




from sklearn.metrics import recall_score

import datasets


__lowerCAmelCase		:				List[str]       			=		'\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n'


__lowerCAmelCase		:				Optional[Any]       			=		'\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions 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. Note that it 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- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {\'recall\': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {\'recall\': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {\'recall\': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'recall\': array([1., 0., 0.])}\n'


__lowerCAmelCase		:				Optional[int]       			=		'\n@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}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,       _KWARGS_DESCRIPTION   )
class     snake_case__							(datasets.Metric   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							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.recall_score.html"]					,      )





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     str=None					,      __lowerCamelCase				:     Union[str, Any]=1					,      __lowerCamelCase				:     Any="binary"					,      __lowerCamelCase				:     Dict=None					,      __lowerCamelCase				:     List[str]="warn"					,      )					->     Union[str, Any]:
							a    =			recall_score(
							    __lowerCamelCase					,      __lowerCamelCase					,      labels=__lowerCamelCase					,      pos_label=__lowerCamelCase					,      average=__lowerCamelCase					,      sample_weight=__lowerCamelCase					,      zero_division=__lowerCamelCase					,      )
							return {"recall": float(__lowerCamelCase							) if score.size == 1 else score}











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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

							lowerCamelCase__   			=		{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase     :							Any    =    image_size
														_lowerCAmelCase     :							Union[str, Any]    =    patch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embed_dim
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Optional[Any]    =    len(__a)
														_lowerCAmelCase     :							int    =    num_heads
														_lowerCAmelCase     :							int    =    window_size
														_lowerCAmelCase     :							int    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    qkv_bias
														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							Union[str, Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    drop_path_rate
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    use_absolute_embeddings
														_lowerCAmelCase     :							Optional[int]    =    layer_norm_eps
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Tuple    =    encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_lowerCAmelCase     :							List[str]    =    int(embed_dim * 2 ** (len(__a) - 1))
														_lowerCAmelCase     :							List[Any]    =    ["stem"] + [f"stage{idx}" for idx in range(1,			len(__a) + 1)]
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_aligned_output_features_output_indices(
														    out_features=__a,			out_indices=__a,			stage_names=self.stage_names)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
