





'''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      UpperCamelCase__					(					tf.keras.layers.Layer			):




					"""simple docstring"""


					def __init__(						self				:			List[Any]      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int = 3      ,						lowerCamelCase_				:			int = 1      ,						lowerCamelCase_				:			int = 1      ,						lowerCamelCase_				:			Optional[str] = "relu"      ,						**lowerCamelCase_				:			List[Any]      ,						):

							'''simple docstring'''



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



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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.convolution(self.padding(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				List[str]		=			self.normalization(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.activation(lowerCamelCase_						)
							return hidden_state







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




					"""simple docstring"""


					def __init__(						self				:			List[Any]      ,						lowerCamelCase_				:			RegNetConfig      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			config.num_channels
							SCREAMING_SNAKE_CASE :				int		=			TFRegNetConvLayer(
							    out_channels=config.embedding_size      ,						kernel_size=3      ,						stride=2      ,						activation=config.hidden_act      ,						name="""embedder"""      ,						)



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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			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 :				Any		=			tf.transpose(lowerCamelCase_      ,						perm=(0, 2, 3, 1)						)
							SCREAMING_SNAKE_CASE :				str		=			self.embedder(lowerCamelCase_						)
							return hidden_state







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




					"""simple docstring"""


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

							'''simple docstring'''



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



					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			tf.Tensor      ,						lowerCamelCase_				:			bool = False						):

							'''simple docstring'''



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







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




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase_      ,						name="""pooler"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			[
							    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 lowerCamelCase_			(						self				:			List[Any]      ,						lowerCamelCase_				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.pooler(lowerCamelCase_						)
							for layer_module in self.attention:
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			layer_module(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			hidden_state * pooled
							return hidden_state







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




					"""simple docstring"""


					def __init__(						self				:			int      ,						lowerCamelCase_				:			RegNetConfig      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int = 1      ,						**lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			in_channels != out_channels or stride != 1
							SCREAMING_SNAKE_CASE :				Any		=			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"""						)
							)
							# `self.layers` instead of `self.layer` because that is a reserved argument.
							SCREAMING_SNAKE_CASE :				int		=			[
							    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 :				Union[str, Any]		=			ACTaFN[config.hidden_act]



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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			hidden_state
							for layer_module in self.layers:
									SCREAMING_SNAKE_CASE :				List[str]		=			layer_module(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.shortcut(lowerCamelCase_						)
							hidden_state += residual
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.activation(lowerCamelCase_						)
							return hidden_state







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




					"""simple docstring"""


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			RegNetConfig      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int      ,						lowerCamelCase_				:			int = 1      ,						**lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			in_channels != out_channels or stride != 1
							SCREAMING_SNAKE_CASE :				List[Any]		=			max(1      ,						out_channels // config.groups_width						)
							SCREAMING_SNAKE_CASE :				Dict		=			(
							    TFRegNetShortCut(lowerCamelCase_      ,						stride=lowerCamelCase_      ,						name="""shortcut"""						)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation("""linear"""      ,						name="""shortcut"""						)
							)
							SCREAMING_SNAKE_CASE :				Tuple		=			[
							    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 :				Tuple		=			ACTaFN[config.hidden_act]



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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			hidden_state
							for layer_module in self.layers:
									SCREAMING_SNAKE_CASE :				List[Any]		=			layer_module(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			self.shortcut(lowerCamelCase_						)
							hidden_state += residual
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.activation(lowerCamelCase_						)
							return hidden_state







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




					"""simple docstring"""


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

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
							SCREAMING_SNAKE_CASE :				int		=			[
							    # 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 lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							for layer_module in self.layers:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			layer_module(lowerCamelCase_						)
							return hidden_state







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




					"""simple docstring"""


					def __init__(						self				:			int      ,						lowerCamelCase_				:			RegNetConfig      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
							# 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 lowerCamelCase_			(						self				:			Optional[Any]      ,						lowerCamelCase_				:			tf.Tensor      ,						lowerCamelCase_				:			bool = False      ,						lowerCamelCase_				:			bool = True						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			() if output_hidden_states else None

							for stage_module in self.stages:
									if output_hidden_states:
											SCREAMING_SNAKE_CASE :				List[str]		=			hidden_states + (hidden_state,)

