



"""simple docstring"""





from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )     ):


						'''simple docstring'''






						snake_case_ :			Dict            =      tau * frequency / samplerate
						snake_case_ :			List[Any]            =      sin(__UpperCamelCase     )
						snake_case_ :			List[str]            =      cos(__UpperCamelCase     )
						snake_case_ :			List[Any]            =      _sin / (2 * q_factor)

						snake_case_ :			List[Any]            =      (1 - _cos) / 2
						snake_case_ :			Any            =      1 - _cos

						snake_case_ :			Union[str, Any]            =      1 + alpha
						snake_case_ :			Tuple            =      -2 * _cos
						snake_case_ :			str            =      1 - alpha

						snake_case_ :			int            =      IIRFilter(2     )
						filt.set_coefficients([aa, aa, aa]       ,			[ba, ba, ba]     )
						return filt
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )     ):


						'''simple docstring'''






						snake_case_ :			Optional[Any]            =      tau * frequency / samplerate
						snake_case_ :			Dict            =      sin(__UpperCamelCase     )
						snake_case_ :			Union[str, Any]            =      cos(__UpperCamelCase     )
						snake_case_ :			Optional[int]            =      _sin / (2 * q_factor)

						snake_case_ :			List[Any]            =      (1 + _cos) / 2
						snake_case_ :			Union[str, Any]            =      -1 - _cos

						snake_case_ :			Any            =      1 + alpha
						snake_case_ :			Any            =      -2 * _cos
						snake_case_ :			List[Any]            =      1 - alpha

						snake_case_ :			int            =      IIRFilter(2     )
						filt.set_coefficients([aa, aa, aa]       ,			[ba, ba, ba]     )
						return filt
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )     ):


						'''simple docstring'''






						snake_case_ :			Union[str, Any]            =      tau * frequency / samplerate
						snake_case_ :			Optional[int]            =      sin(__UpperCamelCase     )
						snake_case_ :			Dict            =      cos(__UpperCamelCase     )
						snake_case_ :			List[Any]            =      _sin / (2 * q_factor)

						snake_case_ :			str            =      _sin / 2
						snake_case_ :			Any            =      0
						snake_case_ :			List[Any]            =      -ba

						snake_case_ :			List[Any]            =      1 + alpha
						snake_case_ :			List[str]            =      -2 * _cos
						snake_case_ :			Optional[int]            =      1 - alpha

						snake_case_ :			List[str]            =      IIRFilter(2     )
						filt.set_coefficients([aa, aa, aa]       ,			[ba, ba, ba]     )
						return filt
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )     ):


						'''simple docstring'''






						snake_case_ :			Tuple            =      tau * frequency / samplerate
						snake_case_ :			Union[str, Any]            =      sin(__UpperCamelCase     )
						snake_case_ :			Union[str, Any]            =      cos(__UpperCamelCase     )
						snake_case_ :			List[str]            =      _sin / (2 * q_factor)

						snake_case_ :			str            =      1 - alpha
						snake_case_ :			Union[str, Any]            =      -2 * _cos
						snake_case_ :			int            =      1 + alpha

						snake_case_ :			Tuple            =      IIRFilter(2     )
						filt.set_coefficients([ba, ba, ba]       ,			[ba, ba, ba]     )
						return filt
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )       ,			):


						'''simple docstring'''






						snake_case_ :			str            =      tau * frequency / samplerate
						snake_case_ :			str            =      sin(__UpperCamelCase     )
						snake_case_ :			Union[str, Any]            =      cos(__UpperCamelCase     )
						snake_case_ :			List[str]            =      _sin / (2 * q_factor)
						snake_case_ :			Tuple            =      1_0 ** (gain_db / 4_0)

						snake_case_ :			List[Any]            =      1 + alpha * big_a
						snake_case_ :			Optional[int]            =      -2 * _cos
						snake_case_ :			Dict            =      1 - alpha * big_a
						snake_case_ :			List[Any]            =      1 + alpha / big_a
						snake_case_ :			Union[str, Any]            =      -2 * _cos
						snake_case_ :			Any            =      1 - alpha / big_a

						snake_case_ :			Dict            =      IIRFilter(2     )
						filt.set_coefficients([aa, aa, aa]       ,			[ba, ba, ba]     )
						return filt
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )       ,			):


