






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						fname.split(os.path.sep   )[-1]
    return re.search(r'^(.*)_\d+\.jpg$',   _lowercase   ).groups()[0]

class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Optional[int] ,							snake_case__:						List[str] ,							snake_case__:						Optional[int]=None ,							snake_case__:						int=None						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						file_names
       _lowercase     			=						image_transform
       _lowercase     			=						label_to_id

   def __len__(     self:						Any						)	->		Optional[Any]:


       """simple docstring"""



       return len(self.file_names						)

   def __getitem__(     self:						Union[str, Any] ,							snake_case__:						Optional[int]						)	->		List[Any]:


       """simple docstring"""



       _lowercase     			=						self.file_names[idx]
       _lowercase     			=						PIL.Image.open(snake_case__						)
       _lowercase     			=						raw_image.convert('RGB'						)
       if self.image_transform is not None:
           _lowercase     			=						self.image_transform(snake_case__						)
       _lowercase     			=						extract_label(snake_case__						)
       if self.label_to_id is not None:
           _lowercase     			=						self.label_to_id[label]
       return {"image": image, "label": label}






def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						Accelerator(cpu=args.cpu,   mixed_precision=args.mixed_precision   )

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase     			=						config['lr']
    _lowercase     			=						int(config['num_epochs']   )
    _lowercase     			=						int(config['seed']   )
    _lowercase     			=						int(config['batch_size']   )
    _lowercase     			=						config['image_size']
    if not isinstance(_lowercase,   (list, tuple)   ):
        _lowercase     			=						(image_size, image_size)

    # Grab all the image filenames
    _lowercase     			=						[os.path.join(args.data_dir,   _lowercase   ) for fname in os.listdir(args.data_dir   ) if fname.endswith('.jpg'   )]

    # Build the label correspondences
    _lowercase     			=						[extract_label(_lowercase   ) for fname in file_names]
    _lowercase     			=						list(set(_lowercase   )   )
    id_to_label.sort()
    _lowercase     			=						{lbl: i for i, lbl in enumerate(_lowercase   )}

    # Set the seed before splitting the data.
    np.random.seed(_lowercase   )
    torch.manual_seed(_lowercase   )
    torch.cuda.manual_seed_all(_lowercase   )

    # Split our filenames between train and validation
    _lowercase     			=						np.random.permutation(len(_lowercase   )   )
    _lowercase     			=						int(0.8 * len(_lowercase   )   )
    _lowercase     			=						random_perm[:cut]
    _lowercase     			=						random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    _lowercase     			=						Compose([RandomResizedCrop(_lowercase,   scale=(0.5, 1.0)   ), ToTensor()]   )
    _lowercase     			=						PetsDataset(
        [file_names[i] for i in train_split],   image_transform=_lowercase,   label_to_id=_lowercase   )

    # For evaluation, we use a deterministic Resize
    _lowercase     			=						Compose([Resize(_lowercase   ), ToTensor()]   )
    _lowercase     			=						PetsDataset([file_names[i] for i in eval_split],   image_transform=_lowercase,   label_to_id=_lowercase   )

    # Instantiate dataloaders.
    _lowercase     			=						DataLoader(_lowercase,   shuffle=_lowercase,   batch_size=_lowercase,   num_workers=4   )
    _lowercase     			=						DataLoader(_lowercase,   shuffle=_lowercase,   batch_size=_lowercase,   num_workers=4   )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase     			=						create_model('resnet50d',   pretrained=_lowercase,   num_classes=len(_lowercase   )   )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    _lowercase     			=						model.to(accelerator.device   )

    # Freezing the base model
    for param in model.parameters():
        _lowercase     			=						False
    for param in model.get_classifier().parameters():
        _lowercase     			=						True

    # We normalize the batches of images to be a bit faster.
    _lowercase     			=						torch.tensor(model.default_cfg['mean']   )[None, :, None, None].to(accelerator.device   )
    _lowercase     			=						torch.tensor(model.default_cfg['std']   )[None, :, None, None].to(accelerator.device   )

    # Instantiate optimizer
    _lowercase     			=						torch.optim.Adam(params=model.parameters(),   lr=lr / 25   )

    # Instantiate learning rate scheduler
    _lowercase     			=						OneCycleLR(optimizer=_lowercase,   max_lr=_lowercase,   epochs=_lowercase,   steps_per_epoch=len(_lowercase   )   )

