


'''simple docstring'''



from sklearn.metrics import fa_score

import datasets


__lowercase      :		Union[str, Any]			=      "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


__lowercase      :		Any			=      "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


__lowercase      :		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 				__UpperCamelCase							(						datasets.Metric   ):




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						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.f1_score.html']		,							)





	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a=None		,							__a=1		,							__a="binary"		,							__a=None				):

						'''simple docstring'''





						__a     :			Tuple          =    fa_score(
						    a__		,							a__		,							labels=a__		,							pos_label=a__		,							average=a__		,							sample_weight=a__				)
						return {"f1": float(a__				) if score.size == 1 else score}







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				_lowercase							:   List[str]						=						None

_lowercase							:   List[str]						=						logging.get_logger(__name__)
_lowercase							:   List[Any]						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase							:   str						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase							:   List[str]						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase							:   Dict						=						"▁"






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			AlbertTokenizer

				def __init__(    self					, a__=None					, a__=None					, a__=True					, a__=True					, a__=False					, a__="[CLS]"					, a__="[SEP]"					, a__="<unk>"					, a__="[SEP]"					, a__="<pad>"					, a__="[CLS]"					, a__="[MASK]"					, **a__					, )						->					Dict:
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					A             =					(
					    AddedToken(a__					, lstrip=a__					, rstrip=a__					, normalized=a__ )
					    if isinstance(a__					, a__ )
					    else mask_token
					)

					super().__init__(
					    a__					, tokenizer_file=a__					, do_lower_case=a__					, remove_space=a__					, keep_accents=a__					, bos_token=a__					, eos_token=a__					, unk_token=a__					, sep_token=a__					, pad_token=a__					, cls_token=a__					, mask_token=a__					, **a__					, )

					A             =					do_lower_case
					A             =					remove_space
					A             =					keep_accents
					A             =					vocab_file
					A             =					False if not self.vocab_file else True

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[self.cls_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					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[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					, a__					, a__ = None )						->					Tuple[str]:
					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(a__ ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
						return
					A             =					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__ ):
						copyfile(self.vocab_file					, a__ )

					return (out_vocab_file,)



