
"""simple docstring"""


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


A_    							=					logging.get_logger(__name__)

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

A_    							=					{
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

A_    							=					{"allegro/herbert-base-cased": 514}
A_    							=					{}




class  __lowerCamelCase  ( UpperCamelCase__			):
				a__:      Optional[Any]          =    VOCAB_FILES_NAMES
				a__:      Optional[int]          =    PRETRAINED_VOCAB_FILES_MAP
				a__:      Any          =    PRETRAINED_INIT_CONFIGURATION
				a__:      Optional[int]          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__:      Any          =    HerbertTokenizer




				def __init__(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase="<s>"							,						UpperCAmelCase="<unk>"							,						UpperCAmelCase="<pad>"							,						UpperCAmelCase="<mask>"							,						UpperCAmelCase="</s>"							,						**UpperCAmelCase							,						):
							super().__init__(
							    __snake_case							,						__snake_case							,						tokenizer_file=__snake_case							,						cls_token=__snake_case							,						unk_token=__snake_case							,						pad_token=__snake_case							,						mask_token=__snake_case							,						sep_token=__snake_case							,						**__snake_case							,						)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  [self.cls_token_id]
							lowerCamelCase_					=  [self.sep_token_id]
							if token_ids_a is None:
										return cls + token_ids_a + sep

							return cls + token_ids_a + sep + token_ids_a + sep




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = False   ):

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__snake_case							,						token_ids_a=__snake_case							,						already_has_special_tokens=__snake_case   )

							if token_ids_a is None:
										return [1] + ([0] * len(__snake_case   )) + [1]
							return [1] + ([0] * len(__snake_case   )) + [1] + ([0] * len(__snake_case   )) + [1]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  [self.sep_token_id]
							lowerCamelCase_					=  [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							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  self._tokenizer.model.save(__snake_case							,						name=__snake_case   )
							return tuple(__snake_case   )









from sklearn.metrics import matthews_corrcoef

import datasets


UpperCAmelCase_		:					Dict          =  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCAmelCase_		:					Any          =  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

UpperCAmelCase_		:					Dict          =  "\\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      ):

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""int32"""			),
										            """references""": datasets.Value("""int32"""			),
										        }			)     ,			reference_urls=[
										        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
										    ]     ,			)


						def      A__    (		self :Tuple     ,			__snake_case :str     ,			__snake_case :Tuple     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										return {
										    "matthews_correlation": float(matthews_corrcoef(__snake_case     ,			__snake_case     ,			sample_weight=__snake_case			)			),
										}




