



'''simple docstring'''

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


UpperCAmelCase     		=   logging.get_logger(__name__)

# General docstring
UpperCAmelCase     		=   'RegNetConfig'

# Base docstring
UpperCAmelCase     		=   'facebook/regnet-y-040'
UpperCAmelCase     		=   [1, 1088, 7, 7]

# Image classification docstring
UpperCAmelCase     		=   'facebook/regnet-y-040'
UpperCAmelCase     		=   'tabby, tabby cat'

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

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


						'''simple docstring'''



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




						def     __snake_case   ( self     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.convolution(self.padding(A_				)				)
							lowerCAmelCase   =     self.normalization(A_				)
							lowerCAmelCase   =     self.activation(A_				)
							return hidden_state







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


						'''simple docstring'''



						def __init__( self     ,	A_     ,	**A_				)     ->     str:
							super().__init__(**A_				)
							lowerCAmelCase   =     config.num_channels
							lowerCAmelCase   =     TFRegNetConvLayer(
							    out_channels=config.embedding_size     ,	kernel_size=3     ,	stride=2     ,	activation=config.hidden_act     ,	name="""embedder"""     ,	)




						def     __snake_case   ( self     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     shape_list(A_				)[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)
							lowerCAmelCase   =     tf.transpose(A_     ,	perm=(0, 2, 3, 1)				)
							lowerCAmelCase   =     self.embedder(A_				)
							return hidden_state







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


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_ = 2     ,	**A_				)     ->     str:
							super().__init__(**A_				)
							lowerCAmelCase   =     tf.keras.layers.ConvaD(
							    filters=A_     ,	kernel_size=1     ,	strides=A_     ,	use_bias=A_     ,	name="""convolution"""				)
							lowerCAmelCase   =     tf.keras.layers.BatchNormalization(epsilon=1e-5     ,	momentum=0.9     ,	name="""normalization"""				)




						def     __snake_case   ( self     ,	A_     ,	A_ = False				)     ->     tf.Tensor:
							return self.normalization(self.convolution(A_				)     ,	training=A_				)







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


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_     ,	**A_				)     ->     Any:
							super().__init__(**A_				)
							lowerCAmelCase   =     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_     ,	name="""pooler"""				)
							lowerCAmelCase   =     [
							    tf.keras.layers.ConvaD(filters=A_     ,	kernel_size=1     ,	activation="""relu"""     ,	name="""attention.0"""				),
							    tf.keras.layers.ConvaD(filters=A_     ,	kernel_size=1     ,	activation="""sigmoid"""     ,	name="""attention.2"""				),
							]




						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
							lowerCAmelCase   =     self.pooler(A_				)
							for layer_module in self.attention:
								lowerCAmelCase   =     layer_module(A_				)
							lowerCAmelCase   =     hidden_state * pooled
							return hidden_state







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


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_     ,	A_     ,	A_ = 1     ,	**A_				)     ->     Any:
							super().__init__(**A_				)
							lowerCAmelCase   =     in_channels != out_channels or stride != 1
							lowerCAmelCase   =     max(1     ,	out_channels // config.groups_width				)
							lowerCAmelCase   =     (
							    TFRegNetShortCut(A_     ,	stride=A_     ,	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.
							lowerCAmelCase   =     [
							    TFRegNetConvLayer(A_     ,	kernel_size=1     ,	activation=config.hidden_act     ,	name="""layer.0"""				),
							    TFRegNetConvLayer(
							        A_     ,	stride=A_     ,	groups=A_     ,	activation=config.hidden_act     ,	name="""layer.1"""				),
							    TFRegNetConvLayer(A_     ,	kernel_size=1     ,	activation=A_     ,	name="""layer.2"""				),
							]
							lowerCAmelCase   =     ACTaFN[config.hidden_act]




						def     __snake_case   ( self     ,	A_				)     ->     Any:
							lowerCAmelCase   =     hidden_state
							for layer_module in self.layers:
								lowerCAmelCase   =     layer_module(A_				)
							lowerCAmelCase   =     self.shortcut(A_				)
							hidden_state += residual
							lowerCAmelCase   =     self.activation(A_				)
							return hidden_state







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


						'''simple docstring'''



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




						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     hidden_state
							for layer_module in self.layers:
								lowerCAmelCase   =     layer_module(A_				)
							lowerCAmelCase   =     self.shortcut(A_				)
							hidden_state += residual
							lowerCAmelCase   =     self.activation(A_				)
							return hidden_state







