from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # 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
import math
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
from ...modeling_tf_utils import (TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import shape_list, stable_softmax
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_vit import ViTConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ViTConfig"
_CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k"
_EXPECTED_OUTPUT_SHAPE = [1, 197, 768]
_IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"
class TFViTEmbeddings(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.patch_embeddings = TFViTPatchEmbeddings(config, name="patch_embeddings")
        self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.config = config
    def build(self, input_shape=None):
        num_patches = self.patch_embeddings.num_patches
        self.cls_token = self.add_weight(shape=(1, 1, self.config.hidden_size), initializer=get_initializer(self.config.initializer_range), trainable=True, name="cls_token")
        self.position_embeddings = self.add_weight(shape=(1, num_patches + 1, self.config.hidden_size), initializer=get_initializer(self.config.initializer_range), trainable=True, name="position_embeddings")
        if self.built: return
        self.built = True
        if getattr(self, "patch_embeddings", None) is not None:
            with tf.name_scope(self.patch_embeddings.name): self.patch_embeddings.build(None)
    def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
        batch_size, seq_len, dim = shape_list(embeddings)
        num_patches = seq_len - 1
        _, num_positions, _ = shape_list(self.position_embeddings)
        num_positions -= 1
        if num_patches == num_positions and height == width: return self.position_embeddings
        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]
        h0 = height // self.config.patch_size
        w0 = width // self.config.patch_size
        patch_pos_embed = tf.image.resize(images=tf.reshape(patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)), size=(h0, w0), method="bicubic")
        shape = shape_list(patch_pos_embed)
        assert h0 == shape[-3] and w0 == shape[-2]
        patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim))
        return tf.concat(values=(class_pos_embed, patch_pos_embed), axis=1)
    def call(self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False) -> tf.Tensor:
        batch_size, num_channels, height, width = shape_list(pixel_values)
        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, training=training)
        cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0)
        embeddings = tf.concat((cls_tokens, embeddings), axis=1)
        if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
        else: embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings, training=training)
        return embeddings
class TFViTPatchEmbeddings(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size
        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.num_channels = num_channels
        self.config = config
        self.projection = keras.layers.Conv2D(filters=hidden_size, kernel_size=patch_size, strides=patch_size, padding="valid", data_format="channels_last", use_bias=True,
        kernel_initializer=get_initializer(self.config.initializer_range), bias_initializer="zeros", name="projection")
    def call(self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False) -> tf.Tensor:
        batch_size, num_channels, height, width = 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.")
        if not interpolate_pos_encoding:
            if tf.executing_eagerly():
                if height != self.image_size[0] or width != self.image_size[1]: raise ValueError(f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]}).")
        pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
        projection = self.projection(pixel_values)
        num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
        embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1))
        return embeddings
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "projection", None) is not None:
            with tf.name_scope(self.projection.name): self.projection.build([None, None, None, self.num_channels])
class TFViTSelfAttention(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        if config.hidden_size % config.num_attention_heads != 0: raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
        self.query = keras.layers.Dense(units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query")
        self.key = keras.layers.Dense(units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key")
        self.value = keras.layers.Dense(units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value")
        self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
        self.config = config
    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
        return tf.transpose(tensor, perm=[0, 2, 1, 3])
    def call(self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False) -> Tuple[tf.Tensor]:
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(inputs=hidden_states)
        mixed_key_layer = self.key(inputs=hidden_states)
        mixed_value_layer = self.value(inputs=hidden_states)
        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
        attention_scores = tf.divide(attention_scores, dk)
        attention_probs = stable_softmax(logits=attention_scores, axis=-1)
        attention_probs = self.dropout(inputs=attention_probs, training=training)
        if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask)
        attention_output = tf.matmul(attention_probs, value_layer)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "query", None) is not None:
            with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size])
        if getattr(self, "key", None) is not None:
            with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size])
        if getattr(self, "value", None) is not None:
            with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size])
class TFViTSelfOutput(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.config = config
    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
class TFViTAttention(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.self_attention = TFViTSelfAttention(config, name="attention")
        self.dense_output = TFViTSelfOutput(config, name="output")
    def prune_heads(self, heads): raise NotImplementedError
    def call(self, input_tensor: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training)
        attention_output = self.dense_output(hidden_states=self_outputs[0], input_tensor=input_tensor, training=training)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "self_attention", None) is not None:
            with tf.name_scope(self.self_attention.name): self.self_attention.build(None)
        if getattr(self, "dense_output", None) is not None:
            with tf.name_scope(self.dense_output.name): self.dense_output.build(None)
class TFViTIntermediate(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else: self.intermediate_act_fn = config.hidden_act
        self.config = config
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
class TFViTOutput(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
        self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
        self.config = config
    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = hidden_states + input_tensor
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size])
