"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
import collections.abc
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (TFBaseModelOutputWithNoAttention, TFImageClassifierOutputWithNoAttention)
from ...modeling_tf_utils import TFPreTrainedModel, keras, keras_serializable, unpack_inputs
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, logging)
from .configuration_swiftformer import SwiftFormerConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "SwiftFormerConfig"
_CHECKPOINT_FOR_DOC = "MBZUAI/swiftformer-xs"
_EXPECTED_OUTPUT_SHAPE = [1, 220, 7, 7]
_IMAGE_CLASS_CHECKPOINT = "MBZUAI/swiftformer-xs"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
class TFSwiftFormerPatchEmbeddingSequential(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, **kwargs):
        super().__init__(**kwargs)
        self.out_chs = config.embed_dims[0]
        self.zero_padding = keras.layers.ZeroPadding2D(padding=(1, 1))
        self.conv1 = keras.layers.Conv2D(self.out_chs // 2, kernel_size=3, strides=2, name="0")
        self.batch_norm1 = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="1")
        self.conv2 = keras.layers.Conv2D(self.out_chs, kernel_size=3, strides=2, name="3")
        self.batch_norm2 = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="4")
        self.config = config
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        x = self.zero_padding(x)
        x = self.conv1(x)
        x = self.batch_norm1(x, training=training)
        x = get_tf_activation("relu")(x)
        x = self.zero_padding(x)
        x = self.conv2(x)
        x = self.batch_norm2(x, training=training)
        x = get_tf_activation("relu")(x)
        return x
    def build(self, input_shape=None):
        if self.built: return
        if getattr(self, "conv1", None) is not None:
            with tf.name_scope(self.conv1.name): self.conv1.build(self.config.num_channels)
        if getattr(self, "batch_norm1", None) is not None:
            with tf.name_scope(self.batch_norm1.name): self.batch_norm1.build((None, None, None, self.out_chs // 2))
        if getattr(self, "conv2", None) is not None:
            with tf.name_scope(self.conv2.name): self.conv2.build((None, None, None, self.out_chs // 2))
        if getattr(self, "batch_norm2", None) is not None:
            with tf.name_scope(self.batch_norm2.name): self.batch_norm2.build((None, None, None, self.out_chs))
        self.built = True
class TFSwiftFormerPatchEmbedding(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, **kwargs):
        super().__init__(**kwargs)
        self.patch_embedding = TFSwiftFormerPatchEmbeddingSequential(config, name="patch_embedding")
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        return self.patch_embedding(x, training=training)
    def build(self, input_shape=None):
        if self.built: return
        if getattr(self, "patch_embedding", None) is not None:
            with tf.name_scope(self.patch_embedding.name): self.patch_embedding.build(None)
        self.built = True
class TFSwiftFormerDropPath(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        raise NotImplementedError("Drop path is not implemented in TF port")
    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: raise NotImplementedError("Drop path is not implemented in TF port")
class TFSwiftFormerEmbeddings(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, index: int, **kwargs):
        super().__init__(**kwargs)
        patch_size = config.down_patch_size
        stride = config.down_stride
        padding = config.down_pad
        embed_dims = config.embed_dims
        self.in_chans = embed_dims[index]
        self.embed_dim = embed_dims[index + 1]
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride)
        padding = padding if isinstance(padding, collections.abc.Iterable) else (padding, padding)
        self.pad = keras.layers.ZeroPadding2D(padding=padding)
        self.proj = keras.layers.Conv2D(self.embed_dim, kernel_size=patch_size, strides=stride, name="proj")
        self.norm = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="norm")
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        x = self.pad(x)
        x = self.proj(x)
        x = self.norm(x, training=training)
        return x
    def build(self, input_shape=None):
        if self.built: return
        if getattr(self, "proj", None) is not None:
            with tf.name_scope(self.proj.name): self.proj.build(self.in_chans)
        if getattr(self, "norm", None) is not None:
            with tf.name_scope(self.norm.name): self.norm.build((None, None, None, self.embed_dim))