						'''simple docstring'''






						snake_case_ :			Optional[int]            =      tau * frequency / samplerate
						snake_case_ :			Optional[int]            =      sin(__UpperCamelCase     )
						snake_case_ :			List[str]            =      cos(__UpperCamelCase     )
						snake_case_ :			Optional[Any]            =      _sin / (2 * q_factor)
						snake_case_ :			Tuple            =      1_0 ** (gain_db / 4_0)
						snake_case_ :			Tuple            =      (big_a + 1) - (big_a - 1) * _cos
						snake_case_ :			Any            =      (big_a + 1) + (big_a - 1) * _cos
						snake_case_ :			List[str]            =      (big_a - 1) - (big_a + 1) * _cos
						snake_case_ :			List[Any]            =      (big_a - 1) + (big_a + 1) * _cos
						snake_case_ :			Optional[Any]            =      2 * sqrt(__UpperCamelCase     ) * alpha

						snake_case_ :			Optional[int]            =      big_a * (pmc + aaa)
						snake_case_ :			int            =      2 * big_a * mpc
						snake_case_ :			List[Any]            =      big_a * (pmc - aaa)
						snake_case_ :			Optional[Any]            =      ppmc + aaa
						snake_case_ :			List[Any]            =      -2 * pmpc
						snake_case_ :			Optional[Any]            =      ppmc - aaa

						snake_case_ :			Dict            =      IIRFilter(2     )
						filt.set_coefficients([aa, aa, aa]       ,			[ba, ba, ba]     )
						return filt






def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      int       ,			__UpperCamelCase   :      float       ,			__UpperCamelCase   :      float = 1 / sqrt(2     )       ,			):


						'''simple docstring'''






						snake_case_ :			List[str]            =      tau * frequency / samplerate
						snake_case_ :			List[str]            =      sin(__UpperCamelCase     )
						snake_case_ :			List[str]            =      cos(__UpperCamelCase     )
						snake_case_ :			Any            =      _sin / (2 * q_factor)
						snake_case_ :			Optional[int]            =      1_0 ** (gain_db / 4_0)
						snake_case_ :			Union[str, Any]            =      (big_a + 1) - (big_a - 1) * _cos
						snake_case_ :			Dict            =      (big_a + 1) + (big_a - 1) * _cos
						snake_case_ :			int            =      (big_a - 1) - (big_a + 1) * _cos
						snake_case_ :			Tuple            =      (big_a - 1) + (big_a + 1) * _cos
						snake_case_ :			str            =      2 * sqrt(__UpperCamelCase     ) * alpha

						snake_case_ :			str            =      big_a * (ppmc + aaa)
						snake_case_ :			List[Any]            =      -2 * big_a * pmpc
						snake_case_ :			Union[str, Any]            =      big_a * (ppmc - aaa)
						snake_case_ :			Any            =      pmc + aaa
						snake_case_ :			Dict            =      2 * mpc
						snake_case_ :			List[Any]            =      pmc - aaa

						snake_case_ :			int            =      IIRFilter(2     )
						filt.set_coefficients([aa, aa, aa]       ,			[ba, ba, ba]     )
						return filt









"""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


__lowerCamelCase							=  logging.get_logger(__name__)

# General docstring
__lowerCamelCase							=  'RegNetConfig'

# Base docstring
__lowerCamelCase							=  'facebook/regnet-y-040'
__lowerCamelCase							=  [1, 10_88, 7, 7]

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

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



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

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






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      str					)			->							Dict:
											__magic_name__:	Optional[Any]							=       self.convolution(self.padding(__snake_case					)					)
											__magic_name__:	Union[str, Any]							=       self.normalization(__snake_case					)
											__magic_name__:	Tuple							=       self.activation(__snake_case					)
											return hidden_state



