# coding=utf-8
# Copyright 2022 Microsoft Research 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.
""" TF 2.0 Swin Transformer model."""


import collections.abc
import math
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACT2FN
from ...modeling_tf_utils import (
    TFPreTrainedModel,
    TFSequenceClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import shape_list
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_swin import SwinConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "SwinConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoFeatureExtractor"

# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/swin-tiny-patch4-window7-224"
_EXPECTED_OUTPUT_SHAPE = [1, 49, 768]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "microsoft/swin-tiny-patch4-window7-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "microsoft/swin-tiny-patch4-window7-224",
    # See all Swin models at https://huggingface.co/models?filter=swin
]

# drop_path, TFSwinPatchEmbeddings, TFSwinPatchMerging and TFSwinDropPath are tensorflow
# implementations of PyTorch functionalities in the timm library.


@dataclass
class TFSwinEncoderOutput(ModelOutput):
    """
    Swin encoder's outputs, with potential hidden states and attentions.

    Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        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 stage) 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 stage) 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.
        reshaped_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 stage) of shape
            `(batch_size, hidden_size, height, width)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
            include the spatial dimensions.
    """

    last_hidden_state: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    reshaped_hidden_states: Optional[Tuple[tf.Tensor]] = None


@dataclass
class TFSwinModelOutput(ModelOutput):
    """
    Swin model's outputs that also contains a pooling of the last hidden states.

    Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
            Average pooling of the last layer hidden-state.
        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 stage) 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 stage) 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.
        reshaped_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 stage) of shape
            `(batch_size, hidden_size, height, width)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
            include the spatial dimensions.
    """

    last_hidden_state: tf.Tensor = None
    pooler_output: Optional[tf.Tensor] = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    reshaped_hidden_states: Optional[Tuple[tf.Tensor]] = None


@dataclass
class TFSwinMaskedImageModelingOutput(ModelOutput):
    """
    Swin masked image model outputs.

    Args:
        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
            Masked image modeling (MLM) loss.
        logits (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Reconstructed pixel values.
        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 stage) 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 stage) 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.
        reshaped_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 stage) of shape
            `(batch_size, hidden_size, height, width)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
            include the spatial dimensions.
    """

    loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    reshaped_hidden_states: Optional[Tuple[tf.Tensor]] = None


@dataclass
class TFSwinImageClassifierOutput(ModelOutput):
    """
    Swin outputs for image classification.

    Args:
        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        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 stage) 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 stage) 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.
        reshaped_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 stage) of shape
            `(batch_size, hidden_size, height, width)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
            include the spatial dimensions.
    """

    loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    reshaped_hidden_states: Optional[Tuple[tf.Tensor]] = None


def window_partition(input_feature: tf.Tensor, window_size: int) -> tf.Tensor:
    """
    Partitions the given input into windows.
    """
    batch_size, height, width, num_channels = shape_list(input_feature)
    input_feature = tf.reshape(
        input_feature,
        (batch_size, height // window_size, window_size, width // window_size, window_size, num_channels),
    )
    windows = tf.transpose(input_feature, (0, 1, 3, 2, 4, 5))
    windows = tf.reshape(windows, (-1, window_size, window_size, num_channels))
    return windows


def window_reverse(windows: tf.Tensor, window_size: int, height: int, width: int) -> tf.Tensor:
    """
    Merges windows to produce higher resolution features.
    """
    x = shape_list(windows)[0]
    y = tf.cast(height * width / (window_size * window_size), tf.int32)
    batch_size = int(x / y)
    windows = tf.reshape(
        windows, (batch_size, height // window_size, width // window_size, window_size, window_size, -1)
    )
    windows = tf.transpose(windows, (0, 1, 3, 2, 4, 5))
    windows = tf.reshape(windows, (batch_size, height, width, -1))
    return windows


def drop_path(
    input: tf.Tensor, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
) -> tf.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    input_shape = shape_list(input)
    ndim = len(input_shape)
    shape = [input_shape[0]] + [1] * (ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = tf.random.uniform(shape)
    random_tensor = tf.where(random_tensor <= keep_prob, 1.0, 0.0)
    if keep_prob > 0.0 and scale_by_keep:
        random_tensor /= keep_prob
    return input * random_tensor