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _lowercase			,     _lowercase			,     _lowercase			,     _lowercase			,     _lowercase     			=						accelerator.prepare(
        _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase   )

    # Now we train the model
    for epoch in range(_lowercase   ):
        model.train()
        for step, batch in enumerate(_lowercase   ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase     			=						{k: v.to(accelerator.device   ) for k, v in batch.items()}
            _lowercase     			=						(batch['image'] - mean) / std
            _lowercase     			=						model(_lowercase   )
            _lowercase     			=						torch.nn.functional.cross_entropy(_lowercase,   batch['label']   )
            accelerator.backward(_lowercase   )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        _lowercase     			=						0
        _lowercase     			=						0
        for _, batch in enumerate(_lowercase   ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _lowercase     			=						{k: v.to(accelerator.device   ) for k, v in batch.items()}
            _lowercase     			=						(batch['image'] - mean) / std
            with torch.no_grad():
                _lowercase     			=						model(_lowercase   )
            _lowercase     			=						outputs.argmax(dim=-1   )
            _lowercase			,     _lowercase     			=						accelerator.gather_for_metrics((predictions, batch['label'])   )
            _lowercase     			=						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        _lowercase     			=						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						argparse.ArgumentParser(description='Simple example of training script.'   )
    parser.add_argument('--data_dir',   required=_lowercase,   help='The data folder on disk.'   )
    parser.add_argument(
        '--mixed_precision',   type=_lowercase,   default=_lowercase,   choices=['no', 'fp16', 'bf16', 'fp8'],   help='Whether to use mixed precision. Choose'
        'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
        'and an Nvidia Ampere GPU.',   )
    parser.add_argument(
        '--checkpointing_steps',   type=_lowercase,   default=_lowercase,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',   )
    parser.add_argument('--cpu',   action='store_true',   help='If passed, will train on the CPU.'   )
    _lowercase     			=						parser.parse_args()
    _lowercase     			=						{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
    training_function(_lowercase,   _lowercase   )


if __name__ == "__main__":
   main()








from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


lowerCamelCase					    =	logging.get_logger(__name__)

# General docstring
lowerCamelCase					    =	'''ResNetConfig'''

# Base docstring
lowerCamelCase					    =	'''microsoft/resnet-50'''
lowerCamelCase					    =	[1, 2048, 7, 7]

# Image classification docstring
lowerCamelCase					    =	'''microsoft/resnet-50'''
lowerCamelCase					    =	'''tiger cat'''

lowerCamelCase					    =	[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

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







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int = 3 ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						int						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						kernel_size // 2
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=snake_case__ ,							strides=snake_case__ ,							padding='valid' ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)
       _lowercase     			=						ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						_lowercase     			=						(self.pad_value, self.pad_value)
       _lowercase     			=						tf.pad(snake_case__ ,							[(0, 0), height_pad, width_pad, (0, 0)]						)
       _lowercase     			=						self.conv(snake_case__						)
       return hidden_state

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						TFResNetConvLayer(
           config.embedding_size ,							kernel_size=7 ,							stride=2 ,							activation=config.hidden_act ,							name='embedder' ,							)
       _lowercase     			=						tf.keras.layers.MaxPoolaD(pool_size=3 ,							strides=2 ,							padding='valid' ,							name='pooler'						)
       _lowercase     			=						config.num_channels

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase			,     _lowercase			,     _lowercase			,     _lowercase     			=						shape_list(snake_case__						)
       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.'						)
       _lowercase     			=						pixel_values
       _lowercase     			=						self.embedder(snake_case__						)
       _lowercase     			=						tf.pad(snake_case__ ,							[[0, 0], [1, 1], [1, 1], [0, 0]]						)
       _lowercase     			=						self.pooler(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						List[str] ,							snake_case__:						int ,							snake_case__:						int = 2 ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=1 ,							strides=snake_case__ ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						x
       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							activation=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							snake_case__:						int = 4 ,							**snake_case__:						Optional[Any] ,							)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						out_channels // reduction
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							activation=snake_case__ ,							name='layer.2'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						ResNetConfig ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 2 ,							snake_case__:						int = 2 ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)

       _lowercase     			=						TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

       _lowercase     			=						[layer(snake_case__ ,							snake_case__ ,							stride=snake_case__ ,							activation=config.hidden_act ,							name='layers.0'						)]
       layers += [
           layer(snake_case__ ,							snake_case__ ,							activation=config.hidden_act ,							name=f'''layers.{i + 1}'''						)
           for i in range(depth - 1						)
       ]
       _lowercase     			=						layers

