


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case       :					int        =  logging.get_logger(__name__)


if is_vision_available():
     import PIL







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = ['pixel_values']




 def __init__(	self      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						_lowerCamelCase = PILImageResampling.BICUBIC      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = 1 / 255      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = True      ,						**_lowerCamelCase      ,						):
       super().__init__(**_lowerCamelCase					)
       a		:Union[str, Any]											= size if size is not None else {'''shortest_edge''': 224}
       a		:Union[str, Any]											= get_size_dict(_lowerCamelCase      ,						default_to_square=_lowerCamelCase					)
       a		:Any											= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       a		:str											= get_size_dict(_lowerCamelCase					)

       a		:Optional[Any]											= do_resize
       a		:Optional[int]											= size
       a		:Any											= resample
       a		:Tuple											= do_center_crop
       a		:Any											= crop_size
       a		:int											= do_rescale
       a		:str											= rescale_factor
       a		:List[Any]											= do_normalize
       a		:Tuple											= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       a		:Union[str, Any]											= image_std if image_std is not None else OPENAI_CLIP_STD
       a		:str											= do_convert_rgb




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = PILImageResampling.BICUBIC      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       a		:Dict											= get_size_dict(_lowerCamelCase      ,						default_to_square=_lowerCamelCase					)
       a		:int											= get_resize_output_image_size(
           _lowerCamelCase      ,						size=(size['''height'''], size['''width'''])      ,						default_to_square=_lowerCamelCase					)
       return resize(_lowerCamelCase      ,						size=_lowerCamelCase      ,						resample=_lowerCamelCase      ,						data_format=_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       a		:Tuple											= get_size_dict(_lowerCamelCase					)
       return center_crop(_lowerCamelCase      ,						size=(size['''height'''], size['''width'''])      ,						data_format=_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       return rescale(_lowerCamelCase      ,						scale=_lowerCamelCase      ,						data_format=_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       return normalize(_lowerCamelCase      ,						mean=_lowerCamelCase      ,						std=_lowerCamelCase      ,						data_format=_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = ChannelDimension.FIRST      ,						**_lowerCamelCase      ,						):
       a		:str											= do_resize if do_resize is not None else self.do_resize
       a		:int											= size if size is not None else self.size
       a		:Tuple											= get_size_dict(_lowerCamelCase      ,						default_to_square=_lowerCamelCase					)
       a		:Optional[int]											= resample if resample is not None else self.resample
       a		:Dict											= do_center_crop if do_center_crop is not None else self.do_center_crop
       a		:Optional[int]											= crop_size if crop_size is not None else self.crop_size
       a		:str											= get_size_dict(_lowerCamelCase					)
       a		:Tuple											= do_rescale if do_rescale is not None else self.do_rescale
       a		:Optional[int]											= rescale_factor if rescale_factor is not None else self.rescale_factor
       a		:Tuple											= do_normalize if do_normalize is not None else self.do_normalize
       a		:Optional[int]											= image_mean if image_mean is not None else self.image_mean
       a		:Any											= image_std if image_std is not None else self.image_std
       a		:Optional[Any]											= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       a		:Optional[int]											= make_list_of_images(_lowerCamelCase					)

       if not valid_images(_lowerCamelCase					):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

       if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.'''					)

       if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

       if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             a		:Union[str, Any]											= [convert_to_rgb(_lowerCamelCase					) for image in images]

       # All transformations expect numpy arrays.
       a		:int											= [to_numpy_array(_lowerCamelCase					) for image in images]

       if do_resize:
             a		:int											= [self.resize(image=_lowerCamelCase      ,						size=_lowerCamelCase      ,						resample=_lowerCamelCase					) for image in images]

       if do_center_crop:
             a		:int											= [self.center_crop(image=_lowerCamelCase      ,						size=_lowerCamelCase					) for image in images]

       if do_rescale:
             a		:int											= [self.rescale(image=_lowerCamelCase      ,						scale=_lowerCamelCase					) for image in images]

       if do_normalize:
             a		:int											= [self.normalize(image=_lowerCamelCase      ,						mean=_lowerCamelCase      ,						std=_lowerCamelCase					) for image in images]

       a		:Optional[Any]											= [to_channel_dimension_format(_lowerCamelCase      ,						_lowerCamelCase					) for image in images]

       a		:Tuple											= {'''pixel_values''': images}
       return BatchFeature(data=_lowerCamelCase      ,						tensor_type=_lowerCamelCase					)