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

    def __init__(self, config: SwinConfig, use_mask_token: bool = False, **kwargs) -> None:
        super().__init__(**kwargs)
        self.patch_embeddings = TFSwinPatchEmbeddings(config, name="patch_embeddings")
        self.num_patches = self.patch_embeddings.num_patches
        self.patch_grid = self.patch_embeddings.grid_size
        self.embed_dim = config.embed_dim
        self.use_mask_token = use_mask_token
        self.use_absolute_embeddings = config.use_absolute_embeddings

        self.norm = tf.keras.layers.LayerNormalization(name="norm", epsilon=1e-5)
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")

    def build(self, input_shape: tf.TensorShape) -> None:
        if self.use_mask_token:
            self.mask_token = self.add_weight(shape=(1, 1, self.embed_dim), initializer="zeros", name="mask_token")
        else:
            self.mask_token = None

        if self.use_absolute_embeddings:
            self.position_embeddings = self.add_weight(
                (1, self.num_patches + 1, self.embed_dim), initializer="zeros", name="positional_embeddings"
            )
        else:
            self.position_embeddings = None
        super().build(input_shape)

    def call(
        self, pixel_values: tf.Tensor, bool_masked_pos: bool = None, training: bool = False
    ) -> Tuple[tf.Tensor, Tuple[int, int]]:
        embeddings, output_dimensions = self.patch_embeddings(pixel_values, training=training)
        embeddings = self.norm(embeddings, training=training)
        batch_size, seq_len, _ = shape_list(embeddings)

        if bool_masked_pos is not None:
            mask_tokens = tf.repeat(self.mask_token, batch_size, 0)
            mask_tokens = tf.repeat(mask_tokens, seq_len, 1)
            # replace the masked visual tokens by mask_tokens
            mask = tf.expand_dims(bool_masked_pos, -1)
            mask = tf.cast(mask, mask_tokens.dtype)

            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        if self.position_embeddings is not None:
            embeddings = embeddings + self.position_embeddings

        embeddings = self.dropout(embeddings, training=training)

        return embeddings, output_dimensions


class TFSwinPatchEmbeddings(tf.keras.layers.Layer):
    """
    Image to Patch Embedding.
    """

    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.embed_dim
        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.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])

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

    def maybe_pad(self, pixel_values: tf.Tensor, height: int, width: int) -> tf.Tensor:
        if width % self.patch_size[1] != 0:
            pad_values = ((0, 0), (0, 0), (0, 0), (0, self.patch_size[1] - width % self.patch_size[1]))
            pixel_values = tf.pad(pixel_values, pad_values)
        if height % self.patch_size[0] != 0:
            pad_values = ((0, 0), (0, 0), (0, self.patch_size[0] - height % self.patch_size[0]), (0, 0))
            pixel_values = tf.pad(pixel_values, pad_values)
        return pixel_values

    def call(self, pixel_values: tf.Tensor, training: bool = False) -> Tuple[tf.Tensor, Tuple[int, int]]:
        _, num_channels, height, width = shape_list(pixel_values)
        if tf.executing_eagerly() and num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        # pad the input to be divisible by self.patch_size, if needed
        pixel_values = self.maybe_pad(pixel_values, height, width)

        # B,C,H,W -> B,H,W,C
        pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1))

        embeddings = self.projection(pixel_values, training=training)

        # B,H,W,C -> B,C,H,W
        embeddings = tf.transpose(embeddings, (0, 3, 1, 2))

        batch_size, channels, height, width = shape_list(embeddings)
        output_dimensions = (height, width)

        embeddings = tf.reshape(embeddings, (batch_size, channels, -1))
        embeddings = tf.transpose(embeddings, (0, 2, 1))
        return embeddings, output_dimensions


class TFSwinPatchMerging(tf.keras.layers.Layer):
    """
    Patch Merging Layer.

    Args:
        input_resolution (`Tuple[int]`):
            Resolution of input feature.
        dim (`int`):
            Number of input channels.
        norm_layer (`tf.keras.layer.Layer`, *optional*, defaults to `tf.keras.layers.LayerNormalization`):
            Normalization layer class.
    """

    def __init__(
        self, input_resolution: Tuple[int, int], dim: int, norm_layer: Optional[Callable] = None, **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = tf.keras.layers.Dense(2 * dim, use_bias=False, name="reduction")
        if norm_layer is None:
            # Use same default epsilon as PyTorch
            self.norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="norm")
        else:
            self.norm = norm_layer(name="norm")

    def maybe_pad(self, input_feature: tf.Tensor, height: int, width: int) -> tf.Tensor:
        should_pad = (height % 2 == 1) or (width % 2 == 1)
        if should_pad:
            pad_values = ((0, 0), (0, height % 2), (0, width % 2), (0, 0))
            input_feature = tf.pad(input_feature, pad_values)

