


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowerCAmelCase    :     str					=						"""scheduler_config.json"""
class 	UpperCamelCase__					( __UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    1
     __magic_name__    			=    2
     __magic_name__    			=    3
     __magic_name__    			=    4
     __magic_name__    			=    5
@dataclass
class 	UpperCamelCase__					( __UpperCAmelCase	):



     """simple docstring"""






     __magic_name__    			=    42
class 	UpperCamelCase__					:



     """simple docstring"""






     __magic_name__    			=    SCHEDULER_CONFIG_NAME
     __magic_name__    			=    ["dtype"]
     __magic_name__    			=    []
     __magic_name__    			=    True







     @classmethod
     def a	( cls      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       cls.load_config(
                pretrained_model_name_or_path=_lowerCamelCase      ,	subfolder=_lowerCamelCase      ,	return_unused_kwargs=_lowerCamelCase      ,	**_lowerCamelCase      ,	)
            _lowerCAmelCase			,     _lowerCAmelCase    :							Tuple   			=       cls.from_config(_lowerCamelCase      ,	return_unused_kwargs=_lowerCamelCase      ,	**_lowerCamelCase   )

            if hasattr(_lowerCamelCase      ,	'create_state'   ) and getattr(_lowerCamelCase      ,	'has_state'      ,	_lowerCamelCase   ):
                   _lowerCAmelCase    :							str   			=       scheduler.create_state()

            if return_unused_kwargs:
                   return scheduler, state, unused_kwargs

            return scheduler, state







     def a	( self      ,	snake_case__      ,	snake_case__ = False      ,	**snake_case__   ):

            '''simple docstring'''

            self.save_config(save_directory=_lowerCamelCase      ,	push_to_hub=_lowerCamelCase      ,	**_lowerCamelCase   )







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._get_compatibles()







     @classmethod
     def a	( cls   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       list(set([cls.__name__] + cls._compatibles   )   )
            _lowerCAmelCase    :							List[str]   			=       importlib.import_module(__name__.split('.'   )[0]   )
            _lowerCAmelCase    :							Dict   			=       [
                getattr(_lowerCamelCase      ,	_lowerCamelCase   ) for c in compatible_classes_str if hasattr(_lowerCamelCase      ,	_lowerCamelCase   )
            ]
            return compatible_classes






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       assert len(_lowerCAmelCase					) >= x.ndim
       return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowerCAmelCase					) - x.ndim)					)   ,						_lowerCAmelCase					)






def 			lowercase				(_A   ,						_A=0.999   ,						_A=jnp.floataa					):







       """simple docstring"""







       def alpha_bar(_A					):
              return math.cos((time_step + 0.008) / 1.008 * math.pi / 2					) ** 2

       _lowerCAmelCase    :							Dict   			=       []
       for i in range(_lowerCAmelCase					):
              _lowerCAmelCase    :							Any   			=       i / num_diffusion_timesteps
              _lowerCAmelCase    :							str   			=       (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar(_lowerCAmelCase					) / alpha_bar(_lowerCAmelCase					)   ,						_lowerCAmelCase					)					)
       return jnp.array(_lowerCAmelCase   ,						dtype=_lowerCAmelCase					)
@flax.struct.dataclass
class 	UpperCamelCase__					:



     """simple docstring"""






     __magic_name__    			=    42
     __magic_name__    			=    42
     __magic_name__    			=    42







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       scheduler.config

            if config.trained_betas is not None:
                   _lowerCAmelCase    :							Optional[int]   			=       jnp.asarray(config.trained_betas      ,	dtype=scheduler.dtype   )
            elif config.beta_schedule == "linear":
                   _lowerCAmelCase    :							List[str]   			=       jnp.linspace(config.beta_start      ,	config.beta_end      ,	config.num_train_timesteps      ,	dtype=scheduler.dtype   )
            elif config.beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   _lowerCAmelCase    :							Tuple   			=       (
                       jnp.linspace(
                           config.beta_start**0.5      ,	config.beta_end**0.5      ,	config.num_train_timesteps      ,	dtype=scheduler.dtype   )
                       ** 2
                   )
            elif config.beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   _lowerCAmelCase    :							List[Any]   			=       betas_for_alpha_bar(config.num_train_timesteps      ,	dtype=scheduler.dtype   )
            else:
                   raise NotImplementedError(
                       F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'   )

