"""
    #################################################################################################################################################################################################
    # 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
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling, FlaxSequenceClassifierOutput
from ...modeling_flax_utils import (ACT2FN, FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
from .configuration_dinov2 import Dinov2Config
DINOV2_START_DOCSTRING = r"""
    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
    This model is also a
    [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
    a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
    behavior.
    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
    Parameters:
        config ([`Dinov2Config`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).
            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.
            *Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.*
            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""
DINOV2_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`Dinov2ImageProcessor.__call__`]
            for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        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.
"""
class FlaxDinov2PatchEmbeddings(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        image_size = self.config.image_size
        patch_size = self.config.patch_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.num_patches = num_patches
        self.num_channels = self.config.num_channels
        self.projection = nn.Conv(self.config.hidden_size, kernel_size=patch_size, strides=patch_size, padding="VALID", dtype=self.dtype, 
        kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"))
    def __call__(self, pixel_values):
        num_channels = pixel_values.shape[-1]
        if 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.")
        embeddings = self.projection(pixel_values)
        batch_size, _, _, channels = embeddings.shape
        return jnp.reshape(embeddings, (batch_size, -1, channels))
class FlaxDinov2Embeddings(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.cls_token = self.param("cls_token", jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), (1, 1, self.config.hidden_size))
        self.mask_token = self.param("mask_token", jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), (1, self.config.hidden_size))
        self.patch_embeddings = FlaxDinov2PatchEmbeddings(self.config, dtype=self.dtype)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = self.param("position_embeddings", jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), (1, num_patches + 1, self.config.hidden_size))
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def interpolate_pos_encoding(self, config, hidden_states, height, width, position_embeddings):
        num_patches = hidden_states.shape[1] - 1
        num_positions = position_embeddings.shape[1] - 1
        if num_patches == num_positions and height == width: return position_embeddings
        class_pos_embed = position_embeddings[:, 0]
        patch_pos_embed = position_embeddings[:, 1:]
        dim = hidden_states.shape[-1]
        h = height // config.patch_size
        w = width // config.patch_size
        height, width = h + 0.1, w + 0.1
        patch_pos_embed = patch_pos_embed.reshape((1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim))
        patch_pos_embed = jnp.transpose(patch_pos_embed, (0, 3, 1, 2))
        target_dtype = patch_pos_embed.dtype
        new_height_ratio = jnp.float32(height / math.sqrt(num_positions))
        new_width_ratio = jnp.float32(width / math.sqrt(num_positions))
        scale = jnp.array([new_height_ratio, new_width_ratio], dtype=jnp.float32)
        translation = jnp.array([0.0, 0.0], dtype=jnp.float32)
        patch_pos_embed = jax.image.scale_and_translate(patch_pos_embed.astype(jnp.float32), shape=(patch_pos_embed.shape[0], patch_pos_embed.shape[1], h, w), spatial_dims=(2, 3),
        scale=scale, translation=translation, method="bicubic", antialias=False)
        patch_pos_embed = patch_pos_embed.astype(target_dtype)
        patch_pos_embed = jnp.transpose(patch_pos_embed, (0, 2, 3, 1)).reshape((hidden_states.shape[0], -1, dim))
        return jnp.concatenate((class_pos_embed[jnp.newaxis, :], patch_pos_embed), axis=1)
    def __call__(self, pixel_values, deterministic=True):
        batch_size = pixel_values.shape[0]
        target_dtype = self.patch_embeddings.projection.dtype
        height, width = pixel_values.shape[1], pixel_values.shape[2]
        embeddings = self.patch_embeddings(pixel_values.astype(target_dtype))
        cls_tokens = jnp.broadcast_to(self.cls_token, (batch_size, 1, self.config.hidden_size))
        embeddings = jnp.concatenate((cls_tokens, embeddings), axis=1)
        embeddings = embeddings + self.interpolate_pos_encoding(self.config, embeddings, height, width, self.position_embeddings)
        embeddings = self.dropout(embeddings, deterministic=deterministic)
        return embeddings
class FlaxDinov2SelfAttention(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError("`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`: {self.config.num_attention_heads}")
        self.query = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"), use_bias=self.config.qkv_bias)
        self.key = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"), use_bias=self.config.qkv_bias)
