





from sklearn.metrics import recall_score

import datasets


A    :					Optional[int] =      """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


A    :					int =      """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **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.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **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'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions 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. Note that it 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`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **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.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


A    :					Tuple =      """
@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    ):
			'''simple docstring'''




			def    a_       (			self   :    List[str]   )	->	Optional[int]:







					"""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.recall_score.html"""]							,	)







			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    str=1							,	__lowerCAmelCase   :    List[str]="binary"							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    str="warn"							,	)	->	Optional[Any]:







					"""simple docstring"""






					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 typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowerCAmelCase							:  int   	=						logging.get_logger(__name__)


# General docstring
lowerCAmelCase							:  int   	=						"""MobileNetV1Config"""

# Base docstring
lowerCAmelCase							:  List[Any]   	=						"""google/mobilenet_v1_1.0_224"""
lowerCAmelCase							:  Dict   	=						[1, 1024, 7, 7]

# Image classification docstring
lowerCAmelCase							:  Union[str, Any]   	=						"""google/mobilenet_v1_1.0_224"""
lowerCAmelCase							:  Any   	=						"""tabby, tabby cat"""


lowerCAmelCase							:  List[Any]   	=						[
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       {}

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Any  		=       model.mobilenet_va
						else:
												SCREAMING_SNAKE_CASE_: int  		=       model

						SCREAMING_SNAKE_CASE_: Dict  		=       "MobilenetV1/Conv2d_0/"
						SCREAMING_SNAKE_CASE_: str  		=       backbone.conv_stem.convolution.weight
						SCREAMING_SNAKE_CASE_: List[str]  		=       backbone.conv_stem.normalization.bias
						SCREAMING_SNAKE_CASE_: int  		=       backbone.conv_stem.normalization.weight
						SCREAMING_SNAKE_CASE_: List[str]  		=       backbone.conv_stem.normalization.running_mean
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       backbone.conv_stem.normalization.running_var

						for i in range(13    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       i + 1
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       i * 2

												SCREAMING_SNAKE_CASE_: Any  		=       backbone.layer[pt_index]
												SCREAMING_SNAKE_CASE_: Any  		=       f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: str  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_var

												SCREAMING_SNAKE_CASE_: Tuple  		=       backbone.layer[pt_index + 1]
												SCREAMING_SNAKE_CASE_: List[str]  		=       f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
												SCREAMING_SNAKE_CASE_: int  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_var

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       "MobilenetV1/Logits/Conv2d_1c_1x1/"
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.classifier.weight
												SCREAMING_SNAKE_CASE_: Tuple  		=       model.classifier.bias

						return tf_to_pt_map







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						try:
												import numpy as np
												import tensorflow as tf
						except ImportError:
												logger.error(
												    "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
												    "https://www.tensorflow.org/install/ for installation instructions."    )
												raise

						# Load weights from TF model
						SCREAMING_SNAKE_CASE_: int  		=       tf.train.list_variables(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       {}
						for name, shape in init_vars:
												logger.info(f"Loading TF weight {name} with shape {shape}"    )
												SCREAMING_SNAKE_CASE_: Any  		=       tf.train.load_variable(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       array

						# Build TF to PyTorch weights loading map
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       _build_tf_to_pytorch_map(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						for name, pointer in tf_to_pt_map.items():
												logger.info(f"Importing {name}"    )
												if name not in tf_weights:
																		logger.info(f"{name} not in tf pre-trained weights, skipping"    )
																		continue

												SCREAMING_SNAKE_CASE_: int  		=       tf_weights[name]

												if "depthwise_weights" in name:
																		logger.info("Transposing depthwise"    )
																		SCREAMING_SNAKE_CASE_: int  		=       np.transpose(_UpperCAmelCase				,	(2, 3, 0, 1)    )
												elif "weights" in name:
																		logger.info("Transposing"    )
																		if len(pointer.shape    ) == 2:  # copying into linear layer
																								SCREAMING_SNAKE_CASE_: List[str]  		=       array.squeeze().transpose()
																		else:
																								SCREAMING_SNAKE_CASE_: Any  		=       np.transpose(_UpperCAmelCase				,	(3, 2, 0, 1)    )

												if pointer.shape != array.shape:
																		raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"    )

												logger.info(f"Initialize PyTorch weight {name} {array.shape}"    )
												SCREAMING_SNAKE_CASE_: int  		=       torch.from_numpy(_UpperCAmelCase    )

												tf_weights.pop(_UpperCAmelCase				,	_UpperCAmelCase    )
												tf_weights.pop(name + "/RMSProp"				,	_UpperCAmelCase    )
												tf_weights.pop(name + "/RMSProp_1"				,	_UpperCAmelCase    )
												tf_weights.pop(name + "/ExponentialMovingAverage"				,	_UpperCAmelCase    )

						logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()    )}"    )
						return model