        return input_feature

    def call(self, input_feature: tf.Tensor, input_dimensions: Tuple[int, int], training: bool = False) -> tf.Tensor:
        height, width = input_dimensions
        # `dim` is height * width
        batch_size, _, num_channels = shape_list(input_feature)

        input_feature = tf.reshape(input_feature, (batch_size, height, width, num_channels))
        # pad input to be disible by width and height, if needed
        input_feature = self.maybe_pad(input_feature, height, width)
        # [batch_size, height/2, width/2, num_channels]
        input_feature_0 = input_feature[:, 0::2, 0::2, :]
        # [batch_size, height/2, width/2, num_channels]
        input_feature_1 = input_feature[:, 1::2, 0::2, :]
        # [batch_size, height/2, width/2, num_channels]
        input_feature_2 = input_feature[:, 0::2, 1::2, :]
        # [batch_size, height/2, width/2, num_channels]
        input_feature_3 = input_feature[:, 1::2, 1::2, :]
        # batch_size height/2 width/2 4*num_channels
        input_feature = tf.concat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
        input_feature = tf.reshape(
            input_feature, (batch_size, -1, 4 * num_channels)
        )  # batch_size height/2*width/2 4*C

        input_feature = self.norm(input_feature, training=training)
        input_feature = self.reduction(input_feature, training=training)

        return input_feature


class TFSwinDropPath(tf.keras.layers.Layer):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: float = None, scale_by_keep: bool = True, **kwargs) -> None:
        super(TFSwinDropPath, self).__init__(**kwargs)
        self.drop_prob = drop_prob
        self.scale_by_keep = scale_by_keep

    def call(self, input: tf.Tensor, training: bool = False) -> tf.Tensor:
        return drop_path(input, self.drop_prob, training, self.scale_by_keep)


class TFSwinSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
        super().__init__(**kwargs)
        if dim % num_heads != 0:
            raise ValueError(
                f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
            )

        self.num_attention_heads = num_heads
        self.attention_head_size = int(dim / num_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        window_size = config.window_size
        self.window_size = (
            window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size)
        )

        # get pair-wise relative position index for each token inside the window
        coords_h = tf.range(self.window_size[0])
        coords_w = tf.range(self.window_size[1])
        coords = tf.stack(tf.meshgrid(coords_h, coords_w, indexing="ij"))
        coords_flatten = tf.reshape(coords, (shape_list(coords)[0], -1))
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
        relative_coords = tf.transpose(relative_coords, (1, 2, 0))

        stack_0, stack_1 = tf.unstack(relative_coords, axis=2)
        stack_0 += self.window_size[0] - 1
        stack_0 *= 2 * self.window_size[1] - 1
        stack_1 += self.window_size[1] - 1
        relative_coords = tf.stack([stack_0, stack_1], axis=2)
        self.relative_position_index = tf.reduce_sum(relative_coords, axis=-1)

        self.query = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            use_bias=config.qkv_bias,
            name="query",
        )
        self.key = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            use_bias=config.qkv_bias,
            name="key",
        )
        self.value = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            use_bias=config.qkv_bias,
            name="value",
        )

        self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)

    def build(self, input_shape: tf.TensorShape) -> None:
        self.relative_position_bias_table = self.add_weight(
            shape=(((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1)), self.num_attention_heads),
            initializer="zeros",
            name="relative_position_bias_table",
        )
        super().build(input_shape)

    def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor:
        new_x_shape = shape_list(x)[:-1] + [self.num_attention_heads, self.attention_head_size]
        x = tf.reshape(x, new_x_shape)
        return tf.transpose(x, (0, 2, 1, 3))

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: Optional[tf.Tensor] = None,
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor, ...]:
        batch_size, dim, _ = shape_list(hidden_states)
        mixed_query_layer = self.query(hidden_states)

        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = tf.matmul(query_layer, tf.transpose(key_layer, (0, 1, 3, 2)))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        relative_position_bias = tf.gather(
            self.relative_position_bias_table, tf.reshape(self.relative_position_index, (-1,))
        )
        relative_position_bias = tf.reshape(
            relative_position_bias,
            (self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1),
        )

        relative_position_bias = tf.transpose(relative_position_bias, (2, 0, 1))
        attention_scores = attention_scores + tf.expand_dims(relative_position_bias, 0)

