code: 
  
  
  '''simple docstring'''
  
  import json
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_deberta import DebertaTokenizer
  
  
  if TYPE_CHECKING:
  							from transformers.pipelines.conversational import Conversation
  
  
  lowerCAmelCase								=    logging.get_logger(__name__)
  
  lowerCAmelCase								=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCAmelCase								=    {
      '''vocab_file''': {
          '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
          '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
          '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
          '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
          '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
          '''microsoft/deberta-xlarge-mnli''': (
              '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
          ),
      },
      '''merges_file''': {
          '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
          '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
          '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
          '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
          '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
          '''microsoft/deberta-xlarge-mnli''': (
              '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
          ),
      },
  }
  
  lowerCAmelCase								=    {
      '''microsoft/deberta-base''': 5_1_2,
      '''microsoft/deberta-large''': 5_1_2,
      '''microsoft/deberta-xlarge''': 5_1_2,
      '''microsoft/deberta-base-mnli''': 5_1_2,
      '''microsoft/deberta-large-mnli''': 5_1_2,
      '''microsoft/deberta-xlarge-mnli''': 5_1_2,
  }
  
  lowerCAmelCase								=    {
      '''microsoft/deberta-base''': {'''do_lower_case''': False},
      '''microsoft/deberta-large''': {'''do_lower_case''': False},
  }
  class _lowerCamelCase (		a__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    List[Any]   					=       VOCAB_FILES_NAMES
  		A_			:    int   					=       PRETRAINED_VOCAB_FILES_MAP
  		A_			:    Dict   					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		A_			:    List[Any]   					=       ["input_ids", "attention_mask", "token_type_ids"]
  		A_			:    int   					=       DebertaTokenizer
  
  
  
  
  
  
  
  		def __init__(      self	:		List[Any]						,  _A	:		Any=None						,  _A	:		int=None						,  _A	:		Optional[int]=None						,  _A	:		List[str]="replace"						,  _A	:		Optional[int]="[CLS]"						,  _A	:		Tuple="[SEP]"						,  _A	:		str="[SEP]"						,  _A	:		List[str]="[CLS]"						,  _A	:		Any="[UNK]"						,  _A	:		List[Any]="[PAD]"						,  _A	:		Tuple="[MASK]"						,  _A	:		Tuple=False						,  **_A	:		List[Any]						,  )						->				Union[str, Any]:
  			super().__init__(
  			    SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  tokenizer_file=SCREAMING_SNAKE_CASE_						,  errors=SCREAMING_SNAKE_CASE_						,  bos_token=SCREAMING_SNAKE_CASE_						,  eos_token=SCREAMING_SNAKE_CASE_						,  unk_token=SCREAMING_SNAKE_CASE_						,  sep_token=SCREAMING_SNAKE_CASE_						,  cls_token=SCREAMING_SNAKE_CASE_						,  pad_token=SCREAMING_SNAKE_CASE_						,  mask_token=SCREAMING_SNAKE_CASE_						,  add_prefix_space=SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_						,  )
  			__magic_name__ :					Optional[Any]  			=  kwargs.pop('add_bos_token'						,  SCREAMING_SNAKE_CASE_    )
  
  			__magic_name__ :					Any  			=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  			if pre_tok_state.get('add_prefix_space'						,  SCREAMING_SNAKE_CASE_    ) != add_prefix_space:
  				__magic_name__ :					str  			=  getattr(SCREAMING_SNAKE_CASE_						,  pre_tok_state.pop('type'    )    )
  				__magic_name__ :					List[str]  			=  add_prefix_space
  				__magic_name__ :					int  			=  pre_tok_class(**SCREAMING_SNAKE_CASE_    )
  
  			__magic_name__ :					str  			=  add_prefix_space
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				str:
  			if self._mask_token is None:
  				if self.verbose:
  					logger.error('Using mask_token, but it is not set yet.'    )
  				return None
  			return str(self._mask_token    )
  
  
  
  
  
  
  
