from sklearn.metrics import roc_auc_score

import datasets


lowerCamelCase_				:		List[Any]       						=       """
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

lowerCamelCase_				:		str       						=       """
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'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 (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

lowerCamelCase_				:		Optional[Any]       						=       """\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@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 __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								return datasets.MetricInfo(
								    description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
								        {
								            'prediction_scores': datasets.Sequence(datasets.Value('float'    )    ),
								            'references': datasets.Value('int32'    ),
								        }
								        if self.config_name == 'multiclass'
								        else {
								            'references': datasets.Sequence(datasets.Value('int32'    )    ),
								            'prediction_scores': datasets.Sequence(datasets.Value('float'    )    ),
								        }
								        if self.config_name == 'multilabel'
								        else {
								            'references': datasets.Value('int32'    ),
								            'prediction_scores': datasets.Value('float'    ),
								        }    )      ,						reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html']      ,						)







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase="macro"      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase="raise"      ,						UpperCAmelCase=None      ,						) ->    Any:
								return {
								    "roc_auc": roc_auc_score(
								        UpperCAmelCase      ,						UpperCAmelCase      ,						average=UpperCAmelCase      ,						sample_weight=UpperCAmelCase      ,						max_fpr=UpperCAmelCase      ,						multi_class=UpperCAmelCase      ,						labels=UpperCAmelCase      ,						)
								}







from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_						=					'ResNetConfig'

# Base docstring
SCREAMING_SNAKE_CASE_						=					'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_						=					[1, 2048, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_						=					'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_						=					'tiger cat'

SCREAMING_SNAKE_CASE_						=					[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_ = 3  ,    A_ = 1  ,    A_ = "relu"  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						int       						=			kernel_size // 2
										_UpperCAmelCase				:						Dict       						=			tf.keras.layers.ConvaD(
										    A_  ,    kernel_size=A_  ,    strides=A_  ,    padding="valid"  ,    use_bias=A_  ,    name="convolution"		)
										# Use same default momentum and epsilon as PyTorch equivalent
										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.BatchNormalization(epsilon=1e-5  ,    momentum=0.9  ,    name="normalization"		)
										_UpperCAmelCase				:						int       						=			ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			(self.pad_value, self.pad_value)
										_UpperCAmelCase				:						List[Any]       						=			tf.pad(A_  ,    [(0, 0), height_pad, width_pad, (0, 0)]		)
										_UpperCAmelCase				:						int       						=			self.conv(A_		)
										return hidden_state





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			self.convolution(A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.normalization(A_  ,    training=A_		)
										_UpperCAmelCase				:						Dict       						=			self.activation(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(
										    config.embedding_size  ,    kernel_size=7  ,    stride=2  ,    activation=config.hidden_act  ,    name="embedder"  ,    )
										_UpperCAmelCase				:						List[Any]       						=			tf.keras.layers.MaxPoolaD(pool_size=3  ,    strides=2  ,    padding="valid"  ,    name="pooler"		)
										_UpperCAmelCase				:						List[Any]       						=			config.num_channels





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						Dict       						=			shape_list(A_		)
										if tf.executing_eagerly() and num_channels != self.num_channels:
														raise ValueError(
														    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."		)
										_UpperCAmelCase				:						Any       						=			pixel_values
										_UpperCAmelCase				:						List[str]       						=			self.embedder(A_		)
										_UpperCAmelCase				:						int       						=			tf.pad(A_  ,    [[0, 0], [1, 1], [1, 1], [0, 0]]		)
										_UpperCAmelCase				:						Dict       						=			self.pooler(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_ = 2  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.ConvaD(
										    A_  ,    kernel_size=1  ,    strides=A_  ,    use_bias=A_  ,    name="convolution"		)
										# Use same default momentum and epsilon as PyTorch equivalent
										_UpperCAmelCase				:						Any       						=			tf.keras.layers.BatchNormalization(epsilon=1e-5  ,    momentum=0.9  ,    name="normalization"		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			x
										_UpperCAmelCase				:						str       						=			self.convolution(A_		)
										_UpperCAmelCase				:						str       						=			self.normalization(A_  ,    training=A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_  ,    A_ = 1  ,    A_ = "relu"  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			in_channels != out_channels or stride != 1
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(A_  ,    stride=A_  ,    name="layer.0"		)
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(A_  ,    activation=A_  ,    name="layer.1"		)
										_UpperCAmelCase				:						int       						=			(
										    TFResNetShortCut(A_  ,    stride=A_  ,    name="shortcut"		)
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("linear"  ,    name="shortcut"		)
										)
										_UpperCAmelCase				:						List[str]       						=			ACTaFN[activation]





