# coding=utf-8
# Copyright 2022 Facebook AI Research (FAIR) and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TensorFlow DeiT model."""


from __future__ import annotations

import collections.abc
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
    TFBaseModelOutput,
    TFBaseModelOutputWithPooling,
    TFImageClassifierOutput,
    TFMaskedImageModelingOutput,
)
from ...modeling_tf_utils import (
    TFPreTrainedModel,
    TFSequenceClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_deit import DeiTConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "DeiTConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224"
_EXPECTED_OUTPUT_SHAPE = [1, 198, 768]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/deit-base-distilled-patch16-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/deit-base-distilled-patch16-224",
    # See all DeiT models at https://huggingface.co/models?filter=deit
]


@dataclass
class TFDeiTForImageClassificationWithTeacherOutput(ModelOutput):
    """
    Output type of [`DeiTForImageClassificationWithTeacher`].

    Args:
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores as the average of the cls_logits and distillation logits.
        cls_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
            class token).
        distillation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
            distillation token).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus
            the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
            the self-attention heads.
    """

    logits: tf.Tensor = None
    cls_logits: tf.Tensor = None
    distillation_logits: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


class TFDeiTEmbeddings(tf.keras.layers.Layer):
    """
    Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token.
    """

    def __init__(self, config: DeiTConfig, use_mask_token: bool = False, **kwargs) -> None:
        super().__init__(**kwargs)
        self.config = config
        self.use_mask_token = use_mask_token
        self.patch_embeddings = TFDeiTPatchEmbeddings(config=config, name="patch_embeddings")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")

    def build(self, input_shape: tf.TensorShape):
        self.cls_token = self.add_weight(
            shape=(1, 1, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="cls_token",
        )
        self.distillation_token = self.add_weight(
            shape=(1, 1, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="distillation_token",
        )
        self.mask_token = None
        if self.use_mask_token:
            self.mask_token = self.add_weight(
                shape=(1, 1, self.config.hidden_size),
                initializer=tf.keras.initializers.zeros(),
                trainable=True,
                name="mask_token",
            )
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = self.add_weight(
            shape=(1, num_patches + 2, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="position_embeddings",
        )
        super().build(input_shape)

    def call(
        self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None, training: bool = False
    ) -> tf.Tensor:
        embeddings = self.patch_embeddings(pixel_values)
        batch_size, seq_length, _ = shape_list(embeddings)

        if bool_masked_pos is not None:
            mask_tokens = tf.tile(self.mask_token, [batch_size, seq_length, 1])
            # replace the masked visual tokens by mask_tokens
            mask = tf.expand_dims(bool_masked_pos, axis=-1)
            mask = tf.cast(mask, dtype=mask_tokens.dtype)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0)
        distillation_tokens = tf.repeat(self.distillation_token, repeats=batch_size, axis=0)
        embeddings = tf.concat((cls_tokens, distillation_tokens, embeddings), axis=1)
        embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings, training=training)
        return embeddings


class TFDeiTPatchEmbeddings(tf.keras.layers.Layer):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config: DeiTConfig, **kwargs) -> None:
        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_channels = num_channels
        self.num_patches = num_patches

        self.projection = tf.keras.layers.Conv2D(
            hidden_size, kernel_size=patch_size, strides=patch_size, name="projection"
        )

    def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
        batch_size, height, width, num_channels = shape_list(pixel_values)
        if tf.executing_eagerly() and num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if tf.executing_eagerly() 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]})."
            )
        x = self.projection(pixel_values)
        batch_size, height, width, num_channels = shape_list(x)
        x = tf.reshape(x, (batch_size, height * width, num_channels))
        return x


# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->DeiT
class TFDeiTSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **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 "
                f"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 = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)

    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))

        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, 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)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        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)

        # Normalize the attention scores to probabilities.
        attention_probs = stable_softmax(logits=attention_scores, axis=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(inputs=attention_probs, training=training)

        # Mask heads if we want to
        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])

        # (batch_size, seq_len_q, all_head_size)
        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


# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->DeiT
class TFDeiTSelfOutput(tf.keras.layers.Layer):
    """
    The residual connection is defined in TFDeiTLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    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


# Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->DeiT
class TFDeiTAttention(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.self_attention = TFDeiTSelfAttention(config, name="attention")
        self.dense_output = TFDeiTSelfOutput(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:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->DeiT
class TFDeiTIntermediate(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.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

    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


# Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->DeiT
class TFDeiTOutput(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    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


class TFDeiTLayer(tf.keras.layers.Layer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.attention = TFDeiTAttention(config, name="attention")
        self.intermediate = TFDeiTIntermediate(config, name="intermediate")
        self.deit_output = TFDeiTOutput(config, name="output")

        self.layernorm_before = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_before"
        )
        self.layernorm_after = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_after"
        )