            _lowerCAmelCase    :							int   			=       1.0 - betas

            _lowerCAmelCase    :							Tuple   			=       jnp.cumprod(_lowerCamelCase      ,	axis=0   )

            return cls(
                alphas=_lowerCamelCase      ,	betas=_lowerCamelCase      ,	alphas_cumprod=_lowerCamelCase      ,	)






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Dict   			=       state.alphas_cumprod

       _lowerCAmelCase    :							Optional[int]   			=       alphas_cumprod[timesteps] ** 0.5
       _lowerCAmelCase    :							Union[str, Any]   			=       sqrt_alpha_prod.flatten()
       _lowerCAmelCase    :							Any   			=       broadcast_to_shape_from_left(_lowerCAmelCase   ,						original_samples.shape					)

       _lowerCAmelCase    :							List[Any]   			=       (1 - alphas_cumprod[timesteps]) ** 0.5
       _lowerCAmelCase    :							Any   			=       sqrt_one_minus_alpha_prod.flatten()
       _lowerCAmelCase    :							Union[str, Any]   			=       broadcast_to_shape_from_left(_lowerCAmelCase   ,						original_samples.shape					)

       return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[Any]   			=       get_sqrt_alpha_prod(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase					)
       _lowerCAmelCase    :							Dict   			=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase			,     _lowerCAmelCase    :							int   			=       get_sqrt_alpha_prod(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase					)
       _lowerCAmelCase    :							List[Any]   			=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
       return velocity





'''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    :     str					=						logging.get_logger(__name__)

# General docstring
lowerCAmelCase    :     Optional[Any]					=						"""RegNetConfig"""

# Base docstring
lowerCAmelCase    :     int					=						"""facebook/regnet-y-040"""
lowerCAmelCase    :     Optional[Any]					=						[1, 10_88, 7, 7]

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

lowerCAmelCase    :     Tuple					=						[
    """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      ,	snake_case__      ,	snake_case__ = 3      ,	snake_case__ = 1      ,	snake_case__ = 1      ,	snake_case__ = "relu"      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            # The padding and conv has been verified in
            # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
            _lowerCAmelCase    :							Optional[Any]   			=       tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2   )
            _lowerCAmelCase    :							List[Any]   			=       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'      ,	)
            _lowerCAmelCase    :							List[Any]   			=       tf.keras.layers.BatchNormalization(epsilon=1E-5      ,	momentum=0.9      ,	name='normalization'   )
            _lowerCAmelCase    :							int   			=       ACTaFN[activation] if activation is not None else tf.identity







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.convolution(self.padding(snake_case__   )   )
            _lowerCAmelCase    :							Union[str, Any]   			=       self.normalization(snake_case__   )
            _lowerCAmelCase    :							int   			=       self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							str   			=       config.num_channels
            _lowerCAmelCase    :							List[Any]   			=       TFRegNetConvLayer(
                out_channels=config.embedding_size      ,	kernel_size=3      ,	stride=2      ,	activation=config.hidden_act      ,	name='embedder'      ,	)







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[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)
            _lowerCAmelCase    :							List[Any]   			=       tf.transpose(snake_case__      ,	perm=(0, 2, 3, 1)   )
            _lowerCAmelCase    :							Tuple   			=       self.embedder(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__ = 2      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       tf.keras.layers.ConvaD(
                filters=snake_case__      ,	kernel_size=1      ,	strides=snake_case__      ,	use_bias=snake_case__      ,	name='convolution'   )
            _lowerCAmelCase    :							Tuple   			=       tf.keras.layers.BatchNormalization(epsilon=1E-5      ,	momentum=0.9      ,	name='normalization'   )







     def a	( self      ,	snake_case__      ,	snake_case__ = False   ):

            '''simple docstring'''

            return self.normalization(self.convolution(snake_case__   )      ,	training=snake_case__   )
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Any   			=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__      ,	name='pooler'   )
            _lowerCAmelCase    :							str   			=       [
                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 a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.pooler(snake_case__   )
            for layer_module in self.attention:
                   _lowerCAmelCase    :							Tuple   			=       layer_module(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_state * pooled
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 1      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Optional[int]   			=       in_channels != out_channels or stride != 1
            _lowerCAmelCase    :							Union[str, Any]   			=       max(1      ,	out_channels // config.groups_width   )
            _lowerCAmelCase    :							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.
            _lowerCAmelCase    :							Any   			=       [
                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'   ),
            ]
            _lowerCAmelCase    :							List[str]   			=       ACTaFN[config.hidden_act]