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			hidden_state
										_UpperCAmelCase				:						Optional[int]       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						List[Any]       						=			self.shortcut(A_  ,    training=A_		)
										hidden_state += residual
										_UpperCAmelCase				:						Any       						=			self.activation(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_  ,    A_ = 1  ,    A_ = "relu"  ,    A_ = 4  ,    **A_  ,    ):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						List[str]       						=			in_channels != out_channels or stride != 1
										_UpperCAmelCase				:						Dict       						=			out_channels // reduction
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(A_  ,    kernel_size=1  ,    name="layer.0"		)
										_UpperCAmelCase				:						str       						=			TFResNetConvLayer(A_  ,    stride=A_  ,    name="layer.1"		)
										_UpperCAmelCase				:						Optional[Any]       						=			TFResNetConvLayer(A_  ,    kernel_size=1  ,    activation=A_  ,    name="layer.2"		)
										_UpperCAmelCase				:						List[str]       						=			(
										    TFResNetShortCut(A_  ,    stride=A_  ,    name="shortcut"		)
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("linear"  ,    name="shortcut"		)
										)
										_UpperCAmelCase				:						Optional[int]       						=			ACTaFN[activation]





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			hidden_state
										_UpperCAmelCase				:						Union[str, Any]       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Tuple       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Dict       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.shortcut(A_  ,    training=A_		)
										hidden_state += residual
										_UpperCAmelCase				:						Any       						=			self.activation(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_ = 2  ,    A_ = 2  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)

										_UpperCAmelCase				:						str       						=			TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

										_UpperCAmelCase				:						List[str]       						=			[layer(A_  ,    A_  ,    stride=A_  ,    activation=config.hidden_act  ,    name="layers.0"		)]
										layers += [
										    layer(A_  ,    A_  ,    activation=config.hidden_act  ,    name=f'layers.{i + 1}'		)
										    for i in range(depth - 1		)
										]
										_UpperCAmelCase				:						Optional[Any]       						=			layers





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										for layer in self.stage_layers:
														_UpperCAmelCase				:						Union[str, Any]       						=			layer(A_  ,    training=A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										_UpperCAmelCase				:						List[str]       						=			[
										    TFResNetStage(
										        A_  ,    config.embedding_size  ,    config.hidden_sizes[0]  ,    stride=2 if config.downsample_in_first_stage else 1  ,    depth=config.depths[0]  ,    name="stages.0"  ,    )
										]
										for i, (in_channels, out_channels, depth) in enumerate(
										    zip(config.hidden_sizes  ,    config.hidden_sizes[1:]  ,    config.depths[1:]		)		):
														self.stages.append(TFResNetStage(A_  ,    A_  ,    A_  ,    depth=A_  ,    name=f'stages.{i + 1}'		)		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False  ,    A_ = True  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			() if output_hidden_states else None

										for stage_module in self.stages:
														if output_hidden_states:
																		_UpperCAmelCase				:						str       						=			hidden_states + (hidden_state,)

														_UpperCAmelCase				:						Union[str, Any]       						=			stage_module(A_  ,    training=A_		)

										if output_hidden_states:
														_UpperCAmelCase				:						Union[str, Any]       						=			hidden_states + (hidden_state,)

										if not return_dict:
														return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

										return TFBaseModelOutputWithNoAttention(last_hidden_state=A_  ,    hidden_states=A_		)







class  a     (		UpperCAmelCase ):
						_lowercase			  =	ResNetConfig
						_lowercase			  =	"resnet"
						_lowercase			  =	"pixel_values"


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)  ,    dtype=tf.floataa		)}







SCREAMING_SNAKE_CASE_						=					R'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


SCREAMING_SNAKE_CASE_						=					R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'

@keras_serializable
class  a     (		tf.keras.layers.Layer ):
						_lowercase			  =	ResNetConfig


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						List[Any]       						=			config
										_UpperCAmelCase				:						Optional[Any]       						=			TFResNetEmbeddings(A_  ,    name="embedder"		)
										_UpperCAmelCase				:						Tuple       						=			TFResNetEncoder(A_  ,    name="encoder"		)
										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_		)