        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in SwinModel call() function)
            mask_shape = shape_list(attention_mask)[0]
            attention_scores = tf.reshape(
                attention_scores, (batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim)
            )
            attention_mask = tf.expand_dims(attention_mask, 1)
            attention_mask = tf.expand_dims(attention_mask, 0)
            attention_scores = attention_scores + attention_mask
            attention_scores = tf.reshape(attention_scores, (-1, self.num_attention_heads, dim, dim))

        # Normalize the attention scores to probabilities.
        attention_probs = tf.nn.softmax(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(attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = tf.matmul(attention_probs, value_layer)
        context_layer = tf.transpose(context_layer, (0, 2, 1, 3))
        new_context_layer_shape = shape_list(context_layer)[:-2] + [
            self.all_head_size,
        ]
        context_layer = tf.reshape(context_layer, new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


class TFSwinSelfOutput(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(dim, name="dense")
        self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob, name="dropout")

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        return hidden_states


class TFSwinAttention(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
        super().__init__(**kwargs)
        self.self = TFSwinSelfAttention(config, dim, num_heads, name="self")
        self.self_output = TFSwinSelfOutput(config, dim, name="output")
        self.pruned_heads = set()

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

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: Optional[tf.Tensor] = None,
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> tf.Tensor:
        self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions, training=training)
        attention_output = self.self_output(self_outputs[0], hidden_states, training=training)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class TFSwinIntermediate(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(int(config.mlp_ratio * dim), name="dense")
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class TFSwinOutput(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(dim, name="dense")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, "dropout")

    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        return hidden_states


class TFSwinLayer(tf.keras.layers.Layer):
    def __init__(
        self, config, dim, input_resolution: Tuple[int, int], num_heads: int, shift_size: int = 0, **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        min_res = tf.reduce_min(input_resolution)
        self.window_size = min_res if min_res <= config.window_size else config.window_size
        self.shift_size = 0 if min_res <= self.window_size else shift_size
        self.input_resolution = input_resolution

        self.layernorm_before = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_before"
        )
        self.attention = TFSwinAttention(config, dim, num_heads, name="attention")
        self.drop_path = (
            TFSwinDropPath(config.drop_path_rate, name="drop_path")
            if config.drop_path_rate > 0.0
            else tf.keras.layers.Activation("linear", name="drop_path")
        )
        self.layernorm_after = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_after"
        )
        self.intermediate = TFSwinIntermediate(config, dim, name="intermediate")
        self.swin_output = TFSwinOutput(config, dim, name="output")

    def get_attn_mask(self, height: int, width: int, window_size: int, shift_size: int) -> Optional[tf.Tensor]:
        img_mask = tf.zeros((height, width))
        height_slices = ((0, -window_size), (-window_size, -shift_size), (-shift_size, -1))
        width_slices = ((0, -window_size), (-window_size, -shift_size), (-shift_size, -1))

        # calculate attention mask for SW-MSA
        if shift_size > 0:
            count = 0
            for height_slice in height_slices:
                for width_slice in width_slices:
                    height_inds = tf.range(height_slice[0] % height, height_slice[1] % height + 1)
                    width_inds = tf.range(width_slice[0] % width, width_slice[1] % width + 1)
                    indices = tf.reshape(tf.stack(tf.meshgrid(height_inds, width_inds), axis=-1), (-1, 2))
                    if len(indices) >= 1:
                        updates = tf.ones((len(indices),), dtype=img_mask.dtype) * count
                        img_mask = tf.tensor_scatter_nd_update(img_mask, indices, updates)
                    count += 1

        img_mask = tf.expand_dims(img_mask, -1)
        img_mask = tf.expand_dims(img_mask, 0)

        mask_windows = window_partition(img_mask, self.window_size)
        mask_windows = tf.reshape(mask_windows, (-1, self.window_size * self.window_size))
        attn_mask = tf.expand_dims(mask_windows, 1) - tf.expand_dims(mask_windows, 2)
        attn_mask = tf.where(attn_mask != 0, float(-100.0), attn_mask)
        attn_mask = tf.where(attn_mask == 0, float(0.0), attn_mask)
        return attn_mask

    def maybe_pad(self, hidden_states: tf.Tensor, height: int, width: int) -> Tuple[tf.Tensor, tf.Tensor]:
        pad_right = (self.window_size - width % self.window_size) % self.window_size
        pad_bottom = (self.window_size - height % self.window_size) % self.window_size
        pad_values = [[0, 0], [0, pad_bottom], [0, pad_right], [0, 0]]
        hidden_states = tf.pad(hidden_states, pad_values)
        pad_values = tf.reshape(pad_values, (-1,))
        return hidden_states, pad_values

