

from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase							:  Union[str, Any]            =						logging.get_logger(__name__)

# General docstring
lowerCAmelCase							:  str            =						"""RegNetConfig"""

# Base docstring
lowerCAmelCase							:  Optional[int]            =						"""facebook/regnet-y-040"""
lowerCAmelCase							:  int            =						[1, 1088, 7, 7]

# Image classification docstring
lowerCAmelCase							:  Optional[Any]            =						"""facebook/regnet-y-040"""
lowerCAmelCase							:  Any            =						"""tabby, tabby cat"""

lowerCAmelCase							:  Optional[Any]            =						[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



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




			"""simple docstring"""







			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int = 3       ,						lowerCAmelCase__  :					int = 1       ,						lowerCAmelCase__  :					int = 1       ,						lowerCAmelCase__  :					Optional[str] = "relu"       ,						**lowerCAmelCase__  :					str       ,						):
									super().__init__(**lowerCAmelCase__)
									# The padding and conv has been verified in
									# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
									SCREAMING_SNAKE_CASE_:			str  		=	tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tf.keras.layers.ConvaD(
									    filters=lowerCAmelCase__       ,						kernel_size=lowerCAmelCase__       ,						strides=lowerCAmelCase__       ,						padding="VALID"       ,						groups=lowerCAmelCase__       ,						use_bias=lowerCAmelCase__       ,						name="convolution"       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	tf.keras.layers.BatchNormalization(epsilon=1E-5       ,						momentum=0.9       ,						name="normalization")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	ACTaFN[activation] if activation is not None else tf.identity




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.convolution(self.padding(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.normalization(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.activation(lowerCAmelCase__)
									return hidden_state







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




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					RegNetConfig       ,						**lowerCAmelCase__  :					Dict):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	config.num_channels
									SCREAMING_SNAKE_CASE_:			Tuple  		=	TFRegNetConvLayer(
									    out_channels=config.embedding_size       ,						kernel_size=3       ,						stride=2       ,						activation=config.hidden_act       ,						name="embedder"       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	shape_list(lowerCAmelCase__)[1]
									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.")

									# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
									# So change the input format from `NCHW` to `NHWC`.
									# shape = (batch_size, in_height, in_width, in_channels=num_channels)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tf.transpose(lowerCAmelCase__       ,						perm=(0, 2, 3, 1))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.embedder(lowerCAmelCase__)
									return hidden_state







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




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int = 2       ,						**lowerCAmelCase__  :					List[str]):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	tf.keras.layers.ConvaD(
									    filters=lowerCAmelCase__       ,						kernel_size=1       ,						strides=lowerCAmelCase__       ,						use_bias=lowerCAmelCase__       ,						name="convolution")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tf.keras.layers.BatchNormalization(epsilon=1E-5       ,						momentum=0.9       ,						name="normalization")




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					tf.Tensor       ,						lowerCAmelCase__  :					bool = False):
									return self.normalization(self.convolution(lowerCAmelCase__)       ,						training=lowerCAmelCase__)







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




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						**lowerCAmelCase__  :					Dict):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase__       ,						name="pooler")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[
									    tf.keras.layers.ConvaD(filters=lowerCAmelCase__       ,						kernel_size=1       ,						activation="relu"       ,						name="attention.0"),
									    tf.keras.layers.ConvaD(filters=lowerCAmelCase__       ,						kernel_size=1       ,						activation="sigmoid"       ,						name="attention.2"),
									]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					str):
									# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.pooler(lowerCAmelCase__)
									for layer_module in self.attention:
															SCREAMING_SNAKE_CASE_:			Any  		=	layer_module(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	hidden_state * pooled
									return hidden_state







