


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


__snake_case      = logging.get_logger(__name__)

# General docstring
__snake_case      = "RegNetConfig"

# Base docstring
__snake_case      = "facebook/regnet-y-040"
__snake_case      = [1, 1_088, 7, 7]

# Image classification docstring
__snake_case      = "facebook/regnet-y-040"
__snake_case      = "tabby, tabby cat"

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




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




			def __init__(    self				:		Optional[Any]							,							__magic_name__				:		int							,							__magic_name__				:		int = 3							,							__magic_name__				:		Tuple = 1							,							__magic_name__				:		Optional[int] = 1							,							__magic_name__				:		Union[str, Any] = "relu"							,							**__magic_name__				:		Optional[Any]							,							):







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						# The padding and conv has been verified in
						# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
						UpperCamelCase								=       tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2    )
						UpperCamelCase								=       tf.keras.layers.ConvaD(
						    filters=__magic_name__							,							kernel_size=__magic_name__							,							strides=__magic_name__							,							padding="""VALID"""							,							groups=__magic_name__							,							use_bias=__magic_name__							,							name="""convolution"""							,							)
						UpperCamelCase								=       tf.keras.layers.BatchNormalization(epsilon=1e-5							,							momentum=0.9							,							name="""normalization"""    )
						UpperCamelCase								=       ACTaFN[activation] if activation is not None else tf.identity





			def 	lowerCamelCase_       (    self				:		Tuple							,							__magic_name__				:		Tuple    ):







						"""simple docstring"""







						UpperCamelCase								=       self.convolution(self.padding(__magic_name__    )    )
						UpperCamelCase								=       self.normalization(__magic_name__    )
						UpperCamelCase								=       self.activation(__magic_name__    )
						return hidden_state




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




			def __init__(    self				:		Any							,							__magic_name__				:		Optional[int]							,							**__magic_name__				:		Union[str, Any]    ):







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						UpperCamelCase								=       config.num_channels
						UpperCamelCase								=       TFRegNetConvLayer(
						    out_channels=config.embedding_size							,							kernel_size=3							,							stride=2							,							activation=config.hidden_act							,							name="""embedder"""							,							)





			def 	lowerCamelCase_       (    self				:		Any							,							__magic_name__				:		Dict    ):







						"""simple docstring"""







						UpperCamelCase								=       shape_list(__magic_name__    )[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)
						UpperCamelCase								=       tf.transpose(__magic_name__							,							perm=(0, 2, 3, 1)    )
						UpperCamelCase								=       self.embedder(__magic_name__    )
						return hidden_state




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




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







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						UpperCamelCase								=       tf.keras.layers.ConvaD(
						    filters=__magic_name__							,							kernel_size=1							,							strides=__magic_name__							,							use_bias=__magic_name__							,							name="""convolution"""    )
						UpperCamelCase								=       tf.keras.layers.BatchNormalization(epsilon=1e-5							,							momentum=0.9							,							name="""normalization"""    )





			def 	lowerCamelCase_       (    self				:		int							,							__magic_name__				:		Optional[Any]							,							__magic_name__				:		Optional[int] = False    ):







						"""simple docstring"""







						return self.normalization(self.convolution(__magic_name__    )							,							training=__magic_name__    )




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




			def __init__(    self				:		str							,							__magic_name__				:		Dict							,							__magic_name__				:		Tuple							,							**__magic_name__				:		Any    ):







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						UpperCamelCase								=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=__magic_name__							,							name="""pooler"""    )
						UpperCamelCase								=       [
						    tf.keras.layers.ConvaD(filters=__magic_name__							,							kernel_size=1							,							activation="""relu"""							,							name="""attention.0"""    ),
						    tf.keras.layers.ConvaD(filters=__magic_name__							,							kernel_size=1							,							activation="""sigmoid"""							,							name="""attention.2"""    ),
						]





			def 	lowerCamelCase_       (    self				:		Optional[Any]							,							__magic_name__				:		Any    ):







						"""simple docstring"""







						UpperCamelCase								=       self.pooler(__magic_name__    )
						for layer_module in self.attention:
									UpperCamelCase								=       layer_module(__magic_name__    )
						UpperCamelCase								=       hidden_state * pooled
						return hidden_state




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




			def __init__(    self				:		Tuple							,							__magic_name__				:		Any							,							__magic_name__				:		int							,							__magic_name__				:		Optional[Any]							,							__magic_name__				:		Optional[Any] = 1							,							**__magic_name__				:		Any    ):







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						UpperCamelCase								=       in_channels != out_channels or stride != 1
						UpperCamelCase								=       max(1							,							out_channels // config.groups_width    )
						UpperCamelCase								=       (
						    TFRegNetShortCut(__magic_name__							,							stride=__magic_name__							,							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.
						UpperCamelCase								=       [
						    TFRegNetConvLayer(__magic_name__							,							kernel_size=1							,							activation=config.hidden_act							,							name="""layer.0"""    ),
						    TFRegNetConvLayer(
						        __magic_name__							,							stride=__magic_name__							,							groups=__magic_name__							,							activation=config.hidden_act							,							name="""layer.1"""    ),
						    TFRegNetConvLayer(__magic_name__							,							kernel_size=1							,							activation=__magic_name__							,							name="""layer.2"""    ),
						]
						UpperCamelCase								=       ACTaFN[config.hidden_act]





			def 	lowerCamelCase_       (    self				:		Union[str, Any]							,							__magic_name__				:		List[str]    ):