    def call(
        self,
        hidden_states: tf.Tensor,
        input_dimensions: Tuple[int, int],
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> tf.Tensor:
        # if window size is larger than input resolution, we don't partition windows
        min_res = tf.reduce_min(input_dimensions)
        shift_size = 0 if min_res <= self.window_size else self.shift_size
        window_size = min_res if min_res <= self.window_size else self.window_size

        height, width = input_dimensions
        batch_size, _, channels = shape_list(hidden_states)
        shortcut = hidden_states

        hidden_states = self.layernorm_before(hidden_states, training=training)
        hidden_states = tf.reshape(hidden_states, (batch_size, height, width, channels))
        # pad hidden_states to multiples of window size
        hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)

        _, height_pad, width_pad, _ = shape_list(hidden_states)
        # cyclic shift
        if shift_size > 0:
            shifted_hidden_states = tf.roll(hidden_states, shift=(-shift_size, -shift_size), axis=(1, 2))
        else:
            shifted_hidden_states = hidden_states

        # partition windows
        hidden_states_windows = window_partition(shifted_hidden_states, window_size)
        hidden_states_windows = tf.reshape(hidden_states_windows, (-1, window_size * window_size, channels))
        attn_mask = self.get_attn_mask(
            height=height_pad, width=width_pad, window_size=window_size, shift_size=shift_size
        )

        attention_outputs = self.attention(
            hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions, training=training
        )

        attention_output = attention_outputs[0]

        attention_windows = tf.reshape(attention_output, (-1, window_size, window_size, channels))
        shifted_windows = window_reverse(attention_windows, window_size, height_pad, width_pad)

        # reverse cyclic shift
        if shift_size > 0:
            attention_windows = tf.roll(shifted_windows, shift=(shift_size, shift_size), axis=(1, 2))
        else:
            attention_windows = shifted_windows

        was_padded = pad_values[3] > 0 or pad_values[5] > 0
        if was_padded:
            attention_windows = attention_windows[:, :height, :width, :]

        attention_windows = tf.reshape(attention_windows, (batch_size, height * width, channels))

        hidden_states = shortcut + self.drop_path(attention_windows, training=training)

        layer_output = self.layernorm_after(hidden_states, training=training)
        layer_output = self.intermediate(layer_output)
        layer_output = hidden_states + self.swin_output(layer_output, training=training)

        layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
        return layer_outputs


class TFSwinStage(tf.keras.layers.Layer):
    def __init__(
        self,
        config: SwinConfig,
        dim: int,
        input_resolution: Tuple[int, int],
        depth: int,
        num_heads: int,
        drop_path: List[float],
        downsample: Optional[Callable],
        **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.config = config
        self.dim = dim
        self.blocks = [
            TFSwinLayer(
                config=config,
                dim=dim,
                input_resolution=input_resolution,
                num_heads=num_heads,
                shift_size=0 if (i % 2 == 0) else config.window_size // 2,
                name=f"blocks.{i}",
            )
            for i in range(depth)
        ]

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(
                input_resolution,
                dim=dim,
                norm_layer=partial(tf.keras.layers.LayerNormalization, epsilon=1e-5),
                name="downsample",
            )
        else:
            self.downsample = None

        self.pointing = False

    def call(
        self,
        hidden_states: tf.Tensor,
        input_dimensions: Tuple[int, int],
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: Optional[bool] = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor, ...]:
        height, width = input_dimensions
        for i, layer_module in enumerate(self.blocks):
            layer_head_mask = head_mask[i] if head_mask is not None else None

            layer_outputs = layer_module(
                hidden_states, input_dimensions, layer_head_mask, output_attentions, training=training
            )

            hidden_states = layer_outputs[0]

        if self.downsample is not None:
            height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
            output_dimensions = (height, width, height_downsampled, width_downsampled)
            hidden_states = self.downsample(layer_outputs[0], input_dimensions, training=training)
        else:
            output_dimensions = (height, width, height, width)

        stage_outputs = (hidden_states, output_dimensions)

        if output_attentions:
            stage_outputs += layer_outputs[1:]
        return stage_outputs


