code: 
  
  
  import os
  from shutil import copyfile
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
  
  import sentencepiece as spm
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation
  
  __snake_case             =	logging.get_logger(__name__)
  
  __snake_case             =	{'''vocab_file''': '''tokenizer.model'''}
  
  __snake_case             =	{
      '''vocab_file''': {
          '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model''',
      },
      '''tokenizer_file''': {
          '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json''',
      },
  }
  __snake_case             =	{
      '''hf-internal-testing/llama-tokenizer''': 2_0_4_8,
  }
  __snake_case             =	'''▁'''
  
  __snake_case       ,  __snake_case             =	'''[INST]''', '''[/INST]'''
  __snake_case       ,  __snake_case             =	'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''
  
  # fmt: off
  __snake_case             =	'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
  answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
  that your responses are socially unbiased and positive in nature.
  
  If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
  correct. If you don\'t know the answer to a question, please don\'t share false information.'''
  
  
  
  class   __lowerCamelCase (_a	):
         _lowercase       							=    VOCAB_FILES_NAMES
         _lowercase       							=    PRETRAINED_VOCAB_FILES_MAP
         _lowercase       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         _lowercase       							=    ["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  
         def __init__( self:   int,A_:   Union[str, Any],A_:   Optional[Any]="<unk>",A_:   int="<s>",A_:   str="</s>",A_:   List[Any]=None,A_:   Optional[Dict[str, Any]] = None,A_:   List[Any]=True,A_:   List[Any]=False,A_:   List[Any]=False,A_:   List[str]=True,**A_:   Any,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = {} if sp_model_kwargs is None else sp_model_kwargs
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else bos_token
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else eos_token
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else unk_token
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else pad_token
              super().__init__(
                  bos_token=A_,eos_token=A_,unk_token=A_,pad_token=A_,add_bos_token=A_,add_eos_token=A_,sp_model_kwargs=self.sp_model_kwargs,clean_up_tokenization_spaces=A_,legacy=A_,**A_,)
              if legacy:
                   logger.warning_once(
                       F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                       ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'				)
              __UpperCamelCase   = legacy
              __UpperCamelCase   = vocab_file
              __UpperCamelCase   = add_bos_token
              __UpperCamelCase   = add_eos_token
              __UpperCamelCase   = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
              self.sp_model.Load(A_				)
  
  
  
  
  
  
  
         def __getstate__( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.__dict__.copy()
              __UpperCamelCase   = None
              __UpperCamelCase   = self.sp_model.serialized_model_proto()
              return state
  
  
  
  
  
  
  
         def __setstate__( self:   str,A_:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = d
              __UpperCamelCase   = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)
  
  
  
  
  
  
  
         @property
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return self.sp_model.get_piece_size()
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = {self.convert_ids_to_tokens(A_				): i for i in range(self.vocab_size				)}
              vocab.update(self.added_tokens_encoder				)
              return vocab
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   Union[str, Any],**A_:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if not self.legacy:
                   __UpperCamelCase   = SPIECE_UNDERLINE + text.replace(A_,' '				)
              return super().tokenize(A_,**A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   int				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if not self.legacy:
                   __UpperCamelCase   = text.startswith(A_				)
                   if is_first:
                        __UpperCamelCase   = text[1:]
  
              __UpperCamelCase   = self.sp_model.encode(A_,out_type=A_				)
  
              if not self.legacy and not is_first and not text.startswith(' '				) and tokens[0].startswith(A_				):
                   __UpperCamelCase   = ([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
              return tokens
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any],A_:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return self.sp_model.piece_to_id(A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.sp_model.IdToPiece(A_				)
              return token
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple,A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = []
              __UpperCamelCase   = ''
              __UpperCamelCase   = False
              for i, token in enumerate(A_				):
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                        if not prev_is_special and i != 0:
                             out_string += " "
                        out_string += self.sp_model.decode(A_				) + token
                        __UpperCamelCase   = True
                        __UpperCamelCase   = []
                   else:
                        current_sub_tokens.append(A_				)
                        __UpperCamelCase   = False
              out_string += self.sp_model.decode(A_				)
              return out_string
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   Optional[Any],A_:   Optional[str] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if not os.path.isdir(A_				):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                   return
              __UpperCamelCase   = os.path.join(
                  A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
  