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




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					RegNetConfig       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int = 1       ,						**lowerCAmelCase__  :					Dict):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	in_channels != out_channels or stride != 1
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	max(1       ,						out_channels // config.groups_width)
									SCREAMING_SNAKE_CASE_:			str  		=	(
									    TFRegNetShortCut(lowerCAmelCase__       ,						stride=lowerCAmelCase__       ,						name="shortcut")
									    if should_apply_shortcut
									    else tf.keras.layers.Activation("linear"       ,						name="shortcut")
									)
									# `self.layers` instead of `self.layer` because that is a reserved argument.
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[
									    TFRegNetConvLayer(lowerCAmelCase__       ,						kernel_size=1       ,						activation=config.hidden_act       ,						name="layer.0"),
									    TFRegNetConvLayer(
									        lowerCAmelCase__       ,						stride=lowerCAmelCase__       ,						groups=lowerCAmelCase__       ,						activation=config.hidden_act       ,						name="layer.1"),
									    TFRegNetConvLayer(lowerCAmelCase__       ,						kernel_size=1       ,						activation=lowerCAmelCase__       ,						name="layer.2"),
									]
									SCREAMING_SNAKE_CASE_:			List[str]  		=	ACTaFN[config.hidden_act]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					Dict):
									SCREAMING_SNAKE_CASE_:			str  		=	hidden_state
									for layer_module in self.layers:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	layer_module(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.shortcut(lowerCAmelCase__)
									hidden_state += residual
									SCREAMING_SNAKE_CASE_:			str  		=	self.activation(lowerCAmelCase__)
									return hidden_state







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




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					RegNetConfig       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int = 1       ,						**lowerCAmelCase__  :					Optional[Any]):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	in_channels != out_channels or stride != 1
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	max(1       ,						out_channels // config.groups_width)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	(
									    TFRegNetShortCut(lowerCAmelCase__       ,						stride=lowerCAmelCase__       ,						name="shortcut")
									    if should_apply_shortcut
									    else tf.keras.layers.Activation("linear"       ,						name="shortcut")
									)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[
									    TFRegNetConvLayer(lowerCAmelCase__       ,						kernel_size=1       ,						activation=config.hidden_act       ,						name="layer.0"),
									    TFRegNetConvLayer(
									        lowerCAmelCase__       ,						stride=lowerCAmelCase__       ,						groups=lowerCAmelCase__       ,						activation=config.hidden_act       ,						name="layer.1"),
									    TFRegNetSELayer(lowerCAmelCase__       ,						reduced_channels=int(round(in_channels / 4))       ,						name="layer.2"),
									    TFRegNetConvLayer(lowerCAmelCase__       ,						kernel_size=1       ,						activation=lowerCAmelCase__       ,						name="layer.3"),
									]
									SCREAMING_SNAKE_CASE_:			str  		=	ACTaFN[config.hidden_act]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					str):
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_state
									for layer_module in self.layers:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	layer_module(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.shortcut(lowerCAmelCase__)
									hidden_state += residual
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.activation(lowerCAmelCase__)
									return hidden_state







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




			"""simple docstring"""







			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					RegNetConfig       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int = 2       ,						lowerCAmelCase__  :					int = 2       ,						**lowerCAmelCase__  :					int):
									super().__init__(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[
									    # downsampling is done in the first layer with stride of 2
									    layer(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						stride=lowerCAmelCase__       ,						name="layers.0"),
									    *[layer(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						name=F"layers.{i+1}") for i in range(depth - 1)],
									]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Optional[int]):
									for layer_module in self.layers:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	layer_module(lowerCAmelCase__)
									return hidden_state







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




			"""simple docstring"""







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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					tf.Tensor       ,						lowerCAmelCase__  :					bool = False       ,						lowerCAmelCase__  :					bool = True):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	() if output_hidden_states else None

									for stage_module in self.stages:
															if output_hidden_states:
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_states + (hidden_state,)

															SCREAMING_SNAKE_CASE_:			Any  		=	stage_module(lowerCAmelCase__)

									if output_hidden_states:
															SCREAMING_SNAKE_CASE_:			List[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=lowerCAmelCase__       ,						hidden_states=lowerCAmelCase__)