class TFSwinEncoder(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, grid_size: Tuple[int, int], **kwargs):
        super().__init__(**kwargs)
        self.num_layers = len(config.depths)
        self.config = config
        dpr = list((tf.linspace(0, 1, sum(config.depths)) * config.drop_path_rate).numpy())
        self.layers = [
            TFSwinStage(
                config=config,
                dim=int(config.embed_dim * 2**i_layer),
                input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)),
                depth=config.depths[i_layer],
                num_heads=config.num_heads[i_layer],
                drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
                downsample=TFSwinPatchMerging if (i_layer < self.num_layers - 1) else None,
                name=f"layers.{i_layer}",
            )
            for i_layer in range(self.num_layers)
        ]

        self.gradient_checkpointing = False

    def call(
        self,
        hidden_states: tf.Tensor,
        input_dimensions: Tuple[int, int],
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        training: bool = False,
    ) -> Union[Tuple[tf.Tensor, ...], TFSwinEncoderOutput]:
        all_input_dimensions = ()
        all_hidden_states = () if output_hidden_states else None
        all_reshaped_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if output_hidden_states:
            batch_size, _, hidden_size = shape_list(hidden_states)
            # rearrange b (h w) c -> b c h w
            reshaped_hidden_state = tf.reshape(hidden_states, (batch_size, *input_dimensions, hidden_size))
            reshaped_hidden_state = tf.transpose(reshaped_hidden_state, (0, 3, 1, 2))
            all_hidden_states += (hidden_states,)
            all_reshaped_hidden_states += (reshaped_hidden_state,)

        for i, layer_module in enumerate(self.layers):
            layer_head_mask = head_mask[i] if head_mask is not None else None

            layer_outputs = layer_module(
                hidden_states, input_dimensions, layer_head_mask, output_attentions, training=training
            )

            hidden_states = layer_outputs[0]
            output_dimensions = layer_outputs[1]

            input_dimensions = (output_dimensions[-2], output_dimensions[-1])
            all_input_dimensions += (input_dimensions,)

            if output_hidden_states:
                batch_size, _, hidden_size = shape_list(hidden_states)
                # rearrange b (h w) c -> b c h w
                reshaped_hidden_state = tf.reshape(hidden_states, (batch_size, *input_dimensions, hidden_size))
                reshaped_hidden_state = tf.transpose(reshaped_hidden_state, (0, 3, 1, 2))
                all_hidden_states += (hidden_states,)
                all_reshaped_hidden_states += (reshaped_hidden_state,)

            if output_attentions:
                all_self_attentions += layer_outputs[2:]

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)

        return TFSwinEncoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            reshaped_hidden_states=all_reshaped_hidden_states,
        )


class TFSwinPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = SwinConfig
    base_model_prefix = "swin"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    def _set_gradient_checkpointing(self, module, value=False) -> None:
        if isinstance(module, TFSwinEncoder):
            module.gradient_checkpointing = value

    @property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
        """
        Dummy inputs to build the network. Returns:
            `Dict[str, tf.Tensor]`: The dummy inputs.
        """
        VISION_DUMMY_INPUTS = tf.random.uniform(
            shape=(3, self.config.num_channels, self.config.image_size, self.config.image_size),
            dtype=tf.float32,
        )
        return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}

    @tf.function(
        input_signature=[
            {
                "pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
            }
        ]
    )
    def serving(self, inputs):
        output = self.call(inputs)
        return self.serving_output(output)


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

    Parameters:
        config ([`SwinConfig`]): 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.
"""

SWIN_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See
            [`AutoFeatureExtractor.__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.
"""


def normalize_data_format(value: str) -> str:
    """
    From tensorflow addons
    https://github.com/tensorflow/addons/blob/8cec33fcaaf1cf90aec7bdd55a0fcdbb251ce5c2/tensorflow_addons/utils/keras_utils.py#L71
    """
    if value is None:
        value = tf.keras.backend.image_data_format()
    data_format = value.lower()
    if data_format not in {"channels_first", "channels_last"}:
        raise ValueError(
            'The `data_format` argument must be one of "channels_first", "channels_last". Received: ' + str(value)
        )
    return data_format


class AdaptiveAveragePooling1D(tf.keras.layers.Layer):
    """
    Args:
    Average 1D Pooling with adaptive kernel size.
      output_size: An integer or tuple/list of a single integer, specifying pooled_features.
        The new size of output channels.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape `(batch, steps, channels)` while `channels_first` corresponds
        to inputs with shape `(batch, channels, steps)`.
    Input shape:
      - If `data_format='channels_last'`: 3D tensor with shape `(batch, steps, channels)`.
      - If `data_format='channels_first'`: 3D tensor with shape `(batch, channels, steps)`.
    Output shape:
      - If `data_format='channels_last'`: 3D tensor with shape `(batch_size, pooled_steps, channels)`.
      - If `data_format='channels_first'`: 3D tensor with shape `(batch_size, channels, pooled_steps)`.

