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# coding=utf-8
# Copyright 2024 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.


import collections
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F

from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import LossKwargs, auto_docstring, can_return_tuple
from ..auto import AutoModel
from .configuration_got_ocr2 import GotOcr2Config, GotOcr2VisionConfig


class GotOcr2MLPBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
        self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
        self.act = ACT2FN[config.hidden_act]

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.lin1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.lin2(hidden_states)
        return hidden_states


class GotOcr2VisionAttention(nn.Module):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(self, config, window_size):
        super().__init__()
        input_size = (
            (config.image_size // config.patch_size, config.image_size // config.patch_size)
            if window_size == 0
            else (window_size, window_size)
        )

        self.num_attention_heads = config.num_attention_heads
        head_dim = config.hidden_size // config.num_attention_heads
        self.scale = head_dim**-0.5
        self.dropout = config.attention_dropout

        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
        self.proj = nn.Linear(config.hidden_size, config.hidden_size)

        self.use_rel_pos = config.use_rel_pos
        if self.use_rel_pos:
            if input_size is None:
                raise ValueError("Input size must be provided if using relative positional encoding.")

            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

    def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
        """
        Get relative positional embeddings according to the relative positions of
            query and key sizes.

        Args:
            q_size (int):
                size of the query.
            k_size (int):
                size of key k.
            rel_pos (`torch.Tensor`):
                relative position embeddings (L, channel).

        Returns:
            Extracted positional embeddings according to relative positions.
        """
        max_rel_dist = int(2 * max(q_size, k_size) - 1)
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode="linear",
        )
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)

        # Scale the coords with short length if shapes for q and k are different.
        q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
        k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

        return rel_pos_resized[relative_coords.long()]

    def get_decomposed_rel_pos(
        self,
        query: torch.Tensor,
        rel_pos_h: torch.Tensor,
        rel_pos_w: torch.Tensor,
        q_size: Tuple[int, int],
        k_size: Tuple[int, int],
    ) -> torch.Tensor:
        """
        Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
        https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py

        Args:
            query (`torch.Tensor`):
                query q in the attention layer with shape (batch_size, query_height * query_width, channel).
            rel_pos_h (`torch.Tensor`):
                relative position embeddings (Lh, channel) for height axis.
            rel_pos_w (`torch.Tensor`):
                relative position embeddings (Lw, channel) for width axis.
            q_size (tuple):
                spatial sequence size of query q with (query_height, query_width).
            k_size (tuple):
                spatial sequence size of key k with (key_height, key_width).

        Returns:
            decomposed_rel_pos (`torch.Tensor`):
                decomposed relative position embeddings.
        """
        query_height, query_width = q_size
        key_height, key_width = k_size
        relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
        relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)

        batch_size, _, dim = query.shape
        reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
        rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
        rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)

        decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]

        return decomposed_rel_pos

    def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
        batch_size, height, width, _ = hidden_states.shape
        # qkv with shape (3, batch_size, nHead, height * width, channel)
        qkv = (
            self.qkv(hidden_states)
            .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
            .permute(2, 0, 3, 1, 4)
        )
        # q, k, v with shape (batch_size * nHead, height * width, channel)
        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)

        attn_weights = (query * self.scale) @ key.transpose(-2, -1)

        if self.use_rel_pos:
            decomposed_rel_pos = self.get_decomposed_rel_pos(
                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
            )
            decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights)
            attn_weights = attn_weights + decomposed_rel_pos

        attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)

        attn_output = self.proj(attn_output)

        if output_attentions:
            outputs = (attn_output, attn_weights)
        else:
            outputs = (attn_output, None)

        return outputs


class GotOcr2VisionLayer(nn.Module):
    def __init__(self, config, window_size):
        super().__init__()
        self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attn = GotOcr2VisionAttention(config, window_size)
        self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = GotOcr2MLPBlock(config)
        self.window_size = window_size

    def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
        """
        Args:
        Partition into non-overlapping windows with padding if needed.
            hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window
            size.

        Returns:
            windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
            (pad_height, pad_width): padded height and width before partition
        """
        batch_size, height, width, channel = hidden_states.shape

        pad_h = (window_size - height % window_size) % window_size
        pad_w = (window_size - width % window_size) % window_size
        hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
        pad_height, pad_width = height + pad_h, width + pad_w

        hidden_states = hidden_states.reshape(
            batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel
        )
        windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel)
        return windows, (pad_height, pad_width)

    def window_unpartition(
        self, windows: torch.Tensor, window_size: int, padding_shape: Tuple[int, int], original_shape: Tuple[int, int]
    ) -> torch.Tensor:
        """
        Args:
        Window unpartition into original sequences and removing padding.
            hidden_states (tensor):
                input tokens with [batch_size * num_windows, window_size, window_size, channel].
            window_size (int):
                window size.
            padding_shape (Tuple):
                padded height and width (pad_height, pad_width).
            original_shape (Tuple): original height and width (height, width) before padding.

