmelCase_       ,  pair=lowerCAmelCase_       ,  special_tokens=lowerCAmelCase_       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self._add_eos_token


	@property
	def __lowerCAmelCase      (   self       ):
		return self._add_bos_token


	@add_eos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	@add_bos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
			    "tokenizer."       )

		if not os.path.isdir(lowerCAmelCase_       ):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
			return
		_lowercase   =os.path.join(
		    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

		if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCAmelCase_       ):
			copyfile(self.vocab_file       ,  lowerCAmelCase_       )

		return (out_vocab_file,)


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =list(conversation.iter_texts()       )
		if not all([is_user for is_user, msg in dialogue[::2]]       ) or not all(
		    [not is_user for is_user, msg in dialogue[1::2]]       ):
			raise ValueError(
			    "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"       )

		_lowercase   =[]
		if len(conversation.past_user_inputs       ) > 0:
			if not conversation.past_user_inputs[0].startswith(lowerCAmelCase_       ) or E_SYS not in conversation.past_user_inputs[0]:
				_lowercase   =(
				    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
				)
		elif not dialogue[0][1].startswith(lowerCAmelCase_       ) or E_SYS not in dialogue[0][1]:
			_lowercase   =(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

		dialog_tokens += sum(
		    [
		        [self.bos_token_id]
		        + self.encode(
		            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''       ,  add_special_tokens=lowerCAmelCase_       )
		        + [self.eos_token_id]
		        for prompt, answer in zip(dialogue[::2]       ,  dialogue[1::2]       )
		    ]       ,  []       ,  )
		if not (dialogue[-1][0]):
			raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''       )
		dialog_tokens += [self.bos_token_id] + self.encode(
		    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''       ,  add_special_tokens=lowerCAmelCase_       )
		return dialog_tokens