  		@mask_token.setter
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		int    )						->				Optional[Any]:
  			__magic_name__ :					List[Any]  			=  AddedToken(SCREAMING_SNAKE_CASE_						,  lstrip=SCREAMING_SNAKE_CASE_						,  rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_    ) else value
  			__magic_name__ :					Optional[Any]  			=  value
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		Optional[int]						,  _A	:		Union[str, Any] = None    )						->				List[int]:
  			if token_ids_a is None:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  			__magic_name__ :					Dict  			=  [self.cls_token_id]
  			__magic_name__ :					Optional[int]  			=  [self.sep_token_id]
  			return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		List[Any]						,  _A	:		Any = None    )						->				List[int]:
  			__magic_name__ :					Dict  			=  [self.sep_token_id]
  			__magic_name__ :					int  			=  [self.cls_token_id]
  
  			if token_ids_a is None:
  				return len(cls + token_ids_a + sep    ) * [0]
  			return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  *_A	:		int						,  **_A	:		Any    )						->				BatchEncoding:
  			__magic_name__ :					Optional[int]  			=  kwargs.get('is_split_into_words'						,  SCREAMING_SNAKE_CASE_    )
  			assert self.add_prefix_space or not is_split_into_words, (
  			    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
  			    "to use it with pretokenized inputs."
  			)
  
  			return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  *_A	:		List[Any]						,  **_A	:		List[str]    )						->				BatchEncoding:
  			__magic_name__ :					List[str]  			=  kwargs.get('is_split_into_words'						,  SCREAMING_SNAKE_CASE_    )
  
  			assert self.add_prefix_space or not is_split_into_words, (
  			    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
  			    "to use it with pretokenized inputs."
  			)
  
  			return super()._encode_plus(*SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Union[str, Any]						,  _A	:		List[Any] = None    )						->				Tuple[str]:
  			__magic_name__ :					Tuple  			=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE_						,  name=SCREAMING_SNAKE_CASE_    )
  			return tuple(SCREAMING_SNAKE_CASE_    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		int    )						->				List[int]:
  			__magic_name__ :					List[Any]  			=  []
  			for is_user, text in conversation.iter_texts():
  				input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_						,  add_special_tokens=SCREAMING_SNAKE_CASE_    ) + [self.eos_token_id]    )
  
  			if len(SCREAMING_SNAKE_CASE_    ) > self.model_max_length:
  				__magic_name__ :					List[str]  			=  input_ids[-self.model_max_length :]
  			return input_ids
style_context: 
  
  
  '''simple docstring'''
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  lowerCAmelCase   :Union[str, Any]								=    '''\
  @article{scikit-learn,
   title={Scikit-learn: Machine Learning in {P}ython},
   author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
   journal={Journal of Machine Learning Research},
   volume={12},
   pages={2825--2830},
   year={2011}
  }
  '''
  
  lowerCAmelCase   :int								=    '''\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  '''
  
  
  lowerCAmelCase   :Optional[int]								=    '''
  Args:
      predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Estimated target values.
      references: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Ground truth (correct) target values.
      sample_weight: array-like of shape (n_samples,), default=None
          Sample weights.
      multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
          Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
  
                   "raw_values" : Returns a full set of errors in case of multioutput input.
  
                   "uniform_average" : Errors of all outputs are averaged with uniform weight.
  
  Returns:
      mae : mean absolute error.
          If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
          MAE output is non-negative floating point. The best value is 0.0.
  Examples:
  
      >>> mae_metric = datasets.load_metric("mae")
      >>> predictions = [2.5, 0.0, 2, 8]
      >>> references = [3, -0.5, 2, 7]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {\'mae\': 0.5}
  
      If you\'re using multi-dimensional lists, then set the config as follows :
  
      >>> mae_metric = datasets.load_metric("mae", "multilist")
      >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
      >>> references = [[0, 2], [-1, 2], [8, -5]]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {\'mae\': 0.75}
      >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
      >>> print(results)
      {\'mae\': array([0.5, 1. ])}
  '''
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION							)
  class _lowerCamelCase (		datasets.Metric							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				int:
  			return datasets.MetricInfo(
  			    description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(self._get_feature_types()    )						,  reference_urls=[
  			        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
  			    ]						,  )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				List[str]:
  			if self.config_name == "multilist":
  				return {
  				    "predictions": datasets.Sequence(datasets.Value('float'    )    ),
  				    "references": datasets.Sequence(datasets.Value('float'    )    ),
  				}
  			else:
  				return {
  				    "predictions": datasets.Value('float'    ),
  				    "references": datasets.Value('float'    ),
  				}
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]=None						,  _A	:		Tuple="uniform_average"    )						->				Any:
  			__magic_name__ :					Optional[Any]  			=  mean_absolute_error(_A						,  _A						,  sample_weight=_A						,  multioutput=_A    )
  
  			return {"mae": mae_score}