

"""simple docstring"""






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, 2_048, 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 	_lowerCAmelCase    (      tf.keras.layers.Layer       ):

							def __init__( self       :    Tuple ,		a       :    int ,		a       :    int = 3 ,		a       :    int = 1 ,		a       :    str = "relu" ,		**a       :    Union[str, Any]    )   ->    None:

									"""simple docstring"""
									super().__init__(**a    )
									lowercase										=							kernel_size // 2
									lowercase										=							tf.keras.layers.ConvaD(
									    a ,		kernel_size=a ,		strides=a ,		padding='''valid''' ,		use_bias=a ,		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 					_lowerCAmelCase  ( self       :    Any ,		a       :    tf.Tensor    )   ->    tf.Tensor:

									"""simple docstring"""
									# Pad to match that done in the PyTorch Conv2D model
									lowercase										=							lowercase										=							(self.pad_value, self.pad_value)
									lowercase										=							tf.pad(a ,		[(0, 0), height_pad, width_pad, (0, 0)]    )
									lowercase										=							self.conv(a    )
									return hidden_state

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    tf.Tensor ,		a       :    bool = False    )   ->    tf.Tensor:

									"""simple docstring"""
									lowercase										=							self.convolution(a    )
									lowercase										=							self.normalization(a ,		training=a    )
									lowercase										=							self.activation(a    )
									return hidden_state




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

							def __init__( self       :    Any ,		a       :    ResNetConfig ,		**a       :    List[Any]    )   ->    None:

									"""simple docstring"""
									super().__init__(**a    )
									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 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    tf.Tensor ,		a       :    bool = False    )   ->    tf.Tensor:

									"""simple docstring"""
									lowercase					,  lowercase					,  lowercase					,  lowercase										=							shape_list(a    )
									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(a    )
									lowercase										=							tf.pad(a ,		[[0, 0], [1, 1], [1, 1], [0, 0]]    )
									lowercase										=							self.pooler(a    )
									return hidden_state




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

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

									"""simple docstring"""
									super().__init__(**a    )
									lowercase										=							tf.keras.layers.ConvaD(
									    a ,		kernel_size=1 ,		strides=a ,		use_bias=a ,		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 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    tf.Tensor ,		a       :    bool = False    )   ->    tf.Tensor:

									"""simple docstring"""
									lowercase										=							x
									lowercase										=							self.convolution(a    )
									lowercase										=							self.normalization(a ,		training=a    )
									return hidden_state




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

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

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

							def 					_lowerCAmelCase  ( self       :    List[str] ,		a       :    tf.Tensor ,		a       :    bool = False    )   ->    tf.Tensor:

									"""simple docstring"""
									lowercase										=							hidden_state
									lowercase										=							self.conva(a ,		training=a    )
									lowercase										=							self.conva(a ,		training=a    )
									lowercase										=							self.shortcut(a ,		training=a    )
									hidden_state += residual
									lowercase										=							self.activation(a    )
									return hidden_state




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

							def __init__( self       :    Any ,		a       :    int ,		a       :    int ,		a       :    int = 1 ,		a       :    str = "relu" ,		a       :    int = 4 ,		**a       :    str ,		)   ->    None:

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

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    tf.Tensor ,		a       :    bool = False    )   ->    tf.Tensor:

									"""simple docstring"""
									lowercase										=							hidden_state
									lowercase										=							self.conva(a ,		training=a    )
									lowercase										=							self.conva(a ,		training=a    )
									lowercase										=							self.conva(a ,		training=a    )
									lowercase										=							self.shortcut(a ,		training=a    )
									hidden_state += residual
									lowercase										=							self.activation(a    )
									return hidden_state




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

							def __init__( self       :    Any ,		a       :    ResNetConfig ,		a       :    int ,		a       :    int ,		a       :    int = 2 ,		a       :    int = 2 ,		**a       :    Dict    )   ->    None:

									"""simple docstring"""
									super().__init__(**a    )

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

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

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    tf.Tensor ,		a       :    bool = False    )   ->    tf.Tensor:

									"""simple docstring"""
									for layer in self.stage_layers:
											lowercase										=							layer(a ,		training=a    )
									return hidden_state




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

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

									"""simple docstring"""
									super().__init__(**a    )
									# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
									lowercase										=							[
									    TFResNetStage(
									        a ,		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(a ,		a ,		a ,		depth=a ,		name=f"""stages.{i + 1}"""    )    )

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    tf.Tensor ,		a       :    bool = False ,		a       :    bool = True ,		a       :    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(a ,		training=a    )

									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=a ,		hidden_states=a    )




class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    Optional[Any]     =  ResNetConfig
							__lowerCAmelCase					:    Dict     =  '''resnet'''
							__lowerCAmelCase					:    List[str]     =  '''pixel_values'''

							@property
							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    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 	_lowerCAmelCase    (      tf.keras.layers.Layer       ):
							__lowerCAmelCase					:    List[str]     =  ResNetConfig

							def __init__( self       :    List[str] ,		a       :    ResNetConfig ,		**a       :    List[Any]    )   ->    None:

									"""simple docstring"""
									super().__init__(**a    )
									lowercase										=							config
									lowercase										=							TFResNetEmbeddings(a ,		name='''embedder'''    )
									lowercase										=							TFResNetEncoder(a ,		name='''encoder'''    )
									lowercase										=							tf.keras.layers.GlobalAveragePoolingaD(keepdims=a    )

							@unpack_inputs
							def 					_lowerCAmelCase  ( self       :    str ,		a       :    tf.Tensor ,		a       :    Optional[bool] = None ,		a       :    Optional[bool] = None ,		a       :    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(a ,		perm=[0, 2, 3, 1]    )
									lowercase										=							self.embedder(a ,		training=a    )

									lowercase										=							self.encoder(
									    a ,		output_hidden_states=a ,		return_dict=a ,		training=a    )

									lowercase										=							encoder_outputs[0]

									lowercase										=							self.pooler(a    )

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




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

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

									"""simple docstring"""
									super().__init__(a ,		**a    )
									lowercase										=							TFResNetMainLayer(config=a ,		name='''resnet'''    )

							@add_start_docstrings_to_model_forward(a    )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC ,		output_type=a ,		config_class=_CONFIG_FOR_DOC ,		modality='''vision''' ,		expected_output=_EXPECTED_OUTPUT_SHAPE ,		)
							@unpack_inputs
							def 					_lowerCAmelCase  ( self       :    List[str] ,		a       :    tf.Tensor ,		a       :    Optional[bool] = None ,		a       :    Optional[bool] = None ,		a       :    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=a ,		output_hidden_states=a ,		return_dict=a ,		training=a ,		)
									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.
    '''       ,       __snake_case       ,       )
class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ):

							def __init__( self       :    str ,		a       :    ResNetConfig ,		**a       :    List[str]    )   ->    None:

									"""simple docstring"""
									super().__init__(a ,		**a    )
									lowercase										=							config.num_labels
									lowercase										=							TFResNetMainLayer(a ,		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 					_lowerCAmelCase  ( self       :    int ,		a       :    tf.Tensor    )   ->    tf.Tensor:

									"""simple docstring"""
									lowercase										=							tf.keras.layers.Flatten()(a    )
									lowercase										=							self.classifier_layer(a    )
									return logits

							@add_start_docstrings_to_model_forward(a    )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT ,		output_type=a ,		config_class=_CONFIG_FOR_DOC ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,		)
							@unpack_inputs
							def 					_lowerCAmelCase  ( self       :    List[str] ,		a       :    tf.Tensor = None ,		a       :    tf.Tensor = None ,		a       :    bool = None ,		a       :    bool = None ,		a       :    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(
									    a ,		output_hidden_states=a ,		return_dict=a ,		training=a    )

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

									lowercase										=							self.classifier(a    )

									lowercase										=							None if labels is None else self.hf_compute_loss(a ,		a    )

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

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


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase				      =   {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
							from .tokenization_phobert import PhobertTokenizer

else:
							import sys

							__lowerCAmelCase				      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)