						"""simple docstring"""







						UpperCamelCase								=       hidden_state
						for layer_module in self.layers:
									UpperCamelCase								=       layer_module(__magic_name__    )
						UpperCamelCase								=       self.shortcut(__magic_name__    )
						hidden_state += residual
						UpperCamelCase								=       self.activation(__magic_name__    )
						return hidden_state




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




			def __init__(    self				:		Any							,							__magic_name__				:		Tuple							,							__magic_name__				:		List[str]							,							__magic_name__				:		Any							,							__magic_name__				:		str = 1							,							**__magic_name__				:		Dict    ):







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						UpperCamelCase								=       in_channels != out_channels or stride != 1
						UpperCamelCase								=       max(1							,							out_channels // config.groups_width    )
						UpperCamelCase								=       (
						    TFRegNetShortCut(__magic_name__							,							stride=__magic_name__							,							name="""shortcut"""    )
						    if should_apply_shortcut
						    else tf.keras.layers.Activation("""linear"""							,							name="""shortcut"""    )
						)
						UpperCamelCase								=       [
						    TFRegNetConvLayer(__magic_name__							,							kernel_size=1							,							activation=config.hidden_act							,							name="""layer.0"""    ),
						    TFRegNetConvLayer(
						        __magic_name__							,							stride=__magic_name__							,							groups=__magic_name__							,							activation=config.hidden_act							,							name="""layer.1"""    ),
						    TFRegNetSELayer(__magic_name__							,							reduced_channels=int(round(in_channels / 4    )    )							,							name="""layer.2"""    ),
						    TFRegNetConvLayer(__magic_name__							,							kernel_size=1							,							activation=__magic_name__							,							name="""layer.3"""    ),
						]
						UpperCamelCase								=       ACTaFN[config.hidden_act]





			def 	lowerCamelCase_       (    self				:		Union[str, Any]							,							__magic_name__				:		Optional[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       hidden_state
						for layer_module in self.layers:
									UpperCamelCase								=       layer_module(__magic_name__    )
						UpperCamelCase								=       self.shortcut(__magic_name__    )
						hidden_state += residual
						UpperCamelCase								=       self.activation(__magic_name__    )
						return hidden_state




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




			def __init__(    self				:		Any							,							__magic_name__				:		List[str]							,							__magic_name__				:		int							,							__magic_name__				:		Dict							,							__magic_name__				:		Tuple = 2							,							__magic_name__				:		str = 2							,							**__magic_name__				:		Tuple    ):







						"""simple docstring"""







						super().__init__(**__magic_name__    )

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





			def 	lowerCamelCase_       (    self				:		Tuple							,							__magic_name__				:		Union[str, Any]    ):







						"""simple docstring"""







						for layer_module in self.layers:
									UpperCamelCase								=       layer_module(__magic_name__    )
						return hidden_state




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




			def __init__(    self				:		Union[str, Any]							,							__magic_name__				:		Union[str, Any]							,							**__magic_name__				:		Optional[Any]    ):







						"""simple docstring"""







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





			def 	lowerCamelCase_       (    self				:		Union[str, Any]							,							__magic_name__				:		int							,							__magic_name__				:		Optional[int] = False							,							__magic_name__				:		List[str] = True    ):







						"""simple docstring"""







						UpperCamelCase								=       () if output_hidden_states else None

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

									UpperCamelCase								=       stage_module(__magic_name__    )

						if output_hidden_states:
									UpperCamelCase								=       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=__magic_name__							,							hidden_states=__magic_name__    )




@keras_serializable
class  UpperCAmelCase       (							tf.keras.layers.Layer     ):
			lowercase						       =			RegNetConfig




			def __init__(    self				:		List[str]							,							__magic_name__				:		List[str]							,							**__magic_name__				:		List[str]    ):







						"""simple docstring"""







						super().__init__(**__magic_name__    )
						UpperCamelCase								=       config
						UpperCamelCase								=       TFRegNetEmbeddings(__magic_name__							,							name="""embedder"""    )
						UpperCamelCase								=       TFRegNetEncoder(__magic_name__							,							name="""encoder"""    )
						UpperCamelCase								=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=__magic_name__							,							name="""pooler"""    )





			@unpack_inputs
			def 	lowerCamelCase_       (    self				:		Any							,							__magic_name__				:		List[Any]							,							__magic_name__				:		List[str] = None							,							__magic_name__				:		List[Any] = None							,							__magic_name__				:		str = False							,							):







						"""simple docstring"""







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

						UpperCamelCase								=       self.embedder(__magic_name__							,							training=__magic_name__    )

