"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...modeling_tf_outputs import (TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFImageClassifierOutputWithNoAttention)
from ...modeling_tf_utils import (TFPreTrainedModel, TFSequenceClassificationLoss, keras, 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
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ResNetConfig"
_CHECKPOINT_FOR_DOC = "microsoft/resnet-50"
_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7]
_IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
class TFResNetConvLayer(keras.layers.Layer):
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu", **kwargs) -> None:
        super().__init__(**kwargs)
        self.pad_value = kernel_size // 2
        self.conv = keras.layers.Conv2D(out_channels, kernel_size=kernel_size, strides=stride, padding="valid", use_bias=False, name="convolution")
        self.normalization = keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
        self.activation = ACT2FN[activation] if activation is not None else keras.layers.Activation("linear")
        self.in_channels = in_channels
        self.out_channels = out_channels
    def convolution(self, hidden_state: tf.Tensor) -> tf.Tensor:
        height_pad = width_pad = (self.pad_value, self.pad_value)
        hidden_state = tf.pad(hidden_state, [(0, 0), height_pad, width_pad, (0, 0)])
        hidden_state = self.conv(hidden_state)
        return hidden_state
    def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_state = self.convolution(hidden_state)
        hidden_state = self.normalization(hidden_state, training=training)
        hidden_state = self.activation(hidden_state)
        return hidden_state
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "conv", None) is not None:
            with tf.name_scope(self.conv.name): self.conv.build([None, None, None, self.in_channels])
        if getattr(self, "normalization", None) is not None:
            with tf.name_scope(self.normalization.name): self.normalization.build([None, None, None, self.out_channels])
class TFResNetEmbeddings(keras.layers.Layer):
    def __init__(self, config: ResNetConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.embedder = TFResNetConvLayer(config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act, name="embedder")
        self.pooler = keras.layers.MaxPool2D(pool_size=3, strides=2, padding="valid", name="pooler")
        self.num_channels = config.num_channels
    def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
        _, _, _, num_channels = shape_list(pixel_values)
        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.")
        hidden_state = pixel_values
        hidden_state = self.embedder(hidden_state)
        hidden_state = tf.pad(hidden_state, [[0, 0], [1, 1], [1, 1], [0, 0]])
        hidden_state = self.pooler(hidden_state)
        return hidden_state
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embedder", None) is not None:
            with tf.name_scope(self.embedder.name): self.embedder.build(None)
        if getattr(self, "pooler", None) is not None:
            with tf.name_scope(self.pooler.name): self.pooler.build(None)
class TFResNetShortCut(keras.layers.Layer):
    def __init__(self, in_channels: int, out_channels: int, stride: int = 2, **kwargs) -> None:
        super().__init__(**kwargs)
        self.convolution = keras.layers.Conv2D(out_channels, kernel_size=1, strides=stride, use_bias=False, name="convolution")
        self.normalization = keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
        self.in_channels = in_channels
        self.out_channels = out_channels
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_state = x
        hidden_state = self.convolution(hidden_state)
        hidden_state = self.normalization(hidden_state, training=training)
        return hidden_state
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "convolution", None) is not None:
            with tf.name_scope(self.convolution.name): self.convolution.build([None, None, None, self.in_channels])
        if getattr(self, "normalization", None) is not None:
            with tf.name_scope(self.normalization.name): self.normalization.build([None, None, None, self.out_channels])
class TFResNetBasicLayer(keras.layers.Layer):
    def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu", **kwargs) -> None:
        super().__init__(**kwargs)
        should_apply_shortcut = in_channels != out_channels or stride != 1
        self.conv1 = TFResNetConvLayer(in_channels, out_channels, stride=stride, name="layer.0")
        self.conv2 = TFResNetConvLayer(out_channels, out_channels, activation=None, name="layer.1")
        self.shortcut = (TFResNetShortCut(in_channels, out_channels, stride=stride, name="shortcut") if should_apply_shortcut else keras.layers.Activation("linear", name="shortcut"))
        self.activation = ACT2FN[activation]
    def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