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

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      RegNetConfig     ,		**__snake_case  :      Dict					)			->							Optional[int]:
											super().__init__(**__snake_case					)
											__magic_name__:	Tuple							=       config.num_channels
											__magic_name__:	Optional[int]							=       TFRegNetConvLayer(
											    out_channels=config.embedding_size     ,		kernel_size=3     ,		stride=2     ,		activation=config.hidden_act     ,		name="""embedder"""     ,		)






					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Dict					)			->							int:
											__magic_name__:	Union[str, Any]							=       shape_list(__snake_case					)[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)
											__magic_name__:	Any							=       tf.transpose(__snake_case     ,		perm=(0, 2, 3, 1)					)
											__magic_name__:	Dict							=       self.embedder(__snake_case					)
											return hidden_state



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

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






					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      bool = False					)			->							tf.Tensor:
											return self.normalization(self.convolution(__snake_case					)     ,		training=__snake_case					)



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

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






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



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

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      RegNetConfig     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int = 1     ,		**__snake_case  :      Optional[int]					)			->							Optional[int]:
											super().__init__(**__snake_case					)
											__magic_name__:	List[str]							=       in_channels != out_channels or stride != 1
											__magic_name__:	Union[str, Any]							=       max(1     ,		out_channels // config.groups_width					)
											__magic_name__:	Optional[Any]							=       (
											    TFRegNetShortCut(__snake_case     ,		stride=__snake_case     ,		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.
											__magic_name__:	List[str]							=       [
											    TFRegNetConvLayer(__snake_case     ,		kernel_size=1     ,		activation=config.hidden_act     ,		name="""layer.0"""					),
											    TFRegNetConvLayer(
											        __snake_case     ,		stride=__snake_case     ,		groups=__snake_case     ,		activation=config.hidden_act     ,		name="""layer.1"""					),
											    TFRegNetConvLayer(__snake_case     ,		kernel_size=1     ,		activation=__snake_case     ,		name="""layer.2"""					),
											]
											__magic_name__:	Any							=       ACTaFN[config.hidden_act]






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Any					)			->							Union[str, Any]:
											__magic_name__:	Any							=       hidden_state
											for layer_module in self.layers:
																	__magic_name__:	Optional[int]							=       layer_module(__snake_case					)
											__magic_name__:	str							=       self.shortcut(__snake_case					)
											hidden_state += residual
											__magic_name__:	int							=       self.activation(__snake_case					)
											return hidden_state



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

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






					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      int					)			->							Dict:
											__magic_name__:	int							=       hidden_state
											for layer_module in self.layers:
																	__magic_name__:	Optional[Any]							=       layer_module(__snake_case					)
											__magic_name__:	Union[str, Any]							=       self.shortcut(__snake_case					)
											hidden_state += residual
											__magic_name__:	Any							=       self.activation(__snake_case					)
											return hidden_state



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

					def __init__(			self  :      int     ,		__snake_case  :      RegNetConfig     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      int = 2     ,		__snake_case  :      int = 2     ,		**__snake_case  :      List[Any]					)			->							Optional[int]:
											super().__init__(**__snake_case					)

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






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Union[str, Any]					)			->							Tuple:
											for layer_module in self.layers:
																	__magic_name__:	Dict							=       layer_module(__snake_case					)
											return hidden_state



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

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      RegNetConfig     ,		**__snake_case  :      Optional[Any]					)			->							Dict:
											super().__init__(**__snake_case					)
											__magic_name__:	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(
											        __snake_case     ,		config.embedding_size     ,		config.hidden_sizes[0]     ,		stride=2 if config.downsample_in_first_stage else 1     ,		depth=config.depths[0]     ,		name="""stages.0"""     ,		)					)
											__magic_name__:	Dict							=       zip(config.hidden_sizes     ,		config.hidden_sizes[1:]					)
											for i, ((in_channels, out_channels), depth) in enumerate(zip(__snake_case     ,		config.depths[1:]					)					):
																	self.stages.append(TFRegNetStage(__snake_case     ,		__snake_case     ,		__snake_case     ,		depth=__snake_case     ,		name=F'stages.{i+1}'					)					)