        self.built = True
class TFSwiftFormerConvEncoder(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, dim: int, **kwargs):
        super().__init__(**kwargs)
        hidden_dim = int(config.mlp_ratio * dim)
        self.dim = dim
        self.pad = keras.layers.ZeroPadding2D(padding=(1, 1))
        self.depth_wise_conv = keras.layers.Conv2D(dim, kernel_size=3, groups=dim, name="depth_wise_conv")
        self.norm = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="norm")
        self.point_wise_conv1 = keras.layers.Conv2D(hidden_dim, kernel_size=1, name="point_wise_conv1")
        self.act = get_tf_activation("gelu")
        self.point_wise_conv2 = keras.layers.Conv2D(dim, kernel_size=1, name="point_wise_conv2")
        self.drop_path = keras.layers.Dropout(name="drop_path", rate=config.drop_conv_encoder_rate)
        self.hidden_dim = int(config.mlp_ratio * self.dim)
    def build(self, input_shape=None):
        if self.built: return
        self.layer_scale = self.add_weight(name="layer_scale", shape=self.dim, initializer="ones", trainable=True)
        if getattr(self, "depth_wise_conv", None) is not None:
            with tf.name_scope(self.depth_wise_conv.name): self.depth_wise_conv.build(self.dim)
        if getattr(self, "norm", None) is not None:
            with tf.name_scope(self.norm.name): self.norm.build((None, None, None, self.dim))
        if getattr(self, "point_wise_conv1", None) is not None:
            with tf.name_scope(self.point_wise_conv1.name): self.point_wise_conv1.build(self.dim)
        if getattr(self, "point_wise_conv2", None) is not None:
            with tf.name_scope(self.point_wise_conv2.name): self.point_wise_conv2.build(self.hidden_dim)
        if getattr(self, "drop_path", None) is not None:
            with tf.name_scope(self.drop_path.name): self.drop_path.build(None)
        self.built = True
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        input = x
        x = self.pad(x)
        x = self.depth_wise_conv(x)
        x = self.norm(x, training=training)
        x = self.point_wise_conv1(x)
        x = self.act(x)
        x = self.point_wise_conv2(x)
        x = input + self.drop_path(self.layer_scale * x)
        return x
class TFSwiftFormerMlp(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, in_features: int, **kwargs):
        super().__init__(**kwargs)
        hidden_features = int(in_features * config.mlp_ratio)
        self.norm1 = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="norm1")
        self.fc1 = keras.layers.Conv2D(hidden_features, 1, name="fc1")
        act_layer = get_tf_activation(config.hidden_act)
        self.act = act_layer
        self.fc2 = keras.layers.Conv2D(in_features, 1, name="fc2")
        self.drop = keras.layers.Dropout(rate=config.drop_mlp_rate)
        self.hidden_features = hidden_features
        self.in_features = in_features
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        x = self.norm1(x, training=training)
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x, training=training)
        x = self.fc2(x)
        x = self.drop(x, training=training)
        return x
    def build(self, input_shape=None):
        if self.built: return
        if getattr(self, "norm1", None) is not None:
            with tf.name_scope(self.norm1.name): self.norm1.build((None, None, None, self.in_features))
        if getattr(self, "fc1", None) is not None:
            with tf.name_scope(self.fc1.name): self.fc1.build((None, None, None, self.in_features))
        if getattr(self, "fc2", None) is not None:
            with tf.name_scope(self.fc2.name): self.fc2.build((None, None, None, self.hidden_features))
        self.built = True
class TFSwiftFormerEfficientAdditiveAttention(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, dim: int = 512, **kwargs):
        super().__init__(**kwargs)
        self.dim = dim
        self.to_query = keras.layers.Dense(dim, name="to_query")
        self.to_key = keras.layers.Dense(dim, name="to_key")
        self.scale_factor = dim**-0.5
        self.proj = keras.layers.Dense(dim, name="proj")
        self.final = keras.layers.Dense(dim, name="final")
    def build(self, input_shape=None):
        if self.built: return
        self.w_g = self.add_weight(name="w_g", shape=(self.dim, 1), initializer=keras.initializers.RandomNormal(mean=0, stddev=1), trainable=True)
        if getattr(self, "to_query", None) is not None:
            with tf.name_scope(self.to_query.name): self.to_query.build(self.dim)
        if getattr(self, "to_key", None) is not None:
            with tf.name_scope(self.to_key.name): self.to_key.build(self.dim)
        if getattr(self, "proj", None) is not None:
            with tf.name_scope(self.proj.name): self.proj.build(self.dim)
        if getattr(self, "final", None) is not None:
            with tf.name_scope(self.final.name): self.final.build(self.dim)
        self.built = True
    def call(self, x: tf.Tensor) -> tf.Tensor:
        query = self.to_query(x)
        key = self.to_key(x)
        query = tf.math.l2_normalize(query, dim=-1)
        key = tf.math.l2_normalize(key, dim=-1)
        query_weight = query @ self.w_g
        scaled_query_weight = query_weight * self.scale_factor
        scaled_query_weight = tf.nn.softmax(scaled_query_weight, axis=-1)
        global_queries = tf.math.reduce_sum(scaled_query_weight * query, axis=1)
        global_queries = tf.tile(tf.expand_dims(global_queries, 1), (1, key.shape[1], 1))
        out = self.proj(global_queries * key) + query
        out = self.final(out)
        return out
class TFSwiftFormerLocalRepresentation(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, dim: int, **kwargs):
        super().__init__(**kwargs)
        self.dim = dim
        self.pad = keras.layers.ZeroPadding2D(padding=(1, 1))
        self.depth_wise_conv = keras.layers.Conv2D(dim, kernel_size=3, groups=dim, name="depth_wise_conv")
        self.norm = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="norm")
        self.point_wise_conv1 = keras.layers.Conv2D(dim, kernel_size=1, name="point_wise_conv1")
        self.act = get_tf_activation("gelu")
        self.point_wise_conv2 = keras.layers.Conv2D(dim, kernel_size=1, name="point_wise_conv2")
        self.drop_path = keras.layers.Identity(name="drop_path")
    def build(self, input_shape=None):
        if self.built: return
        self.layer_scale = self.add_weight(name="layer_scale", shape=(self.dim), initializer="ones", trainable=True)
        if getattr(self, "depth_wise_conv", None) is not None:
            with tf.name_scope(self.depth_wise_conv.name): self.depth_wise_conv.build((None, None, None, self.dim))
        if getattr(self, "norm", None) is not None:
            with tf.name_scope(self.norm.name): self.norm.build((None, None, None, self.dim))
        if getattr(self, "point_wise_conv1", None) is not None:
            with tf.name_scope(self.point_wise_conv1.name): self.point_wise_conv1.build(self.dim)
        if getattr(self, "point_wise_conv2", None) is not None:
            with tf.name_scope(self.point_wise_conv2.name): self.point_wise_conv2.build(self.dim)
        if getattr(self, "drop_path", None) is not None:
            with tf.name_scope(self.drop_path.name): self.drop_path.build(None)
        self.built = True
    def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
        input = x
        x = self.pad(x)
        x = self.depth_wise_conv(x)
        x = self.norm(x, training=training)
        x = self.point_wise_conv1(x)
        x = self.act(x)
        x = self.point_wise_conv2(x)
        x = input + self.drop_path(self.layer_scale * x, training=training)
        return x
class TFSwiftFormerEncoderBlock(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, dim: int, drop_path: float = 0.0, **kwargs):
        super().__init__(**kwargs)
        layer_scale_init_value = config.layer_scale_init_value
        use_layer_scale = config.use_layer_scale
        self.local_representation = TFSwiftFormerLocalRepresentation(config, dim=dim, name="local_representation")
        self.attn = TFSwiftFormerEfficientAdditiveAttention(config, dim=dim, name="attn")
        self.linear = TFSwiftFormerMlp(config, in_features=dim, name="linear")
        self.drop_path = TFSwiftFormerDropPath(config) if drop_path > 0.0 else keras.layers.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.dim = dim
            self.layer_scale_init_value = layer_scale_init_value
    def build(self, input_shape=None):
        if self.built: return
        self.layer_scale_1 = self.add_weight(name="layer_scale_1", shape=self.dim, initializer=keras.initializers.constant(self.layer_scale_init_value), trainable=True)
        self.layer_scale_2 = self.add_weight(name="layer_scale_2", shape=self.dim, initializer=keras.initializers.constant(self.layer_scale_init_value), trainable=True)
        if getattr(self, "local_representation", None) is not None:
            with tf.name_scope(self.local_representation.name): self.local_representation.build(None)
        if getattr(self, "attn", None) is not None:
            with tf.name_scope(self.attn.name): self.attn.build(None)
        if getattr(self, "linear", None) is not None:
            with tf.name_scope(self.linear.name): self.linear.build(None)
        self.built = True
    def call(self, x: tf.Tensor, training: bool = False):
        x = self.local_representation(x, training=training)
        batch_size, height, width, channels = x.shape
        res = tf.reshape(x, [-1, height * width, channels])
        res = self.attn(res)
        res = tf.reshape(res, [-1, height, width, channels])
        if self.use_layer_scale:
            x = x + self.drop_path(self.layer_scale_1 * res, training=training)
            x = x + self.drop_path(self.layer_scale_2 * self.linear(x), training=training)
        else:
            x = x + self.drop_path(res, training=training)
            x = x + self.drop_path(self.linear(x), training=training)
        return x
class TFSwiftFormerStage(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, index: int, **kwargs) -> None:
        super().__init__(**kwargs)
        layer_depths = config.depths
        dim = config.embed_dims[index]
        depth = layer_depths[index]
        self.blocks = []
        for block_idx in range(depth):
            block_dpr = config.drop_path_rate * (block_idx + sum(layer_depths[:index])) / (sum(layer_depths) - 1)
            if depth - block_idx <= 1: self.blocks.append(TFSwiftFormerEncoderBlock(config, dim=dim, drop_path=block_dpr, name=f"blocks_._{block_idx}"))
            else: self.blocks.append(TFSwiftFormerConvEncoder(config, dim=dim, name=f"blocks_._{block_idx}"))
    def call(self, input: tf.Tensor, training: bool = False) -> tf.Tensor:
        for i, block in enumerate(self.blocks): input = block(input, training=training)
        return input
    def build(self, input_shape=None):
        for layer in self.blocks:
            with tf.name_scope(layer.name): layer.build(None)
class TFSwiftFormerEncoder(keras.layers.Layer):
    def __init__(self, config: SwiftFormerConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.config = config
        embed_dims = config.embed_dims
        downsamples = config.downsamples
        layer_depths = config.depths
        self.network = []
        name_i = 0
        for i in range(len(layer_depths)):
            stage = TFSwiftFormerStage(config, index=i, name=f"network_._{name_i}")
            self.network.append(stage)
            name_i += 1
            if i >= len(layer_depths) - 1: break
            if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
                self.network.append(TFSwiftFormerEmbeddings(config, index=i, name=f"network_._{name_i}"))
                name_i += 1
        self.gradient_checkpointing = False
    def call(self, hidden_states: tf.Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[tuple, TFBaseModelOutputWithNoAttention]:
        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
        all_hidden_states = (hidden_states,) if output_hidden_states else None
        for i, block in enumerate(self.network):
            hidden_states = block(hidden_states, training=training)
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        hidden_states = tf.transpose(hidden_states, perm=[0, 3, 1, 2])
        if all_hidden_states: all_hidden_states = tuple(tf.transpose(s, perm=[0, 3, 1, 2]) for s in all_hidden_states)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
        return TFBaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
    def build(self, input_shape=None):
        for layer in self.network:
            with tf.name_scope(layer.name): layer.build(None)
class TFSwiftFormerPreTrainedModel(TFPreTrainedModel):
    config_class = SwiftFormerConfig
    base_model_prefix = "swiftformer"
    main_input_name = "pixel_values"
TFSWIFTFORMER_START_DOCSTRING = r"""
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.
    <Tip>
    TF 2.0 models accepts two formats as inputs:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.
    This second option is useful when using [`keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.
    If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
    first positional argument :
    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
      `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
      `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
    </Tip>
    Parameters:
        config ([`SwiftFormerConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
TFSWIFTFORMER_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 [`ViTImageProcessor.__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.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to run the model in training mode.
