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 copy import deepcopy
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings)
from ...modeling_tf_outputs import TFBaseModelOutput
from ...modeling_tf_utils import (TFModelInputType, TFPreTrainedModel, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import shape_list, stable_softmax
from ...utils import logging
from .configuration_vit_mae import ViTMAEConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ViTMAEConfig"
_CHECKPOINT_FOR_DOC = "facebook/vit-mae-base"
@dataclass
class TFViTMAEModelOutput(ModelOutput):
    """Args:"""
    last_hidden_state: tf.Tensor = None
    mask: tf.Tensor = None
    ids_restore: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None
@dataclass
class TFViTMAEDecoderOutput(ModelOutput):
    """Args:"""
    logits: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None
@dataclass
class TFViTMAEForPreTrainingOutput(ModelOutput):
    """Args:"""
    loss: tf.Tensor | None = None
    logits: tf.Tensor = None
    mask: tf.Tensor = None
    ids_restore: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None
def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False):
    grid_h = tf.range(grid_size, dtype=tf.float32)
    grid_w = tf.range(grid_size, dtype=tf.float32)
    grid = tf.meshgrid(grid_w, grid_h)
    grid = tf.stack(grid, axis=0)
    grid = tf.reshape(grid, [2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if add_cls_token: pos_embed = tf.concat([tf.zeros((1, embed_dim)), pos_embed], axis=0)
    return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    if embed_dim % 2 != 0: raise ValueError("embed_dim must be even")
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])
    emb = tf.concat([emb_h, emb_w], axis=1)
    return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    if embed_dim % 2 != 0: raise ValueError("embed_dim must be even")
    omega = tf.range(embed_dim // 2, dtype="float32")
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega
    pos = tf.reshape(pos, [-1])
    out = tf.einsum("m,d->md", pos, omega)
    emb_sin = tf.sin(out)
    emb_cos = tf.cos(out)
    emb = tf.concat([emb_sin, emb_cos], axis=1)
    return emb
class TFViTMAEEmbeddings(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **kwargs):
        super().__init__(**kwargs)
        self.patch_embeddings = TFViTMAEPatchEmbeddings(config, name="patch_embeddings")
        self.num_patches = self.patch_embeddings.num_patches
        self.config = config
    def build(self, input_shape=None):
        self.cls_token = self.add_weight(shape=(1, 1, self.config.hidden_size), initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), trainable=True, name="cls_token")
        self.position_embeddings = self.add_weight(shape=(1, self.num_patches + 1, self.config.hidden_size), initializer="zeros", trainable=False, name="position_embeddings")
        pos_embed = get_2d_sincos_pos_embed(self.position_embeddings.shape[-1], int(self.patch_embeddings.num_patches**0.5), add_cls_token=True)[None, ...]
        self.position_embeddings.assign(pos_embed)
        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")
        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 random_masking(self, sequence: tf.Tensor, noise: tf.Tensor | None = None):
        batch_size, seq_length, dim = shape_list(sequence)
        len_keep = int(seq_length * (1 - self.config.mask_ratio))
        if noise is None: noise = tf.random.uniform(shape=(batch_size, seq_length), minval=0.0, maxval=1.0)
        ids_shuffle = tf.argsort(noise, axis=1)
        ids_restore = tf.argsort(ids_shuffle, axis=1)
        ids_keep = ids_shuffle[:, :len_keep]
        sequence_unmasked = tf.gather(sequence, axis=1, batch_dims=1, indices=ids_keep)
        mask_keep = tf.zeros((batch_size, len_keep))
        mask_remove = tf.ones((batch_size, seq_length - len_keep))
        mask = tf.concat([mask_keep, mask_remove], axis=-1)
        mask = tf.gather(mask, axis=1, batch_dims=1, indices=ids_restore)
        return sequence_unmasked, mask, ids_restore
    def call(self, pixel_values: tf.Tensor, noise: tf.Tensor = None, interpolate_pos_encoding: 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)
        if interpolate_pos_encoding: position_embeddings = self.interpolate_pos_encoding(embeddings, height, width)
        else: position_embeddings = self.position_embeddings
        embeddings = embeddings + position_embeddings[:, 1:, :]
        embeddings, mask, ids_restore = self.random_masking(embeddings, noise)
        cls_token = self.cls_token + position_embeddings[:, :1, :]
        cls_tokens = tf.tile(cls_token, (shape_list(embeddings)[0], 1, 1))
        embeddings = tf.concat([cls_tokens, embeddings], axis=1)
        return embeddings, mask, ids_restore
class TFViTMAEPatchEmbeddings(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **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",
        kernel_initializer="glorot_uniform", bias_initializer="zeros", name="projection")
    def call(self, pixel_values: tf.Tensor, training: bool = False, interpolate_pos_encoding: bool = False) -> tf.Tensor:
        batch_size, num_channels, height, width = shape_list(pixel_values)
        if tf.executing_eagerly():
            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.")