        residual = hidden_state
        hidden_state = self.conv1(hidden_state, training=training)
        hidden_state = self.conv2(hidden_state, training=training)
        residual = self.shortcut(residual, training=training)
        hidden_state += residual
        hidden_state = self.activation(hidden_state)
        return hidden_state
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "conv1", None) is not None:
            with tf.name_scope(self.conv1.name): self.conv1.build(None)
        if getattr(self, "conv2", None) is not None:
            with tf.name_scope(self.conv2.name): self.conv2.build(None)
        if getattr(self, "shortcut", None) is not None:
            with tf.name_scope(self.shortcut.name): self.shortcut.build(None)
class TFResNetBottleNeckLayer(keras.layers.Layer):
    def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu", reduction: int = 4, **kwargs) -> None:
        super().__init__(**kwargs)
        should_apply_shortcut = in_channels != out_channels or stride != 1
        reduces_channels = out_channels // reduction
        self.conv0 = TFResNetConvLayer(in_channels, reduces_channels, kernel_size=1, name="layer.0")
        self.conv1 = TFResNetConvLayer(reduces_channels, reduces_channels, stride=stride, name="layer.1")
        self.conv2 = TFResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None, name="layer.2")
        self.shortcut = (TFResNetShortCut(in_channels, out_channels, stride=stride, name="shortcut") if should_apply_shortcut else keras.layers.Activation("linear", name="shortcut"))
        self.activation = ACT2FN[activation]
    def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
        residual = hidden_state
        hidden_state = self.conv0(hidden_state, training=training)
        hidden_state = self.conv1(hidden_state, training=training)
        hidden_state = self.conv2(hidden_state, training=training)
        residual = self.shortcut(residual, training=training)
        hidden_state += residual
        hidden_state = self.activation(hidden_state)
        return hidden_state
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "conv0", None) is not None:
            with tf.name_scope(self.conv0.name): self.conv0.build(None)
        if getattr(self, "conv1", None) is not None:
            with tf.name_scope(self.conv1.name): self.conv1.build(None)
        if getattr(self, "conv2", None) is not None:
            with tf.name_scope(self.conv2.name): self.conv2.build(None)
        if getattr(self, "shortcut", None) is not None:
            with tf.name_scope(self.shortcut.name): self.shortcut.build(None)
class TFResNetStage(keras.layers.Layer):
    def __init__(self, config: ResNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, **kwargs) -> None:
        super().__init__(**kwargs)
        layer = TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer
        layers = [layer(in_channels, out_channels, stride=stride, activation=config.hidden_act, name="layers.0")]
        layers += [layer(out_channels, out_channels, activation=config.hidden_act, name=f"layers.{i + 1}") for i in range(depth - 1)]
        self.stage_layers = layers
    def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
        for layer in self.stage_layers: hidden_state = layer(hidden_state, training=training)
        return hidden_state
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "stage_layers", None) is not None:
            for layer in self.stage_layers:
                with tf.name_scope(layer.name): layer.build(None)
class TFResNetEncoder(keras.layers.Layer):
    def __init__(self, config: ResNetConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.stages = [TFResNetStage(config, 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(config, in_channels, out_channels, depth=depth, name=f"stages.{i + 1}"))
    def call(self, hidden_state: tf.Tensor, output_hidden_states: bool = False, return_dict: bool = True, training: bool = False) -> TFBaseModelOutputWithNoAttention:
        hidden_states = () if output_hidden_states else None
        for stage_module in self.stages:
            if output_hidden_states: hidden_states = hidden_states + (hidden_state,)
            hidden_state = stage_module(hidden_state, training=training)
        if output_hidden_states: hidden_states = 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=hidden_state, hidden_states=hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "stages", None) is not None:
            for layer in self.stages:
                with tf.name_scope(layer.name): layer.build(None)
class TFResNetPreTrainedModel(TFPreTrainedModel):
    config_class = ResNetConfig
    base_model_prefix = "resnet"
    main_input_name = "pixel_values"
    @property
    def input_signature(self): return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224), dtype=tf.float32)}
RESNET_START_DOCSTRING = r"""
    This model is a TensorFlow
    [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.