    Adapted from [tensorflow-addon's adaptive pooling.py](
        https://github.com/tensorflow/addons/blob/8cec33fcaaf1cf90aec7bdd55a0fcdbb251ce5c2/tensorflow_addons/layers/adaptive_pooling.py#L90-L120
    )
    """

    def __init__(
        self,
        output_size: Union[int, Iterable[int]],
        reduce_function: Callable = tf.reduce_mean,
        data_format: Optional[str] = None,
        **kwargs,
    ) -> None:
        self.data_format = normalize_data_format(data_format)
        self.reduce_function = reduce_function
        self.output_size = (output_size,) if isinstance(output_size, int) else tuple(output_size)
        super().__init__(**kwargs)

    def call(self, inputs: tf.Tensor, *args) -> None:
        bins = self.output_size[0]
        if self.data_format == "channels_last":
            splits = tf.split(inputs, bins, axis=1)
            splits = tf.stack(splits, axis=1)
            out_vect = self.reduce_function(splits, axis=2)
        else:
            splits = tf.split(inputs, bins, axis=2)
            splits = tf.stack(splits, axis=2)
            out_vect = self.reduce_function(splits, axis=3)
        return out_vect

    def compute_output_shape(self, input_shape: Iterable[int]) -> tf.TensorShape:
        input_shape = tf.TensorShape(input_shape).as_list()
        if self.data_format == "channels_last":
            shape = tf.TensorShape([input_shape[0], self.output_size[0], input_shape[2]])
        else:
            shape = tf.TensorShape([input_shape[0], input_shape[1], self.output_size[0]])
        return shape

    def get_config(self) -> Dict[str, Any]:
        config = {
            "output_size": self.output_size,
            "data_format": self.data_format,
        }
        base_config = super().get_config()
        return {**base_config, **config}


@keras_serializable
class TFSwinMainLayer(tf.keras.layers.Layer):
    config_class = SwinConfig

    def __init__(
        self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.config = config
        self.num_layers = len(config.depths)
        self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))

        self.embeddings = TFSwinEmbeddings(config, use_mask_token=use_mask_token, name="embeddings")
        self.encoder = TFSwinEncoder(config, self.embeddings.patch_grid, name="encoder")

        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
        self.pooler = AdaptiveAveragePooling1D(output_size=(1,)) if add_pooling_layer else None

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

    def _prune_heads(self, heads_to_prune: Dict[int, List]):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_head_mask(self, head_mask: Optional[Any]) -> List:
        if head_mask is not None:
            raise NotImplementedError
        return [None] * len(self.config.depths)

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

        # 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, input_dimensions = self.embeddings(
            pixel_values, bool_masked_pos=bool_masked_pos, training=training
        )

        encoder_outputs = self.encoder(
            embedding_output,
            input_dimensions,
            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 = None
        if self.pooler is not None:
            batch_size, _, num_features = shape_list(sequence_output)
            pooled_output = self.pooler(sequence_output)
            pooled_output = tf.reshape(pooled_output, (batch_size, num_features))

        if not return_dict:
            output = (sequence_output, pooled_output) + encoder_outputs[1:]
            return output

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


@add_start_docstrings(
    "The bare Swin Model transformer outputting raw hidden-states without any specific head on top.",
    SWIN_START_DOCSTRING,
)
class TFSwinModel(TFSwinPreTrainedModel):
    def __init__(
        self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(config, **kwargs)
        self.config = config
        self.swin = TFSwinMainLayer(config, name="swin")