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       features.shape[-2:]
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       conv_layer.stride
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       conv_layer.kernel_size

						if in_height % stride_height == 0:
												SCREAMING_SNAKE_CASE_: int  		=       max(kernel_height - stride_height				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       max(kernel_height - (in_height % stride_height)				,	0    )

						if in_width % stride_width == 0:
												SCREAMING_SNAKE_CASE_: str  		=       max(kernel_width - stride_width				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Dict  		=       max(kernel_width - (in_width % stride_width)				,	0    )

						SCREAMING_SNAKE_CASE_: str  		=       pad_along_width // 2
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pad_along_width - pad_left
						SCREAMING_SNAKE_CASE_: int  		=       pad_along_height // 2
						SCREAMING_SNAKE_CASE_: Tuple  		=       pad_along_height - pad_top

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (pad_left, pad_right, pad_top, pad_bottom)
						return nn.functional.pad(_UpperCAmelCase				,	_UpperCAmelCase				,	"constant"				,	0.0    )



class   __lowercase				( nn.Module	):




			"""simple docstring"""







			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     bool = False   ,						lowerCAmelCase__  :     Optional[bool] = True   ,						lowerCAmelCase__  :     Optional[bool or str] = True   ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       config

									if in_channels % groups != 0:
															raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups.")
									if out_channels % groups != 0:
															raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups.")

									SCREAMING_SNAKE_CASE_: int  		=       0 if config.tf_padding else int((kernel_size - 1) / 2)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       nn.Convad(
									    in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=lowerCAmelCase__   ,						stride=lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						groups=lowerCAmelCase__   ,						bias=lowerCAmelCase__   ,						padding_mode="zeros"   ,						)

									if use_normalization:
															SCREAMING_SNAKE_CASE_: str  		=       nn.BatchNormad(
															    num_features=lowerCAmelCase__   ,						eps=config.layer_norm_eps   ,						momentum=0.9997   ,						affine=lowerCAmelCase__   ,						track_running_stats=lowerCAmelCase__   ,						)
									else:
															SCREAMING_SNAKE_CASE_: str  		=       None

									if use_activation:
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[use_activation]
															elif isinstance(config.hidden_act   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[config.hidden_act]
															else:
																					SCREAMING_SNAKE_CASE_: Any  		=       config.hidden_act
									else:
															SCREAMING_SNAKE_CASE_: int  		=       None




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     torch.Tensor):
									if self.config.tf_padding:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       apply_tf_padding(lowerCAmelCase__   ,						self.convolution)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.convolution(lowerCAmelCase__)
									if self.normalization is not None:
															SCREAMING_SNAKE_CASE_: int  		=       self.normalization(lowerCAmelCase__)
									if self.activation is not None:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       self.activation(lowerCAmelCase__)
									return features







class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     MobileNetVaConfig
			_UpperCAmelCase   :							List[Any]										=     load_tf_weights_in_mobilenet_va
			_UpperCAmelCase   :							List[Any]										=     '''mobilenet_v1'''
			_UpperCAmelCase   :							int										=     '''pixel_values'''
			_UpperCAmelCase   :							List[Any]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Union[nn.Linear, nn.Convad]):
									if isinstance(lowerCAmelCase__   ,						(nn.Linear, nn.Convad)):
															module.weight.data.normal_(mean=0.0   ,						std=self.config.initializer_range)
															if module.bias is not None:
																					module.bias.data.zero_()
									elif isinstance(lowerCAmelCase__   ,						nn.BatchNormad):
															module.bias.data.zero_()
															module.weight.data.fill_(1.0)







lowerCAmelCase							:  Any   	=						R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase							:  List[str]   	=						R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""



@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''		,       UpperCAmelCase_		,       )
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Dict   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     bool = True):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       config

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       32
									SCREAMING_SNAKE_CASE_: Dict  		=       max(int(depth * config.depth_multiplier)   ,						config.min_depth)

									SCREAMING_SNAKE_CASE_: Tuple  		=       MobileNetVaConvLayer(
									    lowerCAmelCase__   ,						in_channels=config.num_channels   ,						out_channels=lowerCAmelCase__   ,						kernel_size=3   ,						stride=2   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