"""
RESNET_INPUTS_DOCSTRING = 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 TFResNetMainLayer(keras.layers.Layer):
    config_class = ResNetConfig
    def __init__(self, config: ResNetConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.config = config
        self.embedder = TFResNetEmbeddings(config, name="embedder")
        self.encoder = TFResNetEncoder(config, name="encoder")
        self.pooler = keras.layers.GlobalAveragePooling2D(keepdims=True)
    @unpack_inputs
    def call(self, pixel_values: tf.Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    training: bool = False) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        pixel_values = tf.transpose(pixel_values, perm=[0, 2, 3, 1])
        embedding_output = self.embedder(pixel_values, training=training)
        encoder_outputs = self.encoder(embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        last_hidden_state = encoder_outputs[0]
        pooled_output = self.pooler(last_hidden_state)
        last_hidden_state = tf.transpose(last_hidden_state, (0, 3, 1, 2))
        pooled_output = tf.transpose(pooled_output, (0, 3, 1, 2))
        hidden_states = ()
        for hidden_state in encoder_outputs[1:]: hidden_states = hidden_states + tuple(tf.transpose(h, (0, 3, 1, 2)) for h in hidden_state)
        if not return_dict: return (last_hidden_state, pooled_output) + hidden_states
        hidden_states = hidden_states if output_hidden_states else None
        return TFBaseModelOutputWithPoolingAndNoAttention(last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embedder", None) is not None:
            with tf.name_scope(self.embedder.name): self.embedder.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
@add_start_docstrings("The bare ResNet model outputting raw features without any specific head on top.", RESNET_START_DOCSTRING)
class TFResNetModel(TFResNetPreTrainedModel):
    def __init__(self, config: ResNetConfig, **kwargs) -> None:
        super().__init__(config, **kwargs)
        self.resnet = TFResNetMainLayer(config=config, name="resnet")
    @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, pixel_values: tf.Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    training: bool = False) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        resnet_outputs = self.resnet(pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        return resnet_outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "resnet", None) is not None:
            with tf.name_scope(self.resnet.name): self.resnet.build(None)
@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.", RESNET_START_DOCSTRING)
class TFResNetForImageClassification(TFResNetPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: ResNetConfig, **kwargs) -> None:
        super().__init__(config, **kwargs)
        self.num_labels = config.num_labels
        self.resnet = TFResNetMainLayer(config, name="resnet")
        self.classifier_layer = (keras.layers.Dense(config.num_labels, name="classifier.1") if config.num_labels > 0 else keras.layers.Activation("linear", name="classifier.1"))
        self.config = config
    def classifier(self, x: tf.Tensor) -> tf.Tensor:
        x = keras.layers.Flatten()(x)
        logits = self.classifier_layer(x)
        return logits
    @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
    @unpack_inputs
    def call(self, pixel_values: tf.Tensor = None, labels: tf.Tensor = None, output_hidden_states: bool = None, return_dict: bool = None,
    training: bool = False) -> Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        pooled_output = outputs.pooler_output if return_dict else outputs[1]
        logits = self.classifier(pooled_output)
        loss = None if labels is None else self.hf_compute_loss(labels, logits)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return (loss,) + output if loss is not None else output
        return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "resnet", None) is not None:
            with tf.name_scope(self.resnet.name): self.resnet.build(None)
        if getattr(self, "classifier_layer", None) is not None:
            with tf.name_scope(self.classifier_layer.name): self.classifier_layer.build([None, None, self.config.hidden_sizes[-1]])
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
