


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool

class 							lowercase  (						a_			):
     lowerCamelCase			:			List[str]      = '''facebook/bart-large-mnli'''
     lowerCamelCase			:			Any      = (
         '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
         '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
         '''It returns the most likely label in the list of provided `labels` for the input text.'''
     )
     lowerCamelCase			:			int      = '''text_classifier'''
     lowerCamelCase			:			Any      = AutoTokenizer
     lowerCamelCase			:			Optional[int]      = AutoModelForSequenceClassification

     lowerCamelCase			:			Optional[int]      = ['''text''', ['''text''']]
     lowerCamelCase			:			str      = ['''text''']







     def 							lowercase__       (						self :						Union[str, Any]      ):
           super().setup()
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model.config
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		-1
           for idx, label in config.idalabel.items():
                 if label.lower().startswith('''entail'''      ):
                       SCREAMING_SNAKE_CASE__       :  Tuple						     =		int(__UpperCamelCase      )
           if self.entailment_id == -1:
                 raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''      )







     def 							lowercase__       (						self :						List[str]   ,   _lowercase :						Optional[int]   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		labels
           return self.pre_processor(
               [text] * len(__UpperCamelCase      )   ,   [f"""This example is {label}""" for label in labels]   ,   return_tensors='''pt'''   ,   padding='''max_length'''   ,   )







     def 							lowercase__       (						self :						int   ,   _lowercase :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		outputs.logits
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.argmax(logits[:, 2]      ).item()
           return self._labels[label_id]














from sklearn.metrics import fa_score

import datasets


lowerCamelCase      :       Union[str, Any]    =						'''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


lowerCamelCase      :       Optional[Any]    =						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    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.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    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\'`.

        - \'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.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'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.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> 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])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


lowerCamelCase      :       Optional[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}
}
'''



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


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           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       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=1       ,    __UpperCamelCase="binary"       ,    __UpperCamelCase=None						)->						Optional[int]:
           __lowerCAmelCase             =       fa_score(
               __UpperCamelCase       ,    __UpperCamelCase       ,    labels=__UpperCamelCase       ,    pos_label=__UpperCamelCase       ,    average=__UpperCamelCase       ,    sample_weight=__UpperCamelCase						)
           return {"f1": float(__UpperCamelCase						) if score.size == 1 else score}