            if not interpolate_pos_encoding and (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])
        x = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1))
        return x
    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 TFViTMAESelfAttention(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **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 TFViTMAESelfOutput(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **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 TFViTMAEAttention(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **kwargs):
        super().__init__(**kwargs)
        self.self_attention = TFViTMAESelfAttention(config, name="attention")
        self.dense_output = TFViTMAESelfOutput(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 TFViTMAEIntermediate(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **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 TFViTMAEOutput(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **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 TFViTMAELayer(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFViTMAEAttention(config, name="attention")
        self.intermediate = TFViTMAEIntermediate(config, name="intermediate")
        self.vit_output = TFViTMAEOutput(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 TFViTMAEEncoder(keras.layers.Layer):
    def __init__(self, config: ViTMAEConfig, **kwargs):
        super().__init__(**kwargs)
        self.layer = [TFViTMAELayer(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 TFViTMAEMainLayer(keras.layers.Layer):
    config_class = ViTMAEConfig
    def __init__(self, config: ViTMAEConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.embeddings = TFViTMAEEmbeddings(config, name="embeddings")
        self.encoder = TFViTMAEEncoder(config, name="encoder")
        self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
    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, noise: tf.Tensor = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, interpolate_pos_encoding: bool = False) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]:
        embedding_output, mask, ids_restore = self.embeddings(pixel_values=pixel_values, training=training, noise=noise, interpolate_pos_encoding=interpolate_pos_encoding)
        if head_mask is not None: raise NotImplementedError
        else: head_mask = [None] * self.config.num_hidden_layers
        encoder_outputs = self.encoder(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)
        if not return_dict: return (sequence_output, mask, ids_restore) + encoder_outputs[1:]
        return TFViTMAEModelOutput(last_hidden_state=sequence_output, mask=mask, ids_restore=ids_restore, 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])
class TFViTMAEPreTrainedModel(TFPreTrainedModel):
    config_class = ViTMAEConfig
    base_model_prefix = "vit"
    main_input_name = "pixel_values"
VIT_MAE_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 ([`ViTMAEConfig`]): 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_MAE_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.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_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).
        interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
            Whether to interpolate the position encodings at the encoder and decoder.
"""
@add_start_docstrings("The bare ViTMAE Model transformer outputting raw hidden-states without any specific head on top.", VIT_MAE_START_DOCSTRING)
class TFViTMAEModel(TFViTMAEPreTrainedModel):
    def __init__(self, config: ViTMAEConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.vit = TFViTMAEMainLayer(config, name="vit")
    def get_input_embeddings(self): return self.vit.get_input_embeddings()
    @unpack_inputs
    @add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
    def call(self, pixel_values: TFModelInputType | None = None, noise: tf.Tensor = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, interpolate_pos_encoding: bool = False) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]:
        outputs = self.vit(pixel_values=pixel_values, noise=noise, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training, interpolate_pos_encoding=interpolate_pos_encoding)
        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 TFViTMAEDecoder(keras.layers.Layer):
    def __init__(self, config, num_patches, **kwargs):
        super().__init__(**kwargs)
        self.decoder_embed = keras.layers.Dense(config.decoder_hidden_size, name="decoder_embed")
        decoder_config = deepcopy(config)
        decoder_config.hidden_size = config.decoder_hidden_size
        decoder_config.num_hidden_layers = config.decoder_num_hidden_layers
        decoder_config.num_attention_heads = config.decoder_num_attention_heads
        decoder_config.intermediate_size = config.decoder_intermediate_size
        self.decoder_layers = [TFViTMAELayer(decoder_config, name=f"decoder_layers.{j}") for j in range(config.decoder_num_hidden_layers)]
        self.decoder_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="decoder_norm")
        self.decoder_pred = keras.layers.Dense(config.patch_size**2 * config.num_channels, kernel_initializer=get_initializer(config.initializer_range), name="decoder_pred")
        self.config = config
        self.num_patches = num_patches
    def build(self, input_shape=None):
        self.mask_token = self.add_weight(shape=(1, 1, self.config.decoder_hidden_size), initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), trainable=True, name="mask_token")
        self.decoder_pos_embed = self.add_weight(shape=(1, self.num_patches + 1, self.config.decoder_hidden_size), initializer="zeros", trainable=False, name="decoder_pos_embed")
        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.num_patches**0.5), add_cls_token=True)[None, ...]
