"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "data2vec-vision"







       def __init__( self,							lowerCAmelCase__=768,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3072,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.02,							lowerCAmelCase__=1e-12,							lowerCAmelCase__=224,							lowerCAmelCase__=16,							lowerCAmelCase__=3,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=True,							lowerCAmelCase__=[3, 5, 7, 11],							lowerCAmelCase__=[1, 2, 3, 6],							lowerCAmelCase__=True,							lowerCAmelCase__=0.4,							lowerCAmelCase__=256,							lowerCAmelCase__=1,							lowerCAmelCase__=False,							lowerCAmelCase__=255,							**lowerCAmelCase__,							)      ->      Optional[int]:
            super().__init__(**lowerCAmelCase__)

            snake_case_					       =					hidden_size
            snake_case_					       =					num_hidden_layers
            snake_case_					       =					num_attention_heads
            snake_case_					       =					intermediate_size
            snake_case_					       =					hidden_act
            snake_case_					       =					hidden_dropout_prob
            snake_case_					       =					attention_probs_dropout_prob
            snake_case_					       =					initializer_range
            snake_case_					       =					layer_norm_eps

            snake_case_					       =					image_size
            snake_case_					       =					patch_size
            snake_case_					       =					num_channels
            snake_case_					       =					use_mask_token
            snake_case_					       =					use_absolute_position_embeddings
            snake_case_					       =					use_relative_position_bias
            snake_case_					       =					use_shared_relative_position_bias
            snake_case_					       =					layer_scale_init_value
            snake_case_					       =					drop_path_rate
            snake_case_					       =					use_mean_pooling
            # decode head attributes (semantic segmentation)
            snake_case_					       =					out_indices
            snake_case_					       =					pool_scales
            # auxiliary head attributes (semantic segmentation)
            snake_case_					       =					use_auxiliary_head
            snake_case_					       =					auxiliary_loss_weight
            snake_case_					       =					auxiliary_channels
            snake_case_					       =					auxiliary_num_convs
            snake_case_					       =					auxiliary_concat_input
            snake_case_					       =					semantic_loss_ignore_index




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      version.parse("1.11"   )







       @property
       def 		a_						( self)      ->      Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ])







       @property
       def 		a_						( self)      ->      float:
            return 1e-4








from sklearn.metrics import fa_score

import datasets


__lowerCamelCase       :     List[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       :     List[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       :     str   =     """
@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 					A__   (       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'''




       UpperCamelCase	:					List[str]       	=   fa_score(
           A_       ,							A_       ,							labels=A_       ,							pos_label=A_       ,							average=A_       ,							sample_weight=A_   )
       return {"f1": float(A_   ) if score.size == 1 else score}
