

'''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


lowercase__            =     logging.get_logger(__name__)

lowercase__            =     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase__            =     {
    "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"
        ),
    },
}

lowercase__            =     {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowercase__            =     {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Any    						=    VOCAB_FILES_NAMES
     UpperCAmelCase_		:      Union[str, Any]    						=    PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_		:      List[str]    						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_		:      Tuple    						=    ["""input_ids""", """attention_mask""", """token_type_ids"""]
     UpperCAmelCase_		:      Tuple    						=    DebertaTokenizer


     def __init__(						self					:	List[str]	,     lowercase_					:	Optional[Any]=None	,     lowercase_					:	Optional[Any]=None	,     lowercase_					:	Optional[Any]=None	,     lowercase_					:	Dict="replace"	,     lowercase_					:	Dict="[CLS]"	,     lowercase_					:	Union[str, Any]="[SEP]"	,     lowercase_					:	Any="[SEP]"	,     lowercase_					:	List[Any]="[CLS]"	,     lowercase_					:	str="[UNK]"	,     lowercase_					:	Tuple="[PAD]"	,     lowercase_					:	Union[str, Any]="[MASK]"	,     lowercase_					:	List[Any]=False	,     **lowercase_					:	Any	,     )		->      str:
      super().__init__(
          lowercase_	,     lowercase_	,     tokenizer_file=lowercase_	,     errors=lowercase_	,     bos_token=lowercase_	,     eos_token=lowercase_	,     unk_token=lowercase_	,     sep_token=lowercase_	,     cls_token=lowercase_	,     pad_token=lowercase_	,     mask_token=lowercase_	,     add_prefix_space=lowercase_	,     **lowercase_	,     )
      UpperCAmelCase			:      Union[str, Any]		   =					kwargs.pop('add_bos_token'	,     lowercase_			)

      UpperCAmelCase			:      List[str]		   =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
      if pre_tok_state.get('add_prefix_space'	,     lowercase_			) != add_prefix_space:
       UpperCAmelCase			:      Tuple		   =					getattr(lowercase_	,     pre_tok_state.pop('type'			)			)
       UpperCAmelCase			:      str		   =					add_prefix_space
       UpperCAmelCase			:      Dict		   =					pre_tok_class(**lowercase_			)

      UpperCAmelCase			:      List[Any]		   =					add_prefix_space


     @property
     def   UpperCAmelCase_			(						self					:	Dict			)		->      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   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	Union[str, Any]			)		->      Tuple:
      UpperCAmelCase			:      Union[str, Any]		   =					AddedToken(lowercase_	,     lstrip=lowercase_	,     rstrip=lowercase_			) if isinstance(lowercase_	,     lowercase_			) else value
      UpperCAmelCase			:      Union[str, Any]		   =					value


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	List[int]	,     lowercase_					:	Optional[List[int]] = None			)		->      List[int]:
      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCAmelCase			:      Union[str, Any]		   =					[self.cls_token_id]
      UpperCAmelCase			:      List[str]		   =					[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	List[int]	,     lowercase_					:	Optional[List[int]] = None			)		->      List[int]:
      UpperCAmelCase			:      List[Any]		   =					[self.sep_token_id]
      UpperCAmelCase			:      Optional[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   UpperCAmelCase_			(						self					:	Optional[Any]	,     *lowercase_					:	Optional[int]	,     **lowercase_					:	Dict			)		->      BatchEncoding:
      UpperCAmelCase			:      List[str]		   =					kwargs.get('is_split_into_words'	,     lowercase_			)
      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(*lowercase_	,     **lowercase_			)


     def   UpperCAmelCase_			(						self					:	Any	,     *lowercase_					:	Union[str, Any]	,     **lowercase_					:	Tuple			)		->      BatchEncoding:
      UpperCAmelCase			:      List[Any]		   =					kwargs.get('is_split_into_words'	,     lowercase_			)

      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(*lowercase_	,     **lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	str	,     lowercase_					:	Optional[str] = None			)		->      Tuple[str]:
      UpperCAmelCase			:      Dict		   =					self._tokenizer.model.save(lowercase_	,     name=lowercase_			)
      return tuple(lowercase_			)




     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	"Conversation"			)		->      List[int]:
      UpperCAmelCase			:      Optional[int]		   =					[]
      for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(lowercase_	,     add_special_tokens=lowercase_			) + [self.eos_token_id]			)

      if len(lowercase_			) > self.model_max_length:
       UpperCAmelCase			:      int		   =					input_ids[-self.model_max_length :]
      return input_ids







'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


lowercase__            =     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


lowercase__            =     "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,  _KWARGS_DESCRIPTION				)
class 			A_					(							datasets.Metric				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      str:
      return datasets.MetricInfo(
          description=_DESCRIPTION	,     citation=_CITATION	,     inputs_description=_KWARGS_DESCRIPTION	,     features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('int32'			)			),
                  'references': datasets.Sequence(datasets.Value('int32'			)			),
              }
              if self.config_name == 'multilabel'
              else {
                  'predictions': datasets.Value('int32'			),
                  'references': datasets.Value('int32'			),
              }			)	,     reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']	,     )




     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