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       for layer in self.stage_layers:
           _lowercase     			=						layer(snake_case__ ,							training=snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Optional[Any] ,							snake_case__:						ResNetConfig ,							**snake_case__:						Union[str, Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       _lowercase     			=						[
           TFResNetStage(
               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' ,							)
       ]
       for i, (in_channels, out_channels, depth) in enumerate(
           zip(config.hidden_sizes ,							config.hidden_sizes[1:] ,							config.depths[1:]						)						):
           self.stages.append(TFResNetStage(snake_case__ ,							snake_case__ ,							snake_case__ ,							depth=snake_case__ ,							name=f'''stages.{i + 1}'''						)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False ,							snake_case__:						bool = True ,							snake_case__:						bool = False ,							)	->		TFBaseModelOutputWithNoAttention:


       """simple docstring"""



       _lowercase     			=						() if output_hidden_states else None

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

           _lowercase     			=						stage_module(snake_case__ ,							training=snake_case__						)

       if output_hidden_states:
           _lowercase     			=						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__						)






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig
   __lowerCAmelCase  =    """resnet"""
   __lowerCAmelCase  =    """pixel_values"""

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Dict:


       """simple docstring"""



       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) ,							dtype=tf.floataa						)}






lowerCamelCase					    =	r'''
    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.

    Parameters:
        config ([`ResNetConfig`]): 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					    =	r'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__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.
'''

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







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig

   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Dict						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						config
       _lowercase     			=						TFResNetEmbeddings(snake_case__ ,							name='embedder'						)
       _lowercase     			=						TFResNetEncoder(snake_case__ ,							name='encoder'						)
       _lowercase     			=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__						)

   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



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

       # TF 2.0 image layers can't use NCHW format when running on CPU.
       # We transpose to NHWC format and then transpose back after the full forward pass.
       # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
       _lowercase     			=						tf.transpose(snake_case__ ,							perm=[0, 2, 3, 1]						)
       _lowercase     			=						self.embedder(snake_case__ ,							training=snake_case__						)

       _lowercase     			=						self.encoder(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

       _lowercase     			=						encoder_outputs[0]

       _lowercase     			=						self.pooler(snake_case__						)

       # Transpose all the outputs to the NCHW format
       # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
       _lowercase     			=						tf.transpose(snake_case__ ,							(0, 3, 1, 2)						)
       _lowercase     			=						tf.transpose(snake_case__ ,							(0, 3, 1, 2)						)
       _lowercase     			=						()
       for hidden_state in encoder_outputs[1:]:
           _lowercase     			=						hidden_states + tuple(tf.transpose(snake_case__ ,							(0, 3, 1, 2)						) for h in hidden_state						)

       if not return_dict:
           return (last_hidden_state, pooled_output) + hidden_states

       _lowercase     			=						hidden_states if output_hidden_states else None

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=snake_case__ ,							pooler_output=snake_case__ ,							hidden_states=snake_case__ ,							)






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						TFResNetMainLayer(config=snake_case__ ,							name='resnet'						)

   @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 ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



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

       _lowercase     			=						self.resnet(
           pixel_values=snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__ ,							)
       return resnet_outputs






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ,      lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Any						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						config.num_labels
       _lowercase     			=						TFResNetMainLayer(snake_case__ ,							name='resnet'						)
       # classification head
       _lowercase     			=						(
           tf.keras.layers.Dense(config.num_labels ,							name='classifier.1'						)
           if config.num_labels > 0
           else tf.keras.layers.Activation('linear' ,							name='classifier.1'						)
       )

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						tf.keras.layers.Flatten()(snake_case__						)
       _lowercase     			=						self.classifier_layer(snake_case__						)
       return logits

   @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 ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor = None ,							snake_case__:						tf.Tensor = None ,							snake_case__:						bool = None ,							snake_case__:						bool = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:


       """simple docstring"""



       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

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

       _lowercase     			=						self.classifier(snake_case__						)

       _lowercase     			=						None if labels is None else self.hf_compute_loss(snake_case__ ,							snake_case__						)

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

       return TFImageClassifierOutputWithNoAttention(loss=snake_case__ ,							logits=snake_case__ ,							hidden_states=outputs.hidden_states						)