class TFViTLayer(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFViTAttention(config, name="attention")
        self.intermediate = TFViTIntermediate(config, name="intermediate")
        self.vit_output = TFViTOutput(config, name="output")
        self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before")
        self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after")
        self.config = config
    def call(self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False) -> Tuple[tf.Tensor]:
        attention_outputs = self.attention(input_tensor=self.layernorm_before(inputs=hidden_states), head_mask=head_mask, output_attentions=output_attentions, training=training)
        attention_output = attention_outputs[0]
        hidden_states = attention_output + hidden_states
        layer_output = self.layernorm_after(inputs=hidden_states)
        intermediate_output = self.intermediate(hidden_states=layer_output)
        layer_output = self.vit_output(hidden_states=intermediate_output, input_tensor=hidden_states, training=training)
        outputs = (layer_output,) + attention_outputs[1:]
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "attention", None) is not None:
            with tf.name_scope(self.attention.name): self.attention.build(None)
        if getattr(self, "intermediate", None) is not None:
            with tf.name_scope(self.intermediate.name): self.intermediate.build(None)
        if getattr(self, "vit_output", None) is not None:
            with tf.name_scope(self.vit_output.name): self.vit_output.build(None)
        if getattr(self, "layernorm_before", None) is not None:
            with tf.name_scope(self.layernorm_before.name): self.layernorm_before.build([None, None, self.config.hidden_size])
        if getattr(self, "layernorm_after", None) is not None:
            with tf.name_scope(self.layernorm_after.name): self.layernorm_after.build([None, None, self.config.hidden_size])
class TFViTEncoder(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.layer = [TFViTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
    def call(self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_outputs = layer_module(hidden_states=hidden_states, head_mask=head_mask[i], output_attentions=output_attentions, training=training)
            hidden_states = layer_outputs[0]
            if output_attentions: all_attentions = all_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "layer", None) is not None:
            for layer in self.layer:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFViTMainLayer(keras.layers.Layer):
    config_class = ViTConfig
    def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.embeddings = TFViTEmbeddings(config, name="embeddings")
        self.encoder = TFViTEncoder(config, name="encoder")
        self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
        self.pooler = TFViTPooler(config, name="pooler") if add_pooling_layer else None
    def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings.patch_embeddings
    def _prune_heads(self, heads_to_prune): raise NotImplementedError
    @unpack_inputs
    def call(self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        if pixel_values is None: raise ValueError("You have to specify pixel_values")
        embedding_output = self.embeddings(pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, training=training)
        if head_mask is not None: raise NotImplementedError
        else: head_mask = [None] * self.config.num_hidden_layers
        encoder_outputs = self.encoder(hidden_states=embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(inputs=sequence_output)
        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return TFBaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embeddings", None) is not None:
            with tf.name_scope(self.embeddings.name): self.embeddings.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "layernorm", None) is not None:
            with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, self.config.hidden_size])
        if getattr(self, "pooler", None) is not None:
            with tf.name_scope(self.pooler.name): self.pooler.build(None)
class TFViTPreTrainedModel(TFPreTrainedModel):
    config_class = ViTConfig
    base_model_prefix = "vit"
    main_input_name = "pixel_values"
VIT_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>
    TensorFlow models and layers in `transformers` accept two formats as input:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.
    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:
    - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Args:
        config ([`ViTConfig`]): 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.
"""
VIT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`]
            for details.
        head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        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. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        interpolate_pos_encoding (`bool`, *optional*):
            Whether to interpolate the pre-trained position encodings.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False``):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""
@add_start_docstrings("The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", VIT_START_DOCSTRING)
class TFViTModel(TFViTPreTrainedModel):
    def __init__(self, config: ViTConfig, *inputs, add_pooling_layer=True, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.vit = TFViTMainLayer(config, add_pooling_layer=add_pooling_layer, name="vit")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
    def call(self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        outputs = self.vit(pixel_values=pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, training=training)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "vit", None) is not None:
            with tf.name_scope(self.vit.name): self.vit.build(None)
class TFViTPooler(keras.layers.Layer):
    def __init__(self, config: ViTConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = keras.layers.Dense(units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense")
        self.config = config
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)
        return pooled_output
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size])
@add_start_docstrings("""
    ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.
    <Tip>
        Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by
        setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
        position embeddings to the higher resolution.
    </Tip>
    """, VIT_START_DOCSTRING)
class TFViTForImageClassification(TFViTPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: ViTConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.vit = TFViTMainLayer(config, add_pooling_layer=False, name="vit")
        self.classifier = keras.layers.Dense(units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
    def call(self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        outputs = self.vit(pixel_values=pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, training=training)
        sequence_output = outputs[0]
        logits = self.classifier(inputs=sequence_output[:, 0, :])
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "vit", None) is not None:
            with tf.name_scope(self.vit.name): self.vit.build(None)
        if getattr(self, "classifier", None) is not None:
            with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size])
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