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


snake_case       :					int        =  logging.get_logger(__name__)

# General docstring
snake_case       :					Optional[int]        =  '''ResNetConfig'''

# Base docstring
snake_case       :					Dict        =  '''microsoft/resnet-50'''
snake_case       :					List[str]        =  [1, 20_48, 7, 7]

# Image classification docstring
snake_case       :					int        =  '''microsoft/resnet-50'''
snake_case       :					Tuple        =  '''tiger cat'''

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







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 3      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:List[Any]											= kernel_size // 2
       a		:Optional[Any]											= tf.keras.layers.ConvaD(
           _lowerCamelCase      ,						kernel_size=_lowerCamelCase      ,						strides=_lowerCamelCase      ,						padding='''valid'''      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
       # Use same default momentum and epsilon as PyTorch equivalent
       a		:Tuple											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)
       a		:int											= ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       # Pad to match that done in the PyTorch Conv2D model
       a		:Optional[Any]											= (self.pad_value, self.pad_value)
       a		:List[str]											= tf.pad(_lowerCamelCase      ,						[(0, 0), height_pad, width_pad, (0, 0)]					)
       a		:Optional[Any]											= self.conv(_lowerCamelCase					)
       return hidden_state




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Tuple											= self.convolution(_lowerCamelCase					)
       a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:Optional[int]											= TFResNetConvLayer(
           config.embedding_size      ,						kernel_size=7      ,						stride=2      ,						activation=config.hidden_act      ,						name='''embedder'''      ,						)
       a		:List[Any]											= tf.keras.layers.MaxPoolaD(pool_size=3      ,						strides=2      ,						padding='''valid'''      ,						name='''pooler'''					)
       a		:Any											= config.num_channels




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a							,       a							,       a							,       a		:int											= shape_list(_lowerCamelCase					)
       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.'''					)
       a		:Dict											= pixel_values
       a		:int											= self.embedder(_lowerCamelCase					)
       a		:str											= tf.pad(_lowerCamelCase      ,						[[0, 0], [1, 1], [1, 1], [0, 0]]					)
       a		:int											= self.pooler(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:int											= tf.keras.layers.ConvaD(
           _lowerCamelCase      ,						kernel_size=1      ,						strides=_lowerCamelCase      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
       # Use same default momentum and epsilon as PyTorch equivalent
       a		:Optional[Any]											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Optional[Any]											= x
       a		:Union[str, Any]											= self.convolution(_lowerCamelCase					)
       a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:Union[str, Any]											= in_channels != out_channels or stride != 1
       a		:str											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.0'''					)
       a		:Any											= TFResNetConvLayer(_lowerCamelCase      ,						activation=_lowerCamelCase      ,						name='''layer.1'''					)
       a		:Any											= (
           TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
       )
       a		:Union[str, Any]											= ACTaFN[activation]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:List[str]											= hidden_state
       a		:Optional[int]											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Tuple											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
       hidden_state += residual
       a		:Dict											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						_lowerCamelCase = 4      ,						**_lowerCamelCase      ,						):
       super().__init__(**_lowerCamelCase					)
       a		:str											= in_channels != out_channels or stride != 1
       a		:Optional[int]											= out_channels // reduction
       a		:List[Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						name='''layer.0'''					)
       a		:Tuple											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.1'''					)
       a		:Union[str, Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						activation=_lowerCamelCase      ,						name='''layer.2'''					)
       a		:Optional[int]											= (
           TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
       )
       a		:str											= ACTaFN[activation]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Union[str, Any]											= hidden_state
       a		:Tuple											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
       hidden_state += residual
       a		:List[Any]											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)

       a		:Dict											= TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

       a		:Dict											= [layer(_lowerCamelCase      ,						_lowerCamelCase      ,						stride=_lowerCamelCase      ,						activation=config.hidden_act      ,						name='''layers.0'''					)]
       layers += [
           layer(_lowerCamelCase      ,						_lowerCamelCase      ,						activation=config.hidden_act      ,						name=F'''layers.{i + 1}'''					)
           for i in range(depth - 1					)
       ]
       a		:str											= layers