        self.decoder_pos_embed.assign(decoder_pos_embed)
        if self.built: return
        self.built = True
        if getattr(self, "decoder_embed", None) is not None:
            with tf.name_scope(self.decoder_embed.name): self.decoder_embed.build([None, None, self.config.hidden_size])
        if getattr(self, "decoder_norm", None) is not None:
            with tf.name_scope(self.decoder_norm.name): self.decoder_norm.build([None, None, self.config.decoder_hidden_size])
        if getattr(self, "decoder_pred", None) is not None:
            with tf.name_scope(self.decoder_pred.name): self.decoder_pred.build([None, None, self.config.decoder_hidden_size])
        if getattr(self, "decoder_layers", None) is not None:
            for layer in self.decoder_layers:
                with tf.name_scope(layer.name): layer.build(None)
    def interpolate_pos_encoding(self, embeddings) -> tf.Tensor:
        _, num_positions, dim = shape_list(self.decoder_pos_embed)
        seq_len = shape_list(embeddings)[1] - 1
        num_positions = num_positions - 1
        class_pos_embed = self.decoder_pos_embed[:, :1, :]
        patch_pos_embed = self.decoder_pos_embed[:, 1:, :]
        patch_pos_embed = tf.image.resize(images=tf.reshape(patch_pos_embed, shape=(1, 1, -1, dim)), size=(1, seq_len), method="bicubic")
        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, hidden_states, ids_restore, output_attentions=False, output_hidden_states=False, return_dict=True, interpolate_pos_encoding=False):
        x = self.decoder_embed(hidden_states)
        mask_tokens = tf.tile(self.mask_token, (shape_list(x)[0], shape_list(ids_restore)[1] + 1 - shape_list(x)[1], 1))
        x_ = tf.concat([x[:, 1:, :], mask_tokens], axis=1)
        x_ = tf.gather(x_, axis=1, batch_dims=1, indices=ids_restore)
        x = tf.concat([x[:, :1, :], x_], axis=1)
        if interpolate_pos_encoding: decoder_pos_embed = self.interpolate_pos_encoding(x)
        else: decoder_pos_embed = self.decoder_pos_embed
        hidden_states = x + decoder_pos_embed
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.decoder_layers):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        hidden_states = self.decoder_norm(hidden_states)
        logits = self.decoder_pred(hidden_states)
        logits = logits[:, 1:, :]
        if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None)
        return TFViTMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions)
@add_start_docstrings("The ViTMAE Model transformer with the decoder on top for self-supervised pre-training.", VIT_MAE_START_DOCSTRING)
class TFViTMAEForPreTraining(TFViTMAEPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.vit = TFViTMAEMainLayer(config, name="vit")
        self.decoder = TFViTMAEDecoder(config, num_patches=self.vit.embeddings.num_patches, name="decoder")
    def get_input_embeddings(self): return self.vit.get_input_embeddings()
    def _prune_heads(self, heads_to_prune): raise NotImplementedError
    def patchify(self, pixel_values, interpolate_pos_encoding: bool = False):
        patch_size, num_channels = self.config.patch_size, self.config.num_channels
        if shape_list(pixel_values)[1] == num_channels: pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
        if not interpolate_pos_encoding: tf.debugging.assert_equal(shape_list(pixel_values)[1], shape_list(pixel_values)[2], message="Make sure the pixel values have a squared size")
        tf.debugging.assert_equal(shape_list(pixel_values)[1] % patch_size, 0, message="Make sure the pixel values have a size that is divisible by the patch size")
        tf.debugging.assert_equal(shape_list(pixel_values)[3], num_channels, message=("Make sure the number of channels of the pixel values is equal to the one set in the configuration"))