        Returns:
            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
        """
        pad_height, pad_width = padding_shape
        height, width = original_shape
        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
        hidden_states = windows.reshape(
            batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
        )
        hidden_states = (
            hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1)
        )

        hidden_states = hidden_states[:, :height, :width, :].contiguous()
        return hidden_states

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor]:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        # Window partition
        if self.window_size > 0:
            height, width = hidden_states.shape[1], hidden_states.shape[2]
            hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)

        hidden_states, attn_weights = self.attn(
            hidden_states=hidden_states,
            output_attentions=output_attentions,
        )
        # Reverse window partition
        if self.window_size > 0:
            hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))

        hidden_states = residual + hidden_states
        layernorm_output = self.layer_norm2(hidden_states)
        hidden_states = hidden_states + self.mlp(layernorm_output)

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (attn_weights,)

        return outputs


@dataclass
class GotOcr2VisionEncoderOutput(ModelOutput):
    """
    Base class for got_ocr2 vision model's outputs that also contains image embeddings obtained by applying the projection
    layer to the pooler_output.

    Args:
        image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
            The image embeddings obtained by applying the projection layer to the pooler_output.
        last_hidden_state (`torch.FloatTensor` 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(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            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 optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (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.
    """

    image_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


class GotOcr2PatchEmbeddings(nn.Module):
    """
    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):
        super().__init__()
        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 = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values):
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if 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]})."
            )
        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
        return embeddings


class GotOcr2LayerNorm(nn.Module):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError(f"Unsupported data format: {self.data_format}")
        self.normalized_shape = (normalized_shape,)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.data_format == "channels_last":
            x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            input_dtype = x.dtype
            x = x.float()
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = x.to(dtype=input_dtype)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x


class GotOcr2VisionNeck(nn.Module):
    def __init__(self, config: GotOcr2VisionConfig):
        super().__init__()
        self.config = config

        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
        self.layer_norm1 = GotOcr2LayerNorm(config.output_channels, data_format="channels_first")
        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False)
        self.layer_norm2 = GotOcr2LayerNorm(config.output_channels, data_format="channels_first")

    def forward(self, hidden_states):
        hidden_states = hidden_states.permute(0, 3, 1, 2)
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm1(hidden_states)

        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        return hidden_states


class GotOcr2VisionEncoder(nn.Module):
    def __init__(self, config: GotOcr2VisionConfig):
        super().__init__()
        self.config = config
        self.image_size = config.image_size

        self.patch_embed = GotOcr2PatchEmbeddings(config)

        self.pos_embed = None
        if config.use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = nn.Parameter(
                torch.zeros(
                    1,
                    config.image_size // config.patch_size,
                    config.image_size // config.patch_size,
                    config.hidden_size,
                )
            )

        self.layers = nn.ModuleList()
        for i in range(config.num_hidden_layers):
            layer = GotOcr2VisionLayer(
                config,
                window_size=config.window_size if i not in config.global_attn_indexes else 0,
            )
            self.layers.append(layer)

        self.neck = GotOcr2VisionNeck(config)

        self.gradient_checkpointing = False

    def get_input_embeddings(self):
        return self.patch_embed

    @can_return_tuple
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
    ) -> GotOcr2VisionEncoderOutput:
        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
        )

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

        hidden_states = self.patch_embed(pixel_values)
        if self.pos_embed is not None:
            hidden_states = hidden_states + self.pos_embed

        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        for i, layer_module in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.__call__,
                    hidden_states,
                )
            else:
                layer_outputs = layer_module(hidden_states, output_attentions=output_attentions)

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        hidden_states = self.neck(hidden_states)

        return GotOcr2VisionEncoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class GotOcr2MultiModalProjector(nn.Module):
    def __init__(self, config: GotOcr2Config):
        super().__init__()
        vision_output_channels = config.vision_config.output_channels
        language_hidden_size = config.text_config.hidden_size
        self.conv_upsampler1 = nn.Conv2d(
            vision_output_channels, vision_output_channels * 2, kernel_size=3, stride=2, padding=1, bias=False
        )
        self.conv_upsampler2 = nn.Conv2d(
            vision_output_channels * 2, language_hidden_size, kernel_size=3, stride=2, padding=1, bias=False
        )
        self.multimodal_projector = nn.Linear(language_hidden_size, language_hidden_size)

    def forward(self, vision_embeddings: torch.Tensor) -> torch.Tensor:
        hidden_state = self.conv_upsampler1(vision_embeddings)
        hidden_state = self.conv_upsampler2(hidden_state)
        hidden_state = hidden_state.flatten(2).permute(0, 2, 1)
        hidden_state = self.multimodal_projector(hidden_state)
        return hidden_state


@dataclass
class GotOcr2CausalLMOutputWithPast(ModelOutput):
    """
    Base class for GotOcr2 causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            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 optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (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.
        image_hidden_states (`torch.FloatTensor`, *optional*):
            A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
            image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None
    past_key_values: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[torch.FloatTensor] = None