									SCREAMING_SNAKE_CASE :				Optional[int]		=			stage_module(lowerCamelCase_						)

							if output_hidden_states:
									SCREAMING_SNAKE_CASE :				List[str]		=			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      UpperCamelCase__					(					tf.keras.layers.Layer			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	RegNetConfig


					def __init__(						self				:			str      ,						lowerCamelCase_				:			int      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			config
							SCREAMING_SNAKE_CASE :				Any		=			TFRegNetEmbeddings(lowerCamelCase_      ,						name="""embedder"""						)
							SCREAMING_SNAKE_CASE :				str		=			TFRegNetEncoder(lowerCamelCase_      ,						name="""encoder"""						)
							SCREAMING_SNAKE_CASE :				Any		=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase_      ,						name="""pooler"""						)



					@unpack_inputs
					def lowerCamelCase_			(						self				:			List[Any]      ,						lowerCamelCase_				:			tf.Tensor      ,						lowerCamelCase_				:			Optional[bool] = None      ,						lowerCamelCase_				:			Optional[bool] = None      ,						lowerCamelCase_				:			bool = False      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			(
							    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.embedder(lowerCamelCase_      ,						training=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			self.encoder(
							    lowerCamelCase_      ,						output_hidden_states=lowerCamelCase_      ,						return_dict=lowerCamelCase_      ,						training=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			encoder_outputs[0]
							SCREAMING_SNAKE_CASE :				int		=			self.pooler(lowerCamelCase_						)

							# Change to NCHW output format have uniformity in the modules
							SCREAMING_SNAKE_CASE :				Tuple		=			tf.transpose(lowerCamelCase_      ,						perm=(0, 3, 1, 2)						)
							SCREAMING_SNAKE_CASE :				str		=			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 :				Tuple		=			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      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	RegNetConfig
					SCREAMING_SNAKE_CASE__				=	'''regnet'''
					SCREAMING_SNAKE_CASE__				=	'''pixel_values'''


					@property
					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)      ,						dtype=tf.floataa						)}







__UpperCAmelCase            =       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.
"""

__UpperCAmelCase            =       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.'''						,					lowercase_						,					)
class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			RegNetConfig      ,						*lowerCamelCase_				:			Union[str, Any]      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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 lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			tf.Tensor      ,						lowerCamelCase_				:			Optional[bool] = None      ,						lowerCamelCase_				:			Optional[bool] = None      ,						lowerCamelCase_				:			List[Any]=False      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			return_dict if return_dict is not None else self.config.use_return_dict

							SCREAMING_SNAKE_CASE :				str		=			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.
    '''						,					lowercase_						,					)
class      UpperCamelCase__					(					lowercase_						,					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			RegNetConfig      ,						*lowerCamelCase_				:			Union[str, Any]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			config.num_labels
							SCREAMING_SNAKE_CASE :				List[str]		=			TFRegNetMainLayer(lowerCamelCase_      ,						name="""regnet"""						)
							# classification head
							SCREAMING_SNAKE_CASE :				Tuple		=			[
							    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 lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			tf.Tensor = None      ,						lowerCamelCase_				:			tf.Tensor = None      ,						lowerCamelCase_				:			bool = None      ,						lowerCamelCase_				:			bool = None      ,						lowerCamelCase_				:			Optional[Any]=False      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			(
							    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 :				Optional[Any]		=			self.regnet(
							    lowerCamelCase_      ,						output_hidden_states=lowerCamelCase_      ,						return_dict=lowerCamelCase_      ,						training=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			outputs.pooler_output if return_dict else outputs[1]

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.classifier[0](lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.classifier[1](lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			None if labels is None else self.hf_compute_loss(labels=lowerCamelCase_      ,						logits=lowerCamelCase_						)

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

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










'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

							__UpperCAmelCase            =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