"""
@keras_serializable
class TFSwiftFormerMainLayer(keras.layers.Layer):
    config_class = SwiftFormerConfig
    def __init__(self, config: SwiftFormerConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.patch_embed = TFSwiftFormerPatchEmbedding(config, name="patch_embed")
        self.encoder = TFSwiftFormerEncoder(config, name="encoder")
    @unpack_inputs
    def call(self, pixel_values: Optional[tf.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[Tuple, TFBaseModelOutputWithNoAttention]:
        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])
        if pixel_values is None: raise ValueError("You have to specify pixel_values")
        embedding_output = self.patch_embed(pixel_values, training=training)
        encoder_outputs = self.encoder(embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        if not return_dict: return tuple(v for v in encoder_outputs if v is not None)
        return TFBaseModelOutputWithNoAttention(last_hidden_state=encoder_outputs.last_hidden_state, hidden_states=encoder_outputs.hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        if getattr(self, "patch_embed", None) is not None:
            with tf.name_scope(self.patch_embed.name): self.patch_embed.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        self.built = True
@add_start_docstrings("The bare TFSwiftFormer Model transformer outputting raw hidden-states without any specific head on top.", TFSWIFTFORMER_START_DOCSTRING)
class TFSwiftFormerModel(TFSwiftFormerPreTrainedModel):
    def __init__(self, config: SwiftFormerConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.swiftformer = TFSwiftFormerMainLayer(config, name="swiftformer")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(TFSWIFTFORMER_INPUTS_DOCSTRING)
    def call(self, pixel_values: Optional[tf.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[TFBaseModelOutputWithNoAttention, Tuple[tf.Tensor]]:
        outputs = self.swiftformer(pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        if getattr(self, "swiftformer", None) is not None:
            with tf.name_scope(self.swiftformer.name): self.swiftformer.build(None)
        self.built = True
@add_start_docstrings("TFSwiftFormer Model transformer with an image classification head on top (e.g. for ImageNet).", TFSWIFTFORMER_START_DOCSTRING)
class TFSwiftFormerForImageClassification(TFSwiftFormerPreTrainedModel):
    def __init__(self, config: SwiftFormerConfig, **kwargs) -> None:
        super().__init__(config, **kwargs)
        self.num_labels = config.num_labels
        self.swiftformer = TFSwiftFormerMainLayer(config, name="swiftformer")
        self.norm = keras.layers.BatchNormalization(epsilon=config.batch_norm_eps, momentum=0.9, name="norm")
        self.head = (keras.layers.Dense(self.num_labels, name="head") if self.num_labels > 0 else keras.layers.Identity(name="head"))
        self.dist_head = (keras.layers.Dense(self.num_labels, name="dist_head") if self.num_labels > 0 else keras.layers.Identity(name="dist_head"))
    def hf_compute_loss(self, labels, logits):
        if self.config.problem_type is None:
            if self.num_labels == 1: self.config.problem_type = "regression"
            elif self.num_labels > 1 and (labels.dtype == tf.int64 or labels.dtype == tf.int32): self.config.problem_type = "single_label_classification"
            else: self.config.problem_type = "multi_label_classification"
        if self.config.problem_type == "regression":
            loss_fct = keras.losses.MSE
            if self.num_labels == 1: loss = loss_fct(labels.squeeze(), logits.squeeze())
            else: loss = loss_fct(labels, logits)
        elif self.config.problem_type == "single_label_classification":
            loss_fct = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
            loss = loss_fct(labels, logits)
        elif self.config.problem_type == "multi_label_classification":
            loss_fct = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
            loss = loss_fct(labels, logits)
        else: loss = None
        return loss
    @unpack_inputs
    @add_start_docstrings_to_model_forward(TFSWIFTFORMER_INPUTS_DOCSTRING)
    def call(self, pixel_values: Optional[tf.Tensor] = None, labels: Optional[tf.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    training: bool = False) -> Union[tuple, TFImageClassifierOutputWithNoAttention]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.swiftformer(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        sequence_output = outputs.last_hidden_state if return_dict else outputs[0]
        sequence_output = tf.transpose(sequence_output, perm=[0, 2, 3, 1])
        sequence_output = self.norm(sequence_output, training=training)
        sequence_output = tf.transpose(sequence_output, perm=[0, 3, 1, 2])
        _, num_channels, height, width = sequence_output.shape
        sequence_output = tf.reshape(sequence_output, [-1, num_channels, height * width])
        sequence_output = tf.reduce_mean(sequence_output, axis=-1)
        cls_out = self.head(sequence_output)
        distillation_out = self.dist_head(sequence_output)
        logits = (cls_out + distillation_out) / 2
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
        if not return_dict:
            output = (logits,) + outputs[1:]
            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
        if getattr(self, "swiftformer", None) is not None:
            with tf.name_scope(self.swiftformer.name): self.swiftformer.build(None)
        if getattr(self, "norm", None) is not None:
            with tf.name_scope(self.norm.name): self.norm.build((None, None, None, self.config.embed_dims[-1]))
        if getattr(self, "head", None) is not None:
            with tf.name_scope(self.head.name): self.head.build(self.config.embed_dims[-1])
        if getattr(self, "dist_head", None) is not None:
            with tf.name_scope(self.dist_head.name): self.dist_head.build(self.config.embed_dims[-1])
        self.built = True
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