        self.value = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"), use_bias=self.config.qkv_bias)
    def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
        head_dim = self.config.hidden_size // self.config.num_attention_heads
        query_states = self.query(hidden_states).reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim))
        value_states = self.value(hidden_states).reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim))
        key_states = self.key(hidden_states).reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim))
        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout")
        attn_weights = dot_product_attention_weights(query_states, key_states, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob,
        broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None)
        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs
class FlaxDinov2SelfOutput(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states
class FlaxDinov2Attention(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.attention = FlaxDinov2SelfAttention(self.config, dtype=self.dtype)
        self.output = FlaxDinov2SelfOutput(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, deterministic=True, output_attentions: bool = False):
        attn_outputs = self.attention(hidden_states, deterministic=deterministic, output_attentions=output_attentions)
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_outputs[1],)
        return outputs
def ones_with_scale(key, shape, scale, dtype=jnp.float32): return jnp.ones(shape, dtype) * scale
class FlaxDinov2LayerScale(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.lambda1 = self.config.layerscale_value * self.param("lambda1", jax.nn.initializers.ones, (self.config.hidden_size,))
        self.lambda1 = self.lambda1 * self.config.layerscale_value
    def __call__(self, hidden_states): return self.lambda1 * hidden_states
class FlaxDinov2DropPath(nn.Module):
    rate: float
    @nn.module.compact
    def __call__(self, inputs, deterministic: Optional[bool] = True):
        if self.rate == 0.0: return inputs
        keep_prob = 1.0 - self.rate
        if deterministic: return inputs
        else:
            shape = (inputs.shape[0],) + (1,) * (inputs.ndim - 1)
            rng = self.make_rng("droppath")
            random_tensor = keep_prob + jax.random.uniform(rng, shape=shape, dtype=inputs.dtype)
            binary_tensor = jnp.floor(random_tensor)
            output = inputs / keep_prob * binary_tensor
            return output
class FlaxDinov2MLP(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.fc1 = nn.Dense(self.config.hidden_size * self.config.mlp_ratio, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), dtype=self.dtype)
        self.fc2 = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), dtype=self.dtype)
        if isinstance(self.config.hidden_act, str): self.act = ACT2FN[self.config.hidden_act]
        else: self.act = self.config.hidden_act
    def __call__(self, hidden_states):
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states
class FlaxDinov2SwiGLUFFN(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        hidden_features = int(self.config.hidden_size * self.config.mlp_ratio)
        hidden_features = (int(self.hidden_features * 2 / 3) + 7) // 8 * 8
        self.weights_in = nn.Dense(2 * hidden_features, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), dtype=self.dtype)
        self.weights_out = nn.Dense(self.config.hidden_size, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), dtype=self.dtype)
    def __call__(self, hidden_states):
        hidden_states = self.weights_in(hidden_states)
        x1, x2 = jnp.split(hidden_states, 2, axis=-1)
        hidden = nn.silu(x1) * x2
        return self.weights_out(hidden)
class FlaxDinov2Layer(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.norm1 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.attention = FlaxDinov2Attention(self.config, dtype=self.dtype)
        self.layer_scale1 = FlaxDinov2LayerScale(self.config, dtype=self.dtype)
        self.drop_path = FlaxDinov2DropPath(self.config.drop_path_rate)
        self.norm2 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        if self.config.use_swiglu_ffn: self.mlp = FlaxDinov2SwiGLUFFN(self.config, dtype=self.dtype)
        else: self.mlp = FlaxDinov2MLP(self.config, dtype=self.dtype)
        self.layer_scale2 = FlaxDinov2LayerScale(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
        self_attention_outputs = self.attention(self.norm1(hidden_states), deterministic=deterministic, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        attention_output = self.layer_scale1(attention_output)
        outputs = self_attention_outputs[1:]
        hidden_states = self.drop_path(attention_output) + hidden_states
        layer_output = self.norm2(hidden_states)
        layer_output = self.mlp(layer_output)
        layer_output = self.layer_scale2(layer_output)
        layer_output = self.drop_path(layer_output) + hidden_states
        outputs = (layer_output,) + outputs
        return outputs
class FlaxDinov2LayerCollection(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layers = [FlaxDinov2Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)]
    def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            layer_outputs = layer(hidden_states, deterministic=deterministic, output_attentions=output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_attentions += (layer_outputs[1],)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        outputs = (hidden_states,)
