




'''simple docstring'''



from sklearn.metrics import fa_score

import datasets


lowerCamelCase_			=						'\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'


lowerCamelCase_			=						'\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'


lowerCamelCase_			=						'\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 		lowercase_   (			datasets.Metric       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Dict   ):

      """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 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict=None  ,				__lowerCamelCase :     List[Any]=1  ,				__lowerCamelCase :     str="binary"  ,				__lowerCamelCase :     Optional[int]=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				fa_score(
          __lowerCamelCase  ,				__lowerCamelCase  ,				labels=__lowerCamelCase  ,				pos_label=__lowerCamelCase  ,				average=__lowerCamelCase  ,				sample_weight=__lowerCamelCase   )
      return {"f1": float(__lowerCamelCase   ) if score.size == 1 else score}







'''simple docstring'''



from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''efficientnet'''



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 6_0_0  ,				__lowerCamelCase :     float = 2.0  ,				__lowerCamelCase :     float = 3.1  ,				__lowerCamelCase :     int = 8  ,				__lowerCamelCase :     List[int] = [3, 3, 5, 3, 5, 5, 3]  ,				__lowerCamelCase :     List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]  ,				__lowerCamelCase :     List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]  ,				__lowerCamelCase :     List[int] = []  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 2, 1, 2, 1]  ,				__lowerCamelCase :     List[int] = [1, 2, 2, 3, 3, 4, 1]  ,				__lowerCamelCase :     List[int] = [1, 6, 6, 6, 6, 6, 6]  ,				__lowerCamelCase :     float = 0.2_5  ,				__lowerCamelCase :     str = "swish"  ,				__lowerCamelCase :     int = 2_5_6_0  ,				__lowerCamelCase :     str = "mean"  ,				__lowerCamelCase :     float = 0.0_2  ,				__lowerCamelCase :     float = 0.0_0_1  ,				__lowerCamelCase :     float = 0.9_9  ,				__lowerCamelCase :     float = 0.5  ,				__lowerCamelCase :     float = 0.2  ,				**__lowerCamelCase :     Tuple  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				num_channels
      _SCREAMING_SNAKE_CASE	=				image_size
      _SCREAMING_SNAKE_CASE	=				width_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_coefficient
      _SCREAMING_SNAKE_CASE	=				depth_divisor
      _SCREAMING_SNAKE_CASE	=				kernel_sizes
      _SCREAMING_SNAKE_CASE	=				in_channels
      _SCREAMING_SNAKE_CASE	=				out_channels
      _SCREAMING_SNAKE_CASE	=				depthwise_padding
      _SCREAMING_SNAKE_CASE	=				strides
      _SCREAMING_SNAKE_CASE	=				num_block_repeats
      _SCREAMING_SNAKE_CASE	=				expand_ratios
      _SCREAMING_SNAKE_CASE	=				squeeze_expansion_ratio
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dim
      _SCREAMING_SNAKE_CASE	=				pooling_type
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				batch_norm_eps
      _SCREAMING_SNAKE_CASE	=				batch_norm_momentum
      _SCREAMING_SNAKE_CASE	=				dropout_rate
      _SCREAMING_SNAKE_CASE	=				drop_connect_rate
      _SCREAMING_SNAKE_CASE	=				sum(__lowerCamelCase   ) * 4







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						version.parse('''1.11'''       )



   @property
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return 1e-5