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


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_     ,	A_     ,	A_ = 2     ,	A_ = 2     ,	**A_				)     ->     Union[str, Any]:
							super().__init__(**A_				)

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




						def     __snake_case   ( self     ,	A_				)     ->     Optional[Any]:
							for layer_module in self.layers:
								lowerCAmelCase   =     layer_module(A_				)
							return hidden_state







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


						'''simple docstring'''



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




						def     __snake_case   ( self     ,	A_     ,	A_ = False     ,	A_ = True				)     ->     TFBaseModelOutputWithNoAttention:
							lowerCAmelCase   =     () if output_hidden_states else None

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

								lowerCAmelCase   =     stage_module(A_				)

							if output_hidden_states:
								lowerCAmelCase   =     hidden_states + (hidden_state,)

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

							return TFBaseModelOutputWithNoAttention(last_hidden_state=A_     ,	hidden_states=A_				)







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


						'''simple docstring'''



						UpperCAmelCase							:   str									=		RegNetConfig


						def __init__( self     ,	A_     ,	**A_				)     ->     str:
							super().__init__(**A_				)
							lowerCAmelCase   =     config
							lowerCAmelCase   =     TFRegNetEmbeddings(A_     ,	name="""embedder"""				)
							lowerCAmelCase   =     TFRegNetEncoder(A_     ,	name="""encoder"""				)
							lowerCAmelCase   =     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_     ,	name="""pooler"""				)




						@unpack_inputs
						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	)     ->     TFBaseModelOutputWithPoolingAndNoAttention:
							lowerCAmelCase   =     (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowerCAmelCase   =     return_dict if return_dict is not None else self.config.use_return_dict

							lowerCAmelCase   =     self.embedder(A_     ,	training=A_				)

							lowerCAmelCase   =     self.encoder(
							    A_     ,	output_hidden_states=A_     ,	return_dict=A_     ,	training=A_				)

							lowerCAmelCase   =     encoder_outputs[0]
							lowerCAmelCase   =     self.pooler(A_				)

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

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







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Dict									=		RegNetConfig
						UpperCAmelCase							:   Union[str, Any]									=		"regnet"
						UpperCAmelCase							:   Tuple									=		"pixel_values"


						@property
						def     __snake_case   ( self				)     ->     int:
							return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)     ,	dtype=tf.floataa				)}







UpperCAmelCase     		=   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'

UpperCAmelCase     		=   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."		,							_lowerCAmelCase		,							)
class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	*A_     ,	**A_				)     ->     Optional[int]:
							super().__init__(A_     ,	*A_     ,	**A_				)
							lowerCAmelCase   =     TFRegNetMainLayer(A_     ,	name="""regnet"""				)




						@unpack_inputs
						@add_start_docstrings_to_model_forward(A_				)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC     ,	output_type=A_     ,	config_class=_CONFIG_FOR_DOC     ,	modality="""vision"""     ,	expected_output=_EXPECTED_OUTPUT_SHAPE     ,	)
						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_=False     ,	)     ->     Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
							lowerCAmelCase   =     (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowerCAmelCase   =     return_dict if return_dict is not None else self.config.use_return_dict

							lowerCAmelCase   =     self.regnet(
							    pixel_values=A_     ,	output_hidden_states=A_     ,	return_dict=A_     ,	training=A_     ,	)
							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    "		,							_lowerCAmelCase		,							)
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	*A_     ,	**A_				)     ->     List[Any]:
							super().__init__(A_     ,	*A_     ,	**A_				)
							lowerCAmelCase   =     config.num_labels
							lowerCAmelCase   =     TFRegNetMainLayer(A_     ,	name="""regnet"""				)
							# classification head
							lowerCAmelCase   =     [
							    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(A_				)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,	output_type=A_     ,	config_class=_CONFIG_FOR_DOC     ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,	)
						def     __snake_case   ( self     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_=False     ,	)     ->     Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
							lowerCAmelCase   =     (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowerCAmelCase   =     return_dict if return_dict is not None else self.config.use_return_dict

							lowerCAmelCase   =     self.regnet(
							    A_     ,	output_hidden_states=A_     ,	return_dict=A_     ,	training=A_				)

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

							lowerCAmelCase   =     self.classifier[0](A_				)
							lowerCAmelCase   =     self.classifier[1](A_				)

							lowerCAmelCase   =     None if labels is None else self.hf_compute_loss(labels=A_     ,	logits=A_				)

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

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




'''simple docstring'''

from torch import nn






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







	"""simple docstring"""
	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f'Unsupported activation function: {act_fn}'							)