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




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					RegNetConfig



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					Optional[Any]       ,						**lowerCAmelCase__  :					Optional[Any]):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	config
									SCREAMING_SNAKE_CASE_:			List[str]  		=	TFRegNetEmbeddings(lowerCAmelCase__       ,						name="embedder")
									SCREAMING_SNAKE_CASE_:			str  		=	TFRegNetEncoder(lowerCAmelCase__       ,						name="encoder")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase__       ,						name="pooler")




			@unpack_inputs
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					tf.Tensor       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					bool = False       ,						):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_:			Any  		=	self.embedder(lowerCAmelCase__       ,						training=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.encoder(
									    lowerCAmelCase__       ,						output_hidden_states=lowerCAmelCase__       ,						return_dict=lowerCAmelCase__       ,						training=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	encoder_outputs[0]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.pooler(lowerCAmelCase__)

									# Change to NCHW output format have uniformity in the modules
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf.transpose(lowerCAmelCase__       ,						perm=(0, 3, 1, 2))
									SCREAMING_SNAKE_CASE_:			Dict  		=	tf.transpose(lowerCAmelCase__       ,						perm=(0, 3, 1, 2))

									# Change the other hidden state outputs to NCHW as well
									if output_hidden_states:
															SCREAMING_SNAKE_CASE_:			Dict  		=	tuple([tf.transpose(lowerCAmelCase__       ,						perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])

									if not return_dict:
															return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return TFBaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=lowerCAmelCase__       ,						pooler_output=lowerCAmelCase__       ,						hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states       ,						)







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[int]      							=					RegNetConfig
			_UpperCAmelCase       :							Union[str, Any]      							=					'''regnet'''
			_UpperCAmelCase       :							Tuple      							=					'''pixel_values'''



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)       ,						dtype=tf.floataa)}







lowerCAmelCase							:  Any            =						R"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase							:  Optional[int]            =						R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top.'''		,	UpperCAmelCase_		,	)
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					int       ,						lowerCAmelCase__  :					RegNetConfig       ,						*lowerCAmelCase__  :					Dict       ,						**lowerCAmelCase__  :					List[str]):
									super().__init__(lowerCAmelCase__       ,						*lowerCAmelCase__       ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TFRegNetMainLayer(lowerCAmelCase__       ,						name="regnet")




			@unpack_inputs
			@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  :					str       ,						lowerCAmelCase__  :					tf.Tensor       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Tuple=False       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.regnet(
									    pixel_values=lowerCAmelCase__       ,						output_hidden_states=lowerCAmelCase__       ,						return_dict=lowerCAmelCase__       ,						training=lowerCAmelCase__       ,						)
									if not return_dict:
															return (outputs[0],) + outputs[1:]

									return TFBaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=outputs.last_hidden_state       ,						pooler_output=outputs.pooler_output       ,						hidden_states=outputs.hidden_states       ,						)







@add_start_docstrings(
    '''
    RegNet 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_		,	UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					RegNetConfig       ,						*lowerCAmelCase__  :					List[str]       ,						**lowerCAmelCase__  :					Union[str, Any]):
									super().__init__(lowerCAmelCase__       ,						*lowerCAmelCase__       ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	config.num_labels
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	TFRegNetMainLayer(lowerCAmelCase__       ,						name="regnet")
									# classification head
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[
									    tf.keras.layers.Flatten(),
									    tf.keras.layers.Dense(config.num_labels       ,						name="classifier.1") if config.num_labels > 0 else tf.identity,
									]




			@unpack_inputs
			@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  :					Dict       ,						lowerCAmelCase__  :					tf.Tensor = None       ,						lowerCAmelCase__  :					tf.Tensor = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					Dict=False       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_:			str  		=	return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.regnet(
									    lowerCAmelCase__       ,						output_hidden_states=lowerCAmelCase__       ,						return_dict=lowerCAmelCase__       ,						training=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.classifier[0](lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	self.classifier[1](lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	None if labels is None else self.hf_compute_loss(labels=lowerCAmelCase__       ,						logits=lowerCAmelCase__)

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

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









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