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = True					)			->							TFBaseModelOutputWithNoAttention:
											__magic_name__:	int							=       () if output_hidden_states else None

											for stage_module in self.stages:
																	if output_hidden_states:
																							__magic_name__:	Optional[Any]							=       hidden_states + (hidden_state,)

																	__magic_name__:	Optional[Any]							=       stage_module(__snake_case					)

											if output_hidden_states:
																	__magic_name__:	int							=       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=__snake_case     ,		hidden_states=__snake_case					)



@keras_serializable
class       __A		(   tf.keras.layers.Layer			):
					UpperCAmelCase__								=				RegNetConfig

					def __init__(			self  :      Optional[int]     ,		__snake_case  :      Any     ,		**__snake_case  :      List[str]					)			->							int:
											super().__init__(**__snake_case					)
											__magic_name__:	Union[str, Any]							=       config
											__magic_name__:	Optional[int]							=       TFRegNetEmbeddings(__snake_case     ,		name="""embedder"""					)
											__magic_name__:	int							=       TFRegNetEncoder(__snake_case     ,		name="""encoder"""					)
											__magic_name__:	int							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case     ,		name="""pooler"""					)






					@unpack_inputs
					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      tf.Tensor     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      bool = False     ,		)			->							TFBaseModelOutputWithPoolingAndNoAttention:
											__magic_name__:	Optional[int]							=       (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__magic_name__:	int							=       return_dict if return_dict is not None else self.config.use_return_dict

											__magic_name__:	List[str]							=       self.embedder(__snake_case     ,		training=__snake_case					)

											__magic_name__:	Optional[Any]							=       self.encoder(
											    __snake_case     ,		output_hidden_states=__snake_case     ,		return_dict=__snake_case     ,		training=__snake_case					)

											__magic_name__:	str							=       encoder_outputs[0]
											__magic_name__:	List[Any]							=       self.pooler(__snake_case					)

											# Change to NCHW output format have uniformity in the modules
											__magic_name__:	int							=       tf.transpose(__snake_case     ,		perm=(0, 3, 1, 2)					)
											__magic_name__:	List[str]							=       tf.transpose(__snake_case     ,		perm=(0, 3, 1, 2)					)

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



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				RegNetConfig
					UpperCAmelCase__								=				"regnet"
					UpperCAmelCase__								=				"pixel_values"

					@property
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Union[str, Any]:
											return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)     ,		dtype=tf.floataa					)}


__lowerCamelCase							=  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'

__lowerCamelCase							=  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."     ,SCREAMING_SNAKE_CASE_     ,)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      RegNetConfig     ,		*__snake_case  :      List[Any]     ,		**__snake_case  :      Tuple					)			->							Tuple:
											super().__init__(__snake_case     ,		*__snake_case     ,		**__snake_case					)
											__magic_name__:	List[str]							=       TFRegNetMainLayer(__snake_case     ,		name="""regnet"""					)






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

											__magic_name__:	List[str]							=       self.regnet(
											    pixel_values=__snake_case     ,		output_hidden_states=__snake_case     ,		return_dict=__snake_case     ,		training=__snake_case     ,		)
											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    "     ,SCREAMING_SNAKE_CASE_     ,)
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_			):

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

											__magic_name__:	Any							=       self.regnet(
											    __snake_case     ,		output_hidden_states=__snake_case     ,		return_dict=__snake_case     ,		training=__snake_case					)

											__magic_name__:	Optional[Any]							=       outputs.pooler_output if return_dict else outputs[1]

											__magic_name__:	Optional[int]							=       self.classifier[0](__snake_case					)
											__magic_name__:	List[Any]							=       self.classifier[1](__snake_case					)

											__magic_name__:	Optional[int]							=       None if labels is None else self.hf_compute_loss(labels=__snake_case     ,		logits=__snake_case					)

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

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