        if not return_dict: return tuple(v for v in outputs if v is not None)
        return FlaxBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions)
class FlaxDinov2Encoder(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.layer = FlaxDinov2LayerCollection(self.config, dtype=self.dtype)
    def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True): return self.layer(hidden_states, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
class FlaxDinov2PreTrainedModel(FlaxPreTrainedModel):
    config_class = Dinov2Config
    base_model_prefix = "dinov2"
    main_input_name = "pixel_values"
    module_class: nn.Module = None
    def __init__(self, config: Dinov2Config, input_shape=None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        if input_shape is None: input_shape = (1, config.image_size, config.image_size, config.num_channels)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
        params_rng, dropout_rng = jax.random.split(rng)
        dropout_rng, droppath_rng = jax.random.split(dropout_rng)
        rngs = {"params": params_rng, "dropout": dropout_rng, "droppath": droppath_rng}
        random_params = self.module.init(rngs, pixel_values, return_dict=False)["params"]
        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else: return random_params
    @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        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.return_dict
        pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
        rngs = {}
        if dropout_rng is not None:
            dropout_rng, droppath_rng = jax.random.split(dropout_rng)
            rngs["dropout"] = dropout_rng
            rngs["droppath"] = droppath_rng
        return self.module.apply({"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs)
class FlaxDinov2Module(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.embeddings = FlaxDinov2Embeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxDinov2Encoder(self.config, dtype=self.dtype)
        self.layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
    def __call__(self, pixel_values, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True):
        hidden_states = self.embeddings(pixel_values, deterministic=deterministic)
        encoder_outputs = self.encoder(hidden_states, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        pooled_output = sequence_output[:, 0, :]
        if not return_dict:
            head_outputs = (sequence_output, pooled_output)
            return head_outputs + encoder_outputs[1:]
        return FlaxBaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
@add_start_docstrings("The bare Dinov2 Model transformer outputting raw hidden-states without any specific head on top.", DINOV2_START_DOCSTRING)
class FlaxDinov2Model(FlaxDinov2PreTrainedModel): module_class = FlaxDinov2Module
FLAX_VISION_MODEL_DOCSTRING = """
    Returns:
    Examples:
    ```python
    >>> from sapiens_transformers import AutoImageProcessor, FlaxDinov2Model
    >>> from PIL import Image
    >>> import requests
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
    >>> model = FlaxDinov2Model.from_pretrained("facebook/dinov2-base")
    >>> inputs = image_processor(images=image, return_tensors="np")
    >>> outputs = model(**inputs)
    >>> last_hidden_states = outputs.last_hidden_state
    ```
"""
overwrite_call_docstring(FlaxDinov2Model, FLAX_VISION_MODEL_DOCSTRING)
append_replace_return_docstrings(FlaxDinov2Model, output_type=FlaxBaseModelOutputWithPooling, config_class=Dinov2Config)
class FlaxDinov2ForImageClassificationModule(nn.Module):
    config: Dinov2Config
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.dinov2 = FlaxDinov2Module(config=self.config, dtype=self.dtype)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"))
    def __call__(self, pixel_values=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.dinov2(pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        cls_token = hidden_states[:, 0]
        patch_tokens = hidden_states[:, 1:]
        linear_input = jnp.concatenate([cls_token, patch_tokens.mean(axis=1)], axis=-1)
        logits = self.classifier(linear_input)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return output
        return FlaxSequenceClassifierOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("Dinov2 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.", DINOV2_START_DOCSTRING)
class FlaxDinov2ForImageClassification(FlaxDinov2PreTrainedModel): module_class = FlaxDinov2ForImageClassificationModule
FLAX_VISION_CLASSIFICATION_DOCSTRING = """
    Returns:
    Example:
    ```python
    >>> from sapiens_transformers import AutoImageProcessor, FlaxDinov2ForImageClassification
    >>> from PIL import Image
    >>> import jax
    >>> import requests
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base-imagenet1k-1-layer")
    >>> model = FlaxDinov2ForImageClassification.from_pretrained("facebook/dinov2-base-imagenet1k-1-layer")
    >>> inputs = image_processor(images=image, return_tensors="np")
    >>> outputs = model(**inputs)
    >>> logits = outputs.logits
    >>> # model predicts one of the 1000 ImageNet classes
    >>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
    >>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
    ```
"""
overwrite_call_docstring(FlaxDinov2ForImageClassification, FLAX_VISION_CLASSIFICATION_DOCSTRING)
append_replace_return_docstrings(FlaxDinov2ForImageClassification, output_type=FlaxSequenceClassifierOutput, config_class=Dinov2Config)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