									SCREAMING_SNAKE_CASE_: str  		=       nn.ModuleList()
									for i in range(13):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       out_channels

															if strides[i] == 2 or i == 0:
																					depth *= 2
																					SCREAMING_SNAKE_CASE_: str  		=       max(int(depth * config.depth_multiplier)   ,						config.min_depth)

															self.layer.append(
															    MobileNetVaConvLayer(
															        lowerCAmelCase__   ,						in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=3   ,						stride=strides[i]   ,						groups=lowerCAmelCase__   ,						))

															self.layer.append(
															    MobileNetVaConvLayer(
															        lowerCAmelCase__   ,						in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=1   ,						))

									SCREAMING_SNAKE_CASE_: List[str]  		=       nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None

									# Initialize weights and apply final processing
									self.post_init()



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str):
									raise NotImplementedError




			@add_start_docstrings_to_model_forward(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						modality="vision"   ,						expected_output=_EXPECTED_OUTPUT_SHAPE   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_: Any  		=       return_dict if return_dict is not None else self.config.use_return_dict

									if pixel_values is None:
															raise ValueError("You have to specify pixel_values")

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.conv_stem(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       () if output_hidden_states else None

									for i, layer_module in enumerate(self.layer):
															SCREAMING_SNAKE_CASE_: Tuple  		=       layer_module(lowerCAmelCase__)

															if output_hidden_states:
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       all_hidden_states + (hidden_states,)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hidden_states

									if self.pooler is not None:
															SCREAMING_SNAKE_CASE_: int  		=       torch.flatten(self.pooler(lowerCAmelCase__)   ,						start_dim=1)
									else:
															SCREAMING_SNAKE_CASE_: List[str]  		=       None

									if not return_dict:
															return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=lowerCAmelCase__   ,						pooler_output=lowerCAmelCase__   ,						hidden_states=lowerCAmelCase__   ,						)







@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,       UpperCAmelCase_		,       )
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Any   ,						lowerCAmelCase__  :     MobileNetVaConfig):
									super().__init__(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.num_labels
									SCREAMING_SNAKE_CASE_: Dict  		=       MobileNetVaModel(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.mobilenet_va.layer[-1].convolution.out_channels

									# Classifier head
									SCREAMING_SNAKE_CASE_: str  		=       nn.Dropout(config.classifier_dropout_prob   ,						inplace=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.Linear(lowerCAmelCase__   ,						config.num_labels) if config.num_labels > 0 else nn.Identity()

									# Initialize weights and apply final processing
									self.post_init()




			@add_start_docstrings_to_model_forward(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: List[str]  		=       return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.mobilenet_va(lowerCAmelCase__   ,						output_hidden_states=lowerCAmelCase__   ,						return_dict=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       outputs.pooler_output if return_dict else outputs[1]

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.classifier(self.dropout(lowerCAmelCase__))

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       None
									if labels is not None:
															if self.config.problem_type is None:
																					if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       "regression"
																					elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																											SCREAMING_SNAKE_CASE_: int  		=       "single_label_classification"
																					else:
																											SCREAMING_SNAKE_CASE_: str  		=       "multi_label_classification"

															if self.config.problem_type == "regression":
																					SCREAMING_SNAKE_CASE_: Dict  		=       MSELoss()
																					if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE_: Any  		=       loss_fct(logits.squeeze()   ,						labels.squeeze())
																					else:
																											SCREAMING_SNAKE_CASE_: int  		=       loss_fct(lowerCAmelCase__   ,						lowerCAmelCase__)
															elif self.config.problem_type == "single_label_classification":
																					SCREAMING_SNAKE_CASE_: Any  		=       CrossEntropyLoss()
																					SCREAMING_SNAKE_CASE_: Dict  		=       loss_fct(logits.view(-1   ,						self.num_labels)   ,						labels.view(-1))
															elif self.config.problem_type == "multi_label_classification":
																					SCREAMING_SNAKE_CASE_: Dict  		=       BCEWithLogitsLoss()
																					SCREAMING_SNAKE_CASE_: Dict  		=       loss_fct(lowerCAmelCase__   ,						lowerCAmelCase__)

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

									return ImageClassifierOutputWithNoAttention(
									    loss=lowerCAmelCase__   ,						logits=lowerCAmelCase__   ,						hidden_states=outputs.hidden_states   ,						)