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        attention_outputs = self.attention(
            # in DeiT, layernorm is applied before self-attention
            input_tensor=self.layernorm_before(inputs=hidden_states, training=training),
            head_mask=head_mask,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = attention_outputs[0]

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in DeiT, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(inputs=hidden_states, training=training)

        intermediate_output = self.intermediate(hidden_states=layer_output, training=training)

        # second residual connection is done here
        layer_output = self.deit_output(
            hidden_states=intermediate_output, input_tensor=hidden_states, training=training
        )
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->DeiT
class TFDeiTEncoder(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.layer = [TFDeiTLayer(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],)

        # Add last layer
        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
        )


@keras_serializable
class TFDeiTMainLayer(tf.keras.layers.Layer):
    config_class = DeiTConfig

    def __init__(
        self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.config = config

        self.embeddings = TFDeiTEmbeddings(config, use_mask_token=use_mask_token, name="embeddings")
        self.encoder = TFDeiTEncoder(config, name="encoder")

        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
        self.pooler = TFDeiTPooler(config, name="pooler") if add_pooling_layer else None

    def get_input_embeddings(self) -> TFDeiTPatchEmbeddings:
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        raise NotImplementedError

    def get_head_mask(self, head_mask):
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        return head_mask

    @unpack_inputs
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor, ...]]:
        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.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        # TF 2.0 image layers can't use NCHW format when running on CPU.
        # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
        pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1))

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask)

        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos, training=training)

        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(sequence_output, training=training)
        pooled_output = self.pooler(sequence_output, training=training) if self.pooler is not None else None

        if not return_dict:
            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
            return head_outputs + encoder_outputs[1:]

        return TFBaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


# Copied from transformers.models.vit.modeling_tf_vit.TFViTPreTrainedModel with ViT->DeiT all-casing
class TFDeiTPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DeiTConfig
    base_model_prefix = "deit"
    main_input_name = "pixel_values"


DEIT_START_DOCSTRING = r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular
    TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.

    Parameters:
        config ([`DeiTConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

DEIT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`DeiTImageProcessor.__call__`] for details.

        head_mask (`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.
        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.
"""


@add_start_docstrings(
    "The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.",
    DEIT_START_DOCSTRING,
)
class TFDeiTModel(TFDeiTPreTrainedModel):
    def __init__(
        self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(config, **kwargs)

        self.deit = TFDeiTMainLayer(
            config, add_pooling_layer=add_pooling_layer, use_mask_token=use_mask_token, name="deit"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[Tuple, TFBaseModelOutputWithPooling]:
        outputs = self.deit(
            pixel_values=pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTPooler with ViT->DeiT
class TFDeiTPooler(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="tanh",
            name="dense",
        )

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)

        return pooled_output


class TFDeitPixelShuffle(tf.keras.layers.Layer):
    """TF layer implementation of torch.nn.PixelShuffle"""

    def __init__(self, upscale_factor: int, **kwargs) -> None:
        super().__init__(**kwargs)
        if not isinstance(upscale_factor, int) or upscale_factor < 2:
            raise ValueError(f"upscale_factor must be an integer value >= 2 got {upscale_factor}")
        self.upscale_factor = upscale_factor

    def call(self, x: tf.Tensor) -> tf.Tensor:
        hidden_states = x
        batch_size, _, _, num_input_channels = shape_list(hidden_states)
        block_size_squared = self.upscale_factor**2
        output_depth = int(num_input_channels / block_size_squared)
        # When the number of output channels >= 2, PyTorch's PixelShuffle and
        # TF's depth_to_space differ in their output as the order of channels selected for combining
        # is a permutation of the other c.f.
        # https://stackoverflow.com/questions/68272502/tf-depth-to-space-not-same-as-torchs-pixelshuffle-when-output-channels-1
        permutation = tf.constant(
            [[i + j * block_size_squared for i in range(block_size_squared) for j in range(output_depth)]]
        )
        hidden_states = tf.gather(params=hidden_states, indices=tf.tile(permutation, [batch_size, 1]), batch_dims=-1)
        hidden_states = tf.nn.depth_to_space(hidden_states, block_size=self.upscale_factor, data_format="NHWC")
        return hidden_states


class TFDeitDecoder(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.conv2d = tf.keras.layers.Conv2D(
            filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, name="0"
        )
        self.pixel_shuffle = TFDeitPixelShuffle(config.encoder_stride, name="1")

    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = inputs
        hidden_states = self.conv2d(hidden_states)
        hidden_states = self.pixel_shuffle(hidden_states)
        return hidden_states


@add_start_docstrings(
    "DeiT Model with a decoder on top for masked image modeling, as proposed in"
    " [SimMIM](https://arxiv.org/abs/2111.09886).",
    DEIT_START_DOCSTRING,
)
class TFDeiTForMaskedImageModeling(TFDeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="deit")
        self.decoder = TFDeitDecoder(config, name="decoder")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFMaskedImageModelingOutput]:
        r"""
        bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).