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       for layer in self.stage_layers:
             a		:List[Any]											= layer(_lowerCamelCase      ,						training=_lowerCamelCase					)
       return hidden_state







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




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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						):
       a		:str											= () if output_hidden_states else None

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

             a		:Optional[int]											= stage_module(_lowerCamelCase      ,						training=_lowerCamelCase					)

       if output_hidden_states:
             a		:Union[str, Any]											= hidden_states + (hidden_state,)

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

       return TFBaseModelOutputWithNoAttention(last_hidden_state=_lowerCamelCase      ,						hidden_states=_lowerCamelCase					)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = ResNetConfig
 SCREAMING_SNAKE_CASE__   = 'resnet'
 SCREAMING_SNAKE_CASE__   = 'pixel_values'




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







snake_case       :					List[str]        =  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.
'''


snake_case       :					Optional[Any]        =  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							):
 SCREAMING_SNAKE_CASE__   = ResNetConfig




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:str											= config
       a		:Union[str, Any]											= TFResNetEmbeddings(_lowerCamelCase      ,						name='''embedder'''					)
       a		:Tuple											= TFResNetEncoder(_lowerCamelCase      ,						name='''encoder'''					)
       a		:List[str]											= tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCamelCase					)




 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Union[str, Any]											= (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		:List[Any]											= 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)
       a		:Dict											= tf.transpose(_lowerCamelCase      ,						perm=[0, 2, 3, 1]					)
       a		:List[Any]											= self.embedder(_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:Union[str, Any]											= self.encoder(
           _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:List[str]											= encoder_outputs[0]

       a		:int											= self.pooler(_lowerCamelCase					)

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

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

       a		:str											= hidden_states if output_hidden_states else None

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







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:str											= TFResNetMainLayer(config=_lowerCamelCase      ,						name='''resnet'''					)




 @add_start_docstrings_to_model_forward(_lowerCamelCase					)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC      ,						output_type=_lowerCamelCase      ,						config_class=_CONFIG_FOR_DOC      ,						modality='''vision'''      ,						expected_output=_EXPECTED_OUTPUT_SHAPE      ,						)
 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Tuple											= (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		:List[str]											= return_dict if return_dict is not None else self.config.use_return_dict

       a		:Optional[Any]											= self.resnet(
           pixel_values=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase      ,						)
       return resnet_outputs







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:Optional[int]											= config.num_labels
       a		:List[str]											= TFResNetMainLayer(_lowerCamelCase      ,						name='''resnet'''					)
       # classification head
       a		:Optional[Any]											= (
           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      ,						_lowerCamelCase					):
       a		:Optional[int]											= tf.keras.layers.Flatten()(_lowerCamelCase					)
       a		:Tuple											= self.classifier_layer(_lowerCamelCase					)
       return logits




 @add_start_docstrings_to_model_forward(_lowerCamelCase					)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT      ,						output_type=_lowerCamelCase      ,						config_class=_CONFIG_FOR_DOC      ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,						)
 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Optional[Any]											= return_dict if return_dict is not None else self.config.use_return_dict

       a		:Any											= self.resnet(
           _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:Tuple											= outputs.pooler_output if return_dict else outputs[1]

       a		:Optional[int]											= self.classifier(_lowerCamelCase					)

       a		:List[Any]											= None if labels is None else self.hf_compute_loss(_lowerCamelCase      ,						_lowerCamelCase					)

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

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