    @add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSwinModelOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    @unpack_inputs
    def call(
        self,
        pixel_values: Optional[tf.Tensor] = None,
        bool_masked_pos: Optional[tf.Tensor] = None,
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFSwinModelOutput, 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")

        swin_outputs = self.swin(
            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 swin_outputs

    def serving_output(self, output: TFSwinModelOutput) -> TFSwinModelOutput:
        # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
        return TFSwinModelOutput(
            last_hidden_state=output.last_hidden_state,
            pooler_output=output.pooler_output,
            hidden_states=output.hidden_states,
            attentions=output.attentions,
            reshaped_hidden_states=output.reshaped_hidden_states,
        )


class TFSwinPixelShuffle(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 TFSwinDecoder(tf.keras.layers.Layer):
    def __init__(self, config: SwinConfig, **kwargs):
        super().__init__(**kwargs)
        self.conv2d = tf.keras.layers.Conv2D(
            filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, strides=1, name="0"
        )
        self.pixel_shuffle = TFSwinPixelShuffle(config.encoder_stride, name="1")

    def call(self, x: tf.Tensor) -> tf.Tensor:
        hidden_states = x
        # B,C,H,W -> B,H,W,C
        hidden_states = tf.transpose(hidden_states, (0, 2, 3, 1))
        hidden_states = self.conv2d(hidden_states)
        hidden_states = self.pixel_shuffle(hidden_states)
        # B,H,W,C -> B,C,H,W
        hidden_states = tf.transpose(hidden_states, (0, 3, 1, 2))
        return hidden_states


@add_start_docstrings(
    "Swin Model with a decoder on top for masked image modeling, as proposed in"
    " [SimMIM](https://arxiv.org/abs/2111.09886).",
    SWIN_START_DOCSTRING,
)
class TFSwinForMaskedImageModeling(TFSwinPreTrainedModel):
    def __init__(self, config: SwinConfig):
        super().__init__(config)

        self.swin = TFSwinMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="swin")

        self.decoder = TFSwinDecoder(config, name="decoder")

    @add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFSwinMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
    @unpack_inputs
    def call(
        self,
        pixel_values: Optional[tf.Tensor] = None,
        bool_masked_pos: Optional[tf.Tensor] = None,
        head_mask: Optional[tf.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[Tuple, TFSwinMaskedImageModelingOutput]:
        r"""
        bool_masked_pos (`tf.Tensor` of 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 AutoFeatureExtractor, TFSwinForMaskedImageModeling
        >>> 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)

        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
        >>> model = TFSwinForMaskedImageModeling.from_pretrained("microsoft/swin-tiny-patch4-window7-224")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = feature_extractor(images=image, return_tensors="tf").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = tf.random.uniform((1, num_patches)) >= 0.5

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, reconstructed_pixel_values = outputs.loss, outputs.logits
        >>> 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.swin(
            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 = tf.transpose(sequence_output, (0, 2, 1))
        batch_size, num_channels, sequence_length = shape_list(sequence_output)
        height = width = int(sequence_length**0.5)
        sequence_output = tf.reshape(sequence_output, (batch_size, num_channels, height, width))

        # Reconstruct pixel values
        reconstructed_pixel_values = self.decoder(sequence_output)

        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

        if not return_dict:
            output = (reconstructed_pixel_values,) + outputs[2:]
            return ((masked_im_loss,) + output) if masked_im_loss is not None else output

        return TFSwinMaskedImageModelingOutput(
            loss=masked_im_loss,
            logits=reconstructed_pixel_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            reshaped_hidden_states=outputs.reshaped_hidden_states,
        )

    def serving_output(self, output: TFSwinMaskedImageModelingOutput) -> TFSwinMaskedImageModelingOutput:
        # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
        return TFSwinMaskedImageModelingOutput(
            logits=output.logits,
            hidden_states=output.hidden_states,
            attentions=output.attentions,
            reshaped_hidden_states=output.reshaped_hidden_states,
        )


@add_start_docstrings(
    """
    Swin 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.
    """,
    SWIN_START_DOCSTRING,
)
class TFSwinForImageClassification(TFSwinPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: SwinConfig):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.swin = TFSwinMainLayer(config, name="swin")

        # 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")
        )

    @add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=TFSwinImageClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    @unpack_inputs
    def call(
        self,
        pixel_values: Optional[tf.Tensor] = None,
        head_mask: Optional[tf.Tensor] = None,
        labels: Optional[tf.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[Tuple[tf.Tensor, ...], TFSwinImageClassifierOutput]:
        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).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        pooled_output = outputs[1]

        logits = self.classifier(pooled_output, training=training)

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

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TFSwinImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            reshaped_hidden_states=outputs.reshaped_hidden_states,
        )

    def serving_output(self, output: TFSwinImageClassifierOutput) -> TFSwinImageClassifierOutput:
        # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
        return TFSwinImageClassifierOutput(
            logits=output.logits,
            hidden_states=output.hidden_states,
            attentions=output.attentions,
            reshaped_hidden_states=output.reshaped_hidden_states,
        )