						@unpack_inputs
						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_UpperCAmelCase				:						int       						=			return_dict if return_dict is not None else self.config.use_return_dict

										# TF 2.0 image layers can't use NCHW format when running on CPU.
										# We transpose to NHWC format and then transpose back after the full forward pass.
										# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
										_UpperCAmelCase				:						List[Any]       						=			tf.transpose(A_  ,    perm=[0, 2, 3, 1]		)
										_UpperCAmelCase				:						Dict       						=			self.embedder(A_  ,    training=A_		)

										_UpperCAmelCase				:						Any       						=			self.encoder(
										    A_  ,    output_hidden_states=A_  ,    return_dict=A_  ,    training=A_		)

										_UpperCAmelCase				:						Optional[int]       						=			encoder_outputs[0]

										_UpperCAmelCase				:						Tuple       						=			self.pooler(A_		)

										# Transpose all the outputs to the NCHW format
										# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
										_UpperCAmelCase				:						Union[str, Any]       						=			tf.transpose(A_  ,    (0, 3, 1, 2)		)
										_UpperCAmelCase				:						Any       						=			tf.transpose(A_  ,    (0, 3, 1, 2)		)
										_UpperCAmelCase				:						Tuple       						=			()
										for hidden_state in encoder_outputs[1:]:
														_UpperCAmelCase				:						Optional[int]       						=			hidden_states + tuple(tf.transpose(A_  ,    (0, 3, 1, 2)		) for h in hidden_state		)

										if not return_dict:
														return (last_hidden_state, pooled_output) + hidden_states

										_UpperCAmelCase				:						Any       						=			hidden_states if output_hidden_states else None

										return TFBaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=A_  ,    pooler_output=A_  ,    hidden_states=A_  ,    )







@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."							,       UpperCAmelCase							,       )
class  a     (		UpperCAmelCase ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(A_  ,    **A_		)
										_UpperCAmelCase				:						int       						=			TFResNetMainLayer(config=A_  ,    name="resnet"		)





						@add_start_docstrings_to_model_forward(A_		)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC  ,    output_type=A_  ,    config_class=_CONFIG_FOR_DOC  ,    modality="vision"  ,    expected_output=_EXPECTED_OUTPUT_SHAPE  ,    )
						@unpack_inputs
						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_UpperCAmelCase				:						str       						=			return_dict if return_dict is not None else self.config.use_return_dict

										_UpperCAmelCase				:						List[Any]       						=			self.resnet(
										    pixel_values=A_  ,    output_hidden_states=A_  ,    return_dict=A_  ,    training=A_  ,    )
										return resnet_outputs







@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "							,       UpperCAmelCase							,       )
class  a     (		UpperCAmelCase							,       UpperCAmelCase ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(A_  ,    **A_		)
										_UpperCAmelCase				:						Dict       						=			config.num_labels
										_UpperCAmelCase				:						List[Any]       						=			TFResNetMainLayer(A_  ,    name="resnet"		)
										# classification head
										_UpperCAmelCase				:						Union[str, Any]       						=			(
										    tf.keras.layers.Dense(config.num_labels  ,    name="classifier.1"		)
										    if config.num_labels > 0
										    else tf.keras.layers.Activation("linear"  ,    name="classifier.1"		)
										)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.Flatten()(A_		)
										_UpperCAmelCase				:						Dict       						=			self.classifier_layer(A_		)
										return logits





						@add_start_docstrings_to_model_forward(A_		)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT  ,    output_type=A_  ,    config_class=_CONFIG_FOR_DOC  ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,    )
						@unpack_inputs
						def 				_UpperCAmelCase (				self  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			return_dict if return_dict is not None else self.config.use_return_dict

										_UpperCAmelCase				:						Optional[Any]       						=			self.resnet(
										    A_  ,    output_hidden_states=A_  ,    return_dict=A_  ,    training=A_		)

										_UpperCAmelCase				:						Tuple       						=			outputs.pooler_output if return_dict else outputs[1]

										_UpperCAmelCase				:						int       						=			self.classifier(A_		)

										_UpperCAmelCase				:						str       						=			None if labels is None else self.hf_compute_loss(A_  ,    A_		)

										if not return_dict:
														_UpperCAmelCase				:						List[Any]       						=			(logits,) + outputs[2:]
														return (loss,) + output if loss is not None else output

										return TFImageClassifierOutputWithNoAttention(loss=A_  ,    logits=A_  ,    hidden_states=outputs.hidden_states		)