              if os.path.abspath(self.vocab_file				) != os.path.abspath(A_				) and os.path.isfile(self.vocab_file				):
                   copyfile(self.vocab_file,A_				)
              elif not os.path.isfile(self.vocab_file				):
                   with open(A_,'wb'				) as fi:
                        __UpperCamelCase   = self.sp_model.serialized_model_proto()
                        fi.write(A_				)
  
              return (out_vocab_file,)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   int,A_:   Union[str, Any]=None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = [self.bos_token_id] if self.add_bos_token else []
              __UpperCamelCase   = [self.eos_token_id] if self.add_eos_token else []
  
              __UpperCamelCase   = bos_token_id + token_ids_a + eos_token_id
  
              if token_ids_a is not None:
                   __UpperCamelCase   = output + bos_token_id + token_ids_a + eos_token_id
  
              return output
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any],A_:   List[int],A_:   Optional[List[int]] = None,A_:   bool = False				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=A_,token_ids_a=A_,already_has_special_tokens=A_				)
  
              __UpperCamelCase   = [1] if self.add_bos_token else []
              __UpperCamelCase   = [1] if self.add_eos_token else []
  
              if token_ids_a is None:
                   return bos_token_id + ([0] * len(A_				)) + eos_token_id
              return (
                  bos_token_id
                  + ([0] * len(A_				))
                  + eos_token_id
                  + bos_token_id
                  + ([0] * len(A_				))
                  + eos_token_id
              )
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   List[int],A_:   Optional[List[int]] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = [self.bos_token_id] if self.add_bos_token else []
              __UpperCamelCase   = [self.eos_token_id] if self.add_eos_token else []
  
              __UpperCamelCase   = [0] * len(bos_token_id + token_ids_a + eos_token_id				)
  
              if token_ids_a is not None:
                   output += [1] * len(bos_token_id + token_ids_a + eos_token_id				)
  
              return output
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any,A_:   "Conversation"				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = 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...)'				)
  
              __UpperCamelCase   = []
              if len(conversation.past_user_inputs				) > 0:
                   if not conversation.past_user_inputs[0].startswith(A_				) or E_SYS not in conversation.past_user_inputs[0]:
                        __UpperCamelCase   = (
                            B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                        )
              elif not dialogue[0][1].startswith(A_				) or E_SYS not in dialogue[0][1]:
                   __UpperCamelCase   = (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=A_				)
                      + [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=A_				)
              return dialog_tokens
  
  
  
style_context: 
  
  
  import jax
  import jax.numpy as jnp
  from bigbird_flax import FlaxBigBirdForNaturalQuestions
  from datasets import load_from_disk
  
  from transformers import BigBirdTokenizerFast
  
  
  __snake_case             =	{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
  __snake_case             =	set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=0				,     _lowercase=None   )				->   Union[str, Any]:
  
  
       """simple docstring"""
  
  
       return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=False   )				->   Tuple:
  
  
       """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"
            __UpperCamelCase   = (
                given_answers + get_sub_answers(_lowercase				,     begin=1   ) + get_sub_answers(_lowercase				,     end=-1   )
            )
       __UpperCamelCase   = []
       for answer in given_answers:
            __UpperCamelCase   = answer.replace('_'				,     ' '   ).lower()
            __UpperCamelCase   = ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
            answers.append(' '.join(alias.split()   ).strip()   )
       return set(_lowercase   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=1				,     _lowercase=1_00   )				->   Any:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
       __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
  
       __UpperCamelCase   = best_end_idx[:, None] - best_start_idx[None, :]
       __UpperCamelCase   = jnp.logical_or(widths < 0				,     widths > max_size   )
       __UpperCamelCase   = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
       __UpperCamelCase   = jnp.argmax(_lowercase   ).item()
  
       return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = sample['question']['text']
       __UpperCamelCase   = sample['document']['tokens']['token']
       __UpperCamelCase   = sample['document']['tokens']['is_html']
       __UpperCamelCase   = sample['annotations']['long_answer']
       __UpperCamelCase   = sample['annotations']['short_answers']
  
       __UpperCamelCase   = ' '.join([context[i] for i in range(len(_lowercase   )   ) 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",
                 }
  
