






import numpy as np
from matplotlib import pyplot as plt



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any      )  -> Dict:





    """simple docstring"""

    return 1 / (1 + np.exp(-1 * x      ))


class  __lowercase :







     '''simple docstring'''




     def __init__(			self    :     str	,		UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     int=None	,		UpperCamelCase_    :     List[str]=None	,		UpperCamelCase_    :     List[Any]=False			):


         """simple docstring"""



         __A             =		units
         __A             =		None
         __A             =		None
         __A             =		activation
         if learning_rate is None:
             __A             =		0.3
         __A             =		learning_rate
         __A             =		is_input_layer




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     List[str]			):


         """simple docstring"""



         __A             =		np.asmatrix(np.random.normal(0	,		0.5	,		(self.units, back_units)			)			)
         __A             =		np.asmatrix(np.random.normal(0	,		0.5	,		self.units			)			).T
         if self.activation is None:
             __A             =		sigmoid




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         if self.activation == sigmoid:
             __A             =		np.dot(self.output	,		(1 - self.output).T			)
             __A             =		np.diag(np.diag(UpperCamelCase_			)			)
         else:
             __A             =		1
         return gradient_activation




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		xdata
         if self.is_input_layer:
             # input layer
             __A             =		xdata
             __A             =		xdata
             return xdata
         else:
             __A             =		np.dot(self.weight	,		self.xdata			) - self.bias
             __A             =		self.activation(self.wx_plus_b			)
             return self.output





     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		self.cal_gradient()  # i * i 维
         __A             =		np.asmatrix(np.dot(gradient.T	,		UpperCamelCase_			)			)

         __A             =		np.asmatrix(self.xdata			)
         __A             =		-1
         __A             =		self.weight

         __A             =		np.dot(gradient.T	,		self._gradient_weight.T			)
         __A             =		gradient * self._gradient_bias
         __A             =		np.dot(UpperCamelCase_	,		self._gradient_x			).T
         # upgrade: the Negative gradient direction
         __A             =		self.weight - self.learn_rate * self.gradient_weight
         __A             =		self.bias - self.learn_rate * self.gradient_bias.T
         # updates the weights and bias according to learning rate (0.3 if undefined)
         return self.gradient


class  __lowercase :







     '''simple docstring'''




     def __init__(			self    :     Optional[int]			):


         """simple docstring"""



         __A             =		[]
         __A             =		[]
         __A             =		plt.figure()
         __A             =		self.fig_loss.add_subplot(1	,		1	,		1			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     Optional[Any]			):


         """simple docstring"""



         self.layers.append(UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         for i, layer in enumerate(self.layers[:]			):
             if i < 1:
                 __A             =		True
             else:
                 layer.initializer(self.layers[i - 1].units			)




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         for i, layer in enumerate(self.layers[:]			):
             print(F"------- layer {i} -------"			)
             print("""weight.shape """	,		np.shape(layer.weight			)			)
             print("""bias.shape """	,		np.shape(layer.bias			)			)




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Dict	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     Union[str, Any]	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		train_round
         __A             =		accuracy

         self.ax_loss.hlines(self.accuracy	,		0	,		self.train_round * 1.1			)

         __A             =		np.shape(UpperCamelCase_			)
         for _ in range(UpperCamelCase_			):
             __A             =		0
             for row in range(x_shape[0]			):
                 __A             =		np.asmatrix(xdata[row, :]			).T
                 __A             =		np.asmatrix(ydata[row, :]			).T

                 # forward propagation
                 for layer in self.layers:
                     __A             =		layer.forward_propagation(_xdata			)

                 __A      ,		__A             =		self.cal_loss(_ydata	,		_xdata			)
                 __A             =		all_loss + loss

                 # back propagation: the input_layer does not upgrade
                 for layer in self.layers[:0:-1]:
                     __A             =		layer.back_propagation(UpperCamelCase_			)

             __A             =		all_loss / x_shape[0]
             self.train_mse.append(UpperCamelCase_			)

             self.plot_loss()

             if mse < self.accuracy:
                 print("""----达到精度----"""			)
                 return mse
         return None




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         __A             =		np.sum(np.power((ydata - ydata_)	,		2			)			)
         __A             =		2 * (ydata_ - ydata)
         # vector (shape is the same as _ydata.shape)
         return self.loss, self.loss_gradient





     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         if self.ax_loss.lines:
             self.ax_loss.lines.remove(self.ax_loss.lines[0]			)
         self.ax_loss.plot(self.train_mse	,		"""r-"""			)
         plt.ion()
         plt.xlabel("""step"""			)
         plt.ylabel("""loss"""			)
         plt.show()
         plt.pause(0.1			)



def      _SCREAMING_SNAKE_CASE  (	)  -> Union[str, Any]:





    """simple docstring"""