        Returns:

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling
        >>> import tensorflow as tf
        >>> 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/deit-base-distilled-patch16-224")
        >>> model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool)

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 224, 224]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        # Reshape to (batch_size, num_channels, height, width)
        sequence_output = sequence_output[:, 1:-1]
        batch_size, sequence_length, num_channels = shape_list(sequence_output)
        height = width = int(sequence_length**0.5)
        sequence_output = tf.reshape(sequence_output, (batch_size, height, width, num_channels))

        # Reconstruct pixel values
        reconstructed_pixel_values = self.decoder(sequence_output, training=training)
        # TF 2.0 image layers can't use NCHW format when running on CPU, so intermediate layers use NHWC,
        # including the The decoder. We transpose to compute the loss against the pixel values
        # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
        reconstructed_pixel_values = tf.transpose(reconstructed_pixel_values, (0, 3, 1, 2))

        masked_im_loss = None
        if bool_masked_pos is not None:
            size = self.config.image_size // self.config.patch_size
            bool_masked_pos = tf.reshape(bool_masked_pos, (-1, size, size))
            mask = tf.repeat(bool_masked_pos, self.config.patch_size, 1)
            mask = tf.repeat(mask, self.config.patch_size, 2)
            mask = tf.expand_dims(mask, 1)
            mask = tf.cast(mask, tf.float32)

            reconstruction_loss = tf.keras.losses.mean_absolute_error(
                # Swap axes as metric calculation reduces over the final dimension
                tf.transpose(pixel_values, (1, 2, 3, 0)),
                tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)),
            )
            reconstruction_loss = tf.expand_dims(reconstruction_loss, 0)
            total_loss = tf.reduce_sum(reconstruction_loss * mask)
            num_masked_pixels = (tf.reduce_sum(mask) + 1e-5) * self.config.num_channels
            masked_im_loss = total_loss / num_masked_pixels
            masked_im_loss = tf.reshape(masked_im_loss, (1,))

        if not return_dict:
            output = (reconstructed_pixel_values,) + outputs[1:]
            return ((masked_im_loss,) + output) if masked_im_loss is not None else output

        return TFMaskedImageModelingOutput(
            loss=masked_im_loss,
            reconstruction=reconstructed_pixel_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DeiT 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.
    """,
    DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassification(TFDeiTPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: DeiTConfig):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")

        # Classifier head
        self.classifier = (
            tf.keras.layers.Dense(config.num_labels, name="classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="classifier")
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        labels: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tf.Tensor, TFImageClassifierOutput]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFDeiTForImageClassification
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> tf.keras.utils.set_random_seed(3)  # doctest: +IGNORE_RESULT
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here,
        >>> # so the head will be randomly initialized, hence the predictions will be random
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
        >>> model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> # model predicts one of the 1000 ImageNet classes
        >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
        >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
        Predicted class: little blue heron, Egretta caerulea
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        logits = self.classifier(sequence_output[:, 0, :])
        # we don't use the distillation token

        loss = None if labels is None else self.hf_compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return TFImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
    the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

    .. warning::

            This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
            supported.
    """,
    DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassificationWithTeacher(TFDeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")

        # Classifier heads
        self.cls_classifier = (
            tf.keras.layers.Dense(config.num_labels, name="cls_classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="cls_classifier")
        )
        self.distillation_classifier = (
            tf.keras.layers.Dense(config.num_labels, name="distillation_classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="distillation_classifier")
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=TFDeiTForImageClassificationWithTeacherOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFDeiTForImageClassificationWithTeacherOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        cls_logits = self.cls_classifier(sequence_output[:, 0, :])
        distillation_logits = self.distillation_classifier(sequence_output[:, 1, :])

        # during inference, return the average of both classifier predictions
        logits = (cls_logits + distillation_logits) / 2

        if not return_dict:
            output = (logits, cls_logits, distillation_logits) + outputs[1:]
            return output

        return TFDeiTForImageClassificationWithTeacherOutput(
            logits=logits,
            cls_logits=cls_logits,
            distillation_logits=distillation_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