       __UpperCamelCase   = []
       for s in short_answers:
            short_targets.extend(s['text']   )
       __UpperCamelCase   = list(set(_lowercase   )   )
  
       __UpperCamelCase   = []
       for s in long_answers:
            if s["start_token"] == -1:
                 continue
            __UpperCamelCase   = context[s['start_token'] : s['end_token']]
            __UpperCamelCase   = is_html[s['start_token'] : s['end_token']]
            __UpperCamelCase   = ' '.join([answer[i] for i in range(len(_lowercase   )   ) if not html[i]]   )
            if new_answer not in long_targets:
                 long_targets.append(_lowercase   )
  
       __UpperCamelCase   = '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]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = load_from_disk('natural-questions-validation'   )
       __UpperCamelCase   = dataset.map(_lowercase   ).remove_columns(['annotations', 'document', 'id']   )
       print(_lowercase   )
  
       __UpperCamelCase   = dataset.filter(lambda _lowercase   : (len(x['question']   ) + len(x['context']   )) < 4 * 40_96   )
       __UpperCamelCase   = short_validation_dataset.filter(lambda _lowercase   : x["category"] != "null"   )
       short_validation_dataset
  
       __UpperCamelCase   = 'vasudevgupta/flax-bigbird-natural-questions'
       __UpperCamelCase   = FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase   )
       __UpperCamelCase   = BigBirdTokenizerFast.from_pretrained(_lowercase   )
  
       @jax.jit
       def forward(*_lowercase				,     **_lowercase   ):
            __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = model(*_lowercase				,     **_lowercase   )
            return start_logits, end_logits, jnp.argmax(_lowercase				,     axis=-1   )
  
       def evaluate(_lowercase   ):
            # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
            __UpperCamelCase   = tokenizer(
                example['question']				,     example['context']				,     return_tensors='np'				,     max_length=40_96				,     padding='max_length'				,     truncation=_lowercase				,     )
  
            __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = forward(**_lowercase   )
  
            __UpperCamelCase   = CATEGORY_MAPPING[category.item()]
  
            __UpperCamelCase   = example['long'] + example['short']
            if example["category"] in ["yes", "no", "null"]:
                 __UpperCamelCase   = [example['category']]
            __UpperCamelCase   = example['category'] != 'null'
            # Now target can be: "yes", "no", "null", "list of long & short answers"
  
            if predicted_category in ["yes", "no", "null"]:
                 __UpperCamelCase   = [predicted_category]
                 __UpperCamelCase   = example['output'] == example['targets']
                 __UpperCamelCase   = predicted_category != 'null'
                 return example
  
            __UpperCamelCase   = 38 if predicted_category == 'short' else 10_24
            __UpperCamelCase,						__UpperCamelCase   = get_best_valid_start_end_idx(
                start_scores[0]				,     end_scores[0]				,     top_k=8				,     max_size=_lowercase   )
  
            __UpperCamelCase   = inputs['input_ids'][0].tolist()
            __UpperCamelCase   = [tokenizer.decode(input_ids[start_score : end_score + 1]   )]
  
            __UpperCamelCase   = expand_to_aliases(example['targets']				,     make_sub_answers=_lowercase   )
            __UpperCamelCase   = expand_to_aliases(example['output']   )
  
            # some preprocessing to both prediction and answer
            __UpperCamelCase   = {''.join(a.split()   ) for a in answers}
            __UpperCamelCase   = {''.join(p.split()   ) for p in predictions}
            __UpperCamelCase   = {s for s in predictions if s not in ['``', '\'\'', '`', '\'']}
  
            # if there is a common element, it's a exact match
            __UpperCamelCase   = len(list(answers & predictions   )   ) > 0
            __UpperCamelCase   = predicted_category != 'null' and len(_lowercase   ) > 0
  
            return example
  
       __UpperCamelCase   = short_validation_dataset.map(_lowercase   )
  
       __UpperCamelCase   = len(_lowercase   )
       __UpperCamelCase   = len(short_validation_dataset.filter(lambda _lowercase   : x["match"] == 1   )   )
       print('EM score:'				,     (matched / total) * 1_00				,     '%'   )
  
  
  if __name__ == "__main__":
      main()
  
  
  