    __A             =		np.random.randn(1_0     ,			1_0      )
    __A             =		np.asarray(
        [
            [0.8, 0.4],
            [0.4, 0.3],
            [0.34, 0.45],
            [0.67, 0.32],
            [0.88, 0.67],
            [0.78, 0.77],
            [0.55, 0.66],
            [0.55, 0.43],
            [0.54, 0.1],
            [0.1, 0.5],
        ]      )
    __A             =		BPNN()
    for i in (1_0, 2_0, 3_0, 2):
        model.add_layer(DenseLayer(__lowercase      )      )
    model.build()
    model.summary()
    model.train(xdata=__lowercase     ,			ydata=__lowercase     ,			train_round=1_0_0     ,			accuracy=0.01      )


if __name__ == "__main__":
  example()












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


__a :    List[Any]								=				logging.get_logger(__name__)

# General docstring
__a :    Any								=				"ResNetConfig"

# Base docstring
__a :    List[str]								=				"microsoft/resnet-50"
__a :    Dict								=				[1, 2048, 7, 7]

# Image classification docstring
__a :    Optional[int]								=				"microsoft/resnet-50"
__a :    Dict								=				"tiger cat"

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


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







     '''simple docstring'''




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 3	,		UpperCamelCase_    :     int = 1	,		UpperCamelCase_    :     str = "relu"	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     tf.Tensor			):


         """simple docstring"""



         __A             =		__A             =		(self.pad_value, self.pad_value)
         __A             =		tf.pad(UpperCamelCase_	,		[(0, 0), height_pad, width_pad, (0, 0)]			)
         __A             =		self.conv(UpperCamelCase_			)
         return hidden_state





     def 						lowerCAmelCase_       (			self    :     Optional[int]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		self.convolution(UpperCamelCase_			)
         __A             =		self.normalization(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.activation(UpperCamelCase_			)
         return hidden_state


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







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		TFResNetConvLayer(
             config.embedding_size	,		kernel_size=7	,		stride=2	,		activation=config.hidden_act	,		name="""embedder"""	,		)
         __A             =		tf.keras.layers.MaxPoolaD(pool_size=3	,		strides=2	,		padding="""valid"""	,		name="""pooler"""			)
         __A             =		config.num_channels





     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A      ,		__A      ,		__A      ,		__A             =		shape_list(UpperCamelCase_			)
         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."""			)
         __A             =		pixel_values
         __A             =		self.embedder(UpperCamelCase_			)
         __A             =		tf.pad(UpperCamelCase_	,		[[0, 0], [1, 1], [1, 1], [0, 0]]			)
         __A             =		self.pooler(UpperCamelCase_			)
         return hidden_state


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







     '''simple docstring'''




     def __init__(			self    :     str	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 2	,		**UpperCamelCase_    :     Optional[Any]			):


         """simple docstring"""



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





     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		x
         __A             =		self.convolution(UpperCamelCase_			)
         __A             =		self.normalization(UpperCamelCase_	,		training=UpperCamelCase_			)
         return hidden_state


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







     '''simple docstring'''




     def __init__(			self    :     List[str]	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 1	,		UpperCamelCase_    :     str = "relu"	,		**UpperCamelCase_    :     Any			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		in_channels != out_channels or stride != 1
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""layer.0"""			)
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		activation=UpperCamelCase_	,		name="""layer.1"""			)
         __A             =		(
             TFResNetShortCut(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""shortcut"""			)
             if should_apply_shortcut
             else tf.keras.layers.Activation("""linear"""	,		name="""shortcut"""			)
         )
         __A             =		ACTaFN[activation]





     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		hidden_state
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.shortcut(UpperCamelCase_	,		training=UpperCamelCase_			)
         hidden_state += residual
         __A             =		self.activation(UpperCamelCase_			)
         return hidden_state


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







     '''simple docstring'''




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 1	,		UpperCamelCase_    :     str = "relu"	,		UpperCamelCase_    :     int = 4	,		**UpperCamelCase_    :     str	,		):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		in_channels != out_channels or stride != 1
         __A             =		out_channels // reduction
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		kernel_size=1	,		name="""layer.0"""			)
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""layer.1"""			)
         __A             =		TFResNetConvLayer(UpperCamelCase_	,		kernel_size=1	,		activation=UpperCamelCase_	,		name="""layer.2"""			)
         __A             =		(
             TFResNetShortCut(UpperCamelCase_	,		stride=UpperCamelCase_	,		name="""shortcut"""			)
             if should_apply_shortcut
             else tf.keras.layers.Activation("""linear"""	,		name="""shortcut"""			)
         )
         __A             =		ACTaFN[activation]





     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         __A             =		hidden_state
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.conva(UpperCamelCase_	,		training=UpperCamelCase_			)
         __A             =		self.shortcut(UpperCamelCase_	,		training=UpperCamelCase_			)
         hidden_state += residual
         __A             =		self.activation(UpperCamelCase_			)
         return hidden_state


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







     '''simple docstring'''




     def __init__(			self    :     str	,		UpperCamelCase_    :     ResNetConfig	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int = 2	,		UpperCamelCase_    :     int = 2	,		**UpperCamelCase_    :     Optional[int]			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)

         __A             =		TFResNetBottleNeckLayer if config.layer_type == """bottleneck""" else TFResNetBasicLayer

         __A             =		[layer(UpperCamelCase_	,		UpperCamelCase_	,		stride=UpperCamelCase_	,		activation=config.hidden_act	,		name="""layers.0"""			)]
         layers += [
             layer(UpperCamelCase_	,		UpperCamelCase_	,		activation=config.hidden_act	,		name=F"layers.{i + 1}"			)
             for i in range(depth - 1			)
         ]
         __A             =		layers