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       hidden_state
            for layer_module in self.layers:
                   _lowerCAmelCase    :							int   			=       layer_module(snake_case__   )
            _lowerCAmelCase    :							int   			=       self.shortcut(snake_case__   )
            hidden_state += residual
            _lowerCAmelCase    :							Tuple   			=       self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 1      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							List[str]   			=       in_channels != out_channels or stride != 1
            _lowerCAmelCase    :							Union[str, Any]   			=       max(1      ,	out_channels // config.groups_width   )
            _lowerCAmelCase    :							Optional[Any]   			=       (
                TFRegNetShortCut(snake_case__      ,	stride=snake_case__      ,	name='shortcut'   )
                if should_apply_shortcut
                else tf.keras.layers.Activation('linear'      ,	name='shortcut'   )
            )
            _lowerCAmelCase    :							Tuple   			=       [
                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'   ),
            ]
            _lowerCAmelCase    :							Tuple   			=       ACTaFN[config.hidden_act]







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       hidden_state
            for layer_module in self.layers:
                   _lowerCAmelCase    :							List[Any]   			=       layer_module(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       self.shortcut(snake_case__   )
            hidden_state += residual
            _lowerCAmelCase    :							str   			=       self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 2      ,	snake_case__ = 2      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )

            _lowerCAmelCase    :							Dict   			=       TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
            _lowerCAmelCase    :							Optional[int]   			=       [
                # 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 a	( self      ,	snake_case__   ):

            '''simple docstring'''

            for layer_module in self.layers:
                   _lowerCAmelCase    :							int   			=       layer_module(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							str   			=       []
            # 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'      ,	)   )
            _lowerCAmelCase    :							Union[str, Any]   			=       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 a	( self      ,	snake_case__      ,	snake_case__ = False      ,	snake_case__ = True   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       () if output_hidden_states else None

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

                   _lowerCAmelCase    :							List[str]   			=       stage_module(snake_case__   )

            if output_hidden_states:
                   _lowerCAmelCase    :							Dict   			=       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 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     __magic_name__    			=    RegNetConfig







     def __init__( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       config
            _lowerCAmelCase    :							Union[str, Any]   			=       TFRegNetEmbeddings(snake_case__      ,	name='embedder'   )
            _lowerCAmelCase    :							Optional[int]   			=       TFRegNetEncoder(snake_case__      ,	name='encoder'   )
            _lowerCAmelCase    :							Dict   			=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__      ,	name='pooler'   )







     @unpack_inputs
     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	):

            '''simple docstring'''

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

            _lowerCAmelCase    :							int   			=       self.embedder(snake_case__      ,	training=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       self.encoder(
                snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       encoder_outputs[0]
            _lowerCAmelCase    :							Tuple   			=       self.pooler(snake_case__   )

            # Change to NCHW output format have uniformity in the modules
            _lowerCAmelCase    :							Optional[int]   			=       tf.transpose(snake_case__      ,	perm=(0, 3, 1, 2)   )
            _lowerCAmelCase    :							Optional[Any]   			=       tf.transpose(snake_case__      ,	perm=(0, 3, 1, 2)   )

            # Change the other hidden state outputs to NCHW as well
            if output_hidden_states:
                   _lowerCAmelCase    :							Union[str, Any]   			=       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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    RegNetConfig
     __magic_name__    			=    "regnet"
     __magic_name__    			=    "pixel_values"







     @property
     def a	( self   ):

            '''simple docstring'''

            return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,	dtype=tf.floataa   )}
lowerCAmelCase    :     List[Any]					=						r"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase    :     Dict					=						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."    ,					SCREAMING_SNAKE_CASE_    ,					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							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 a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	):

            '''simple docstring'''

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

            _lowerCAmelCase    :							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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       config.num_labels
            _lowerCAmelCase    :							Optional[Any]   			=       TFRegNetMainLayer(snake_case__      ,	name='regnet'   )
            # classification head
            _lowerCAmelCase    :							Optional[int]   			=       [
                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 a	( self      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            _lowerCAmelCase    :							List[str]   			=       return_dict if return_dict is not None else self.config.use_return_dict

            _lowerCAmelCase    :							Dict   			=       self.regnet(
                snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__   )

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

            _lowerCAmelCase    :							List[Any]   			=       self.classifier[0](snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       self.classifier[1](snake_case__   )

            _lowerCAmelCase    :							int   			=       None if labels is None else self.hf_compute_loss(labels=snake_case__      ,	logits=snake_case__   )

            if not return_dict:
                   _lowerCAmelCase    :							str   			=       (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   )