@dataclass
class GotOcr2ModelOutputWithPast(BaseModelOutputWithPast):
    """
    Base class for GotOcr2 outputs, with hidden states and attentions.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            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 optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (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.
        image_hidden_states (`torch.FloatTensor`, *optional*):
            A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
            image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    """

    image_hidden_states: Optional[torch.FloatTensor] = None


@auto_docstring
class GotOcr2PreTrainedModel(PreTrainedModel):
    config_class = GotOcr2Config
    base_model_prefix = ""
    supports_gradient_checkpointing = True
    _skip_keys_device_placement = "past_key_values"
    _supports_cache_class = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_attention_backend = True

    def _init_weights(self, module):
        std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)

        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, (nn.LayerNorm, GotOcr2LayerNorm)):  # noqa: F821
            module.weight.data.fill_(1.0)
            module.bias.data.zero_()
        elif isinstance(module, GotOcr2VisionAttention):
            if module.use_rel_pos:
                module.rel_pos_h.data.zero_()
                module.rel_pos_w.data.zero_()
        elif isinstance(module, GotOcr2VisionEncoder):
            if module.pos_embed is not None:
                module.pos_embed.data.zero_()


@auto_docstring(
    custom_intro="""
    The GotOcr2 model which consists of a vision backbone and a language model, without a language modeling head.
    """
)
class GotOcr2Model(GotOcr2PreTrainedModel):
    _checkpoint_conversion_mapping = {"language_model.model": "language_model"}

    def __init__(self, config: GotOcr2Config):
        super().__init__(config)
        self.vision_tower = GotOcr2VisionEncoder(config.vision_config)

        self.multi_modal_projector = GotOcr2MultiModalProjector(config)
        self.language_model = AutoModel.from_config(config.text_config)
        self.post_init()

    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
    ):
        """
        Obtains image last hidden states from the vision tower and apply multimodal projection.

        Args:
            pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
        Returns:
            image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
        """
        image_outputs = self.vision_tower(pixel_values).last_hidden_state
        return self.multi_modal_projector(image_outputs)

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Union[Tuple, GotOcr2ModelOutputWithPast]:
        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 (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if pixel_values is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
            )

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        if pixel_values is not None:
            image_features = self.get_image_features(pixel_values=pixel_values.to(inputs_embeds.dtype))
            n_image_tokens = (input_ids == self.config.image_token_id).sum()
            n_image_features = image_features.shape[0] * image_features.shape[1]
            if n_image_tokens != n_image_features:
                raise ValueError(
                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
                )
            special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

        outputs = self.language_model(
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
            cache_position=cache_position,
            **kwargs,
        )

        return GotOcr2ModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=image_features if pixel_values is not None else None,
        )


class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...


@auto_docstring(
    custom_intro="""
    The GOT_OCR2 model which consists of a vision backbone and a language model.
    """
)
class GotOcr2ForConditionalGeneration(GotOcr2PreTrainedModel, GenerationMixin):
    _checkpoint_conversion_mapping = {
        "^language_model.model": "model.language_model",
        "^vision_tower": "model.vision_tower",
        "^multi_modal_projector": "model.multi_modal_projector",
        "^language_model.lm_head": "lm_head",
    }
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: GotOcr2Config):
        super().__init__(config)
        self.model = GotOcr2Model(config)
        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
        self.post_init()

    def get_input_embeddings(self):
        return self.model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.model.set_input_embeddings(value)

    def get_output_embeddings(self) -> nn.Module:
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    # Make modules available throught conditional class for BC
    @property
    def language_model(self):
        return self.model.language_model

    @property
    def vision_tower(self):
        return self.model.vision_tower

    @property
    def multi_modal_projector(self):
        return self.model.multi_modal_projector

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[KwargsForCausalLM],
    ) -> Union[Tuple, GotOcr2CausalLMOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, GotOcr2ForConditionalGeneration, TextStreamer

        >>> model = GotOcr2ForConditionalGeneration.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf").to("cuda")
        >>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")

        >>> url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(image, return_tensors="pt", color="green").to("cuda")

        >>> # Generate
        >>> streamer = TextStreamer(processor.tokenizer, skip_prompt=True, skip_special_tokens=True)
        >>> generate_ids = model.generate(
        ...     **inputs,
        ...     do_sample=False,
        ...     tokenizer = processor.tokenizer,
        ...     stop_strings='<|im_end|>',
        ...     streamer=streamer,
        ...     max_new_tokens=4096,
        ... )
        "You should keep in mind what features from the module should be used, especially
        when you're planning to sell a template."
        ```"""
        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

        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(
                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
            )

        return GotOcr2CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=outputs.image_hidden_states,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        attention_mask=None,
        cache_position=None,
        logits_to_keep=None,
        **kwargs,
    ):
        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model

        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )

        if cache_position[0] == 0:
            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
            # Otherwise we need pixel values to be passed to model
            model_inputs