     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False			):


         """simple docstring"""



         for layer in self.stage_layers:
             __A             =		layer(UpperCamelCase_	,		training=UpperCamelCase_			)
         return hidden_state


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







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
         __A             =		[
             TFResNetStage(
                 UpperCamelCase_	,		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(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		depth=UpperCamelCase_	,		name=F"stages.{i + 1}"			)			)





     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     bool = False	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		() if output_hidden_states else None

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

             __A             =		stage_module(UpperCamelCase_	,		training=UpperCamelCase_			)

         if output_hidden_states:
             __A             =		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=UpperCamelCase_	,		hidden_states=UpperCamelCase_			)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			ResNetConfig
     SCREAMING_SNAKE_CASE				  =			"resnet"
     SCREAMING_SNAKE_CASE				  =			"pixel_values"




     @property
     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)	,		dtype=tf.floataa			)}
__a :    Union[str, Any]								=				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"


__a :    Tuple								=				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  __lowercase (		tf.keras.layers.Layer					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			ResNetConfig




     def __init__(			self    :     str	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     str			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)
         __A             =		config
         __A             =		TFResNetEmbeddings(UpperCamelCase_	,		name="""embedder"""			)
         __A             =		TFResNetEncoder(UpperCamelCase_	,		name="""encoder"""			)
         __A             =		tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase_			)





     @unpack_inputs
     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __A             =		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)
         __A             =		tf.transpose(UpperCamelCase_	,		perm=[0, 2, 3, 1]			)
         __A             =		self.embedder(UpperCamelCase_	,		training=UpperCamelCase_			)

         __A             =		self.encoder(
             UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		return_dict=UpperCamelCase_	,		training=UpperCamelCase_			)

         __A             =		encoder_outputs[0]

         __A             =		self.pooler(UpperCamelCase_			)

         # Transpose all the outputs to the NCHW format
         # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
         __A             =		tf.transpose(UpperCamelCase_	,		(0, 3, 1, 2)			)
         __A             =		tf.transpose(UpperCamelCase_	,		(0, 3, 1, 2)			)
         __A             =		()
         for hidden_state in encoder_outputs[1:]:
             __A             =		hidden_states + tuple(tf.transpose(UpperCamelCase_	,		(0, 3, 1, 2)			) for h in hidden_state			)

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

         __A             =		hidden_states if output_hidden_states else None

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


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







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Dict			):


         """simple docstring"""



         super().__init__(UpperCamelCase_	,		**UpperCamelCase_			)
         __A             =		TFResNetMainLayer(config=UpperCamelCase_	,		name="""resnet"""			)





     @add_start_docstrings_to_model_forward(UpperCamelCase_			)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC	,		output_type=UpperCamelCase_	,		config_class=_CONFIG_FOR_DOC	,		modality="""vision"""	,		expected_output=_EXPECTED_OUTPUT_SHAPE	,		)
     @unpack_inputs
     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     tf.Tensor	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     Optional[bool] = None	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __A             =		return_dict if return_dict is not None else self.config.use_return_dict

         __A             =		self.resnet(
             pixel_values=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		return_dict=UpperCamelCase_	,		training=UpperCamelCase_	,		)
         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    "     , lowercase_     , )
class  __lowercase (		lowercase_     , lowercase_					):







     '''simple docstring'''




     def __init__(			self    :     int	,		UpperCamelCase_    :     ResNetConfig	,		**UpperCamelCase_    :     Any			):


         """simple docstring"""



         super().__init__(UpperCamelCase_	,		**UpperCamelCase_			)
         __A             =		config.num_labels
         __A             =		TFResNetMainLayer(UpperCamelCase_	,		name="""resnet"""			)
         # classification head
         __A             =		(
             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 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     tf.Tensor			):


         """simple docstring"""



         __A             =		tf.keras.layers.Flatten()(UpperCamelCase_			)
         __A             =		self.classifier_layer(UpperCamelCase_			)
         return logits





     @add_start_docstrings_to_model_forward(UpperCamelCase_			)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT	,		output_type=UpperCamelCase_	,		config_class=_CONFIG_FOR_DOC	,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,		)
     @unpack_inputs
     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     tf.Tensor = None	,		UpperCamelCase_    :     tf.Tensor = None	,		UpperCamelCase_    :     bool = None	,		UpperCamelCase_    :     bool = None	,		UpperCamelCase_    :     bool = False	,		):


         """simple docstring"""



         __A             =		return_dict if return_dict is not None else self.config.use_return_dict

         __A             =		self.resnet(
             UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		return_dict=UpperCamelCase_	,		training=UpperCamelCase_			)

         __A             =		outputs.pooler_output if return_dict else outputs[1]

         __A             =		self.classifier(UpperCamelCase_			)

         __A             =		None if labels is None else self.hf_compute_loss(UpperCamelCase_	,		UpperCamelCase_			)

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

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