        batch_size = shape_list(pixel_values)[0]
        num_patches_h = shape_list(pixel_values)[1] // patch_size
        num_patches_w = shape_list(pixel_values)[2] // patch_size
        patchified_pixel_values = tf.reshape(pixel_values, (batch_size, num_patches_h, patch_size, num_patches_w, patch_size, num_channels))
        patchified_pixel_values = tf.einsum("nhpwqc->nhwpqc", patchified_pixel_values)
        patchified_pixel_values = tf.reshape(patchified_pixel_values, (batch_size, num_patches_h * num_patches_w, patch_size**2 * num_channels))
        return patchified_pixel_values
    def unpatchify(self, patchified_pixel_values, original_image_size: Optional[Tuple[int, int]] = None):
        patch_size, num_channels = self.config.patch_size, self.config.num_channels
        original_image_size = (original_image_size if original_image_size is not None else (self.config.image_size, self.config.image_size))
        original_height, original_width = original_image_size
        num_patches_h = original_height // patch_size
        num_patches_w = original_width // patch_size
        tf.debugging.assert_equal(num_patches_h * num_patches_w, shape_list(patchified_pixel_values)[1], message=f"The number of patches in the patchified pixel values is {shape_list(patchified_pixel_values)[1]} does not match the patches of original image {num_patches_w}*{num_patches_h}")
        batch_size = shape_list(patchified_pixel_values)[0]
        patchified_pixel_values = tf.reshape(patchified_pixel_values, (batch_size, num_patches_h, num_patches_w, patch_size, patch_size, num_channels))
        patchified_pixel_values = tf.einsum("nhwpqc->nhpwqc", patchified_pixel_values)
        pixel_values = tf.reshape(patchified_pixel_values, (batch_size, num_patches_h * patch_size, num_patches_w * patch_size, num_channels))
        return pixel_values
    def forward_loss(self, pixel_values, pred, mask, interpolate_pos_encoding: bool = False):
        target = self.patchify(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        if self.config.norm_pix_loss:
            mean = tf.reduce_mean(target, axis=-1, keepdims=True)
            var = tf.math.reduce_variance(target, axis=-1, keepdims=True)
            target = (target - mean) / (var + 1.0e-6) ** 0.5
        loss = (pred - target) ** 2
        loss = tf.reduce_mean(loss, axis=-1)
        loss = tf.reduce_sum(loss * mask) / tf.reduce_sum(mask)
        loss = tf.reshape(loss, (1,))
        return loss
    @unpack_inputs
    @add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING)
    def call(self, pixel_values: TFModelInputType | None = None, noise: tf.Tensor = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, interpolate_pos_encoding: bool = False) -> Union[TFViTMAEForPreTrainingOutput, Tuple[tf.Tensor]]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.vit(pixel_values=pixel_values, noise=noise, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training, interpolate_pos_encoding=interpolate_pos_encoding)
        latent = outputs.last_hidden_state
        ids_restore = outputs.ids_restore
        mask = outputs.mask
        decoder_outputs = self.decoder(latent, ids_restore, interpolate_pos_encoding=interpolate_pos_encoding)
        logits = decoder_outputs.logits
        loss = self.forward_loss(pixel_values, logits, mask, interpolate_pos_encoding=interpolate_pos_encoding)
        if not return_dict:
            output = (logits, mask, ids_restore) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFViTMAEForPreTrainingOutput(loss=loss, logits=logits, mask=mask, ids_restore=ids_restore, 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, "decoder", None) is not None:
            with tf.name_scope(self.decoder.name): self.decoder.build(None)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