						UpperCamelCase								=       self.encoder(
						    __magic_name__							,							output_hidden_states=__magic_name__							,							return_dict=__magic_name__							,							training=__magic_name__    )

						UpperCamelCase								=       encoder_outputs[0]
						UpperCamelCase								=       self.pooler(__magic_name__    )

						# Change to NCHW output format have uniformity in the modules
						UpperCamelCase								=       tf.transpose(__magic_name__							,							perm=(0, 3, 1, 2)    )
						UpperCamelCase								=       tf.transpose(__magic_name__							,							perm=(0, 3, 1, 2)    )

						# Change the other hidden state outputs to NCHW as well
						if output_hidden_states:
									UpperCamelCase								=       tuple([tf.transpose(__magic_name__							,							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=__magic_name__							,							pooler_output=__magic_name__							,							hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states							,							)




class  UpperCAmelCase       (							UpperCamelCase__     ):
			lowercase						       =			RegNetConfig
			lowercase						       =			'regnet'
			lowercase						       =			'pixel_values'




			@property
			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







						return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)							,							dtype=tf.floataa    )}


__snake_case      = R"\n    Parameters:\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        config ([`RegNetConfig`]): 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"

__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            [`ConveNextImageProcessor.__call__`] for details.\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"




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




			def __init__(    self				:		List[str]							,							__magic_name__				:		str							,							*__magic_name__				:		str							,							**__magic_name__				:		Dict    ):







						"""simple docstring"""







						super().__init__(__magic_name__							,							*__magic_name__							,							**__magic_name__    )
						UpperCamelCase								=       TFRegNetMainLayer(__magic_name__							,							name="""regnet"""    )





			@unpack_inputs
			@add_start_docstrings_to_model_forward(__magic_name__    )
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC							,							output_type=__magic_name__							,							config_class=_CONFIG_FOR_DOC							,							modality="""vision"""							,							expected_output=_EXPECTED_OUTPUT_SHAPE							,							)
			def 	lowerCamelCase_       (    self				:		Any							,							__magic_name__				:		List[str]							,							__magic_name__				:		int = None							,							__magic_name__				:		str = None							,							__magic_name__				:		List[str]=False							,							):







						"""simple docstring"""







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

						UpperCamelCase								=       self.regnet(
						    pixel_values=__magic_name__							,							output_hidden_states=__magic_name__							,							return_dict=__magic_name__							,							training=__magic_name__							,							)
						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(
    """\n    RegNet 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  UpperCAmelCase       (							UpperCamelCase__   ,			UpperCamelCase__     ):




			def __init__(    self				:		Union[str, Any]							,							__magic_name__				:		Any							,							*__magic_name__				:		int							,							**__magic_name__				:		Tuple    ):







						"""simple docstring"""







						super().__init__(__magic_name__							,							*__magic_name__							,							**__magic_name__    )
						UpperCamelCase								=       config.num_labels
						UpperCamelCase								=       TFRegNetMainLayer(__magic_name__							,							name="""regnet"""    )
						# classification head
						UpperCamelCase								=       [
						    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(__magic_name__    )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT							,							output_type=__magic_name__							,							config_class=_CONFIG_FOR_DOC							,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,							)
			def 	lowerCamelCase_       (    self				:		Dict							,							__magic_name__				:		Dict = None							,							__magic_name__				:		Optional[Any] = None							,							__magic_name__				:		int = None							,							__magic_name__				:		List[str] = None							,							__magic_name__				:		List[Any]=False							,							):







						"""simple docstring"""







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

						UpperCamelCase								=       self.regnet(
						    __magic_name__							,							output_hidden_states=__magic_name__							,							return_dict=__magic_name__							,							training=__magic_name__    )

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

						UpperCamelCase								=       self.classifier[0](__magic_name__    )
						UpperCamelCase								=       self.classifier[1](__magic_name__    )

						UpperCamelCase								=       None if labels is None else self.hf_compute_loss(labels=__magic_name__							,							logits=__magic_name__    )

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

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






'''simple docstring'''
from typing import Any







class   a_ :




			def __init__(       self					,   UpperCAmelCase	):
				a_      				=						data
				a_      				=						None







class   a_ :




			def __init__(       self	):
				a_      				=						None




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.head
				while temp is not None:
					print(temp.data					,   end=""" """	)
					a_      				=						temp.next
				print()




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						Node(UpperCAmelCase	)
				a_      				=						self.head
				a_      				=						new_node






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				if node_data_a == node_data_a:
					return
				else:
					a_      				=						self.head
					while node_a is not None and node_a.data != node_data_a:
						a_      				=						node_a.next

					a_      				=						self.head
					while node_a is not None and node_a.data != node_data_a:
						a_      				=						node_a.next

					if node_a is None or node_a is None:
						return

					a_				,  a_      				=						node_a.data, node_a.data


if __name__ == "__main__":
		lowercase__											=LinkedList()
		for i in range(5, 0, -1):
				ll.push(i)

		ll.print_list()

		ll.swap_nodes(1, 4)
		print('After swapping')
		ll.print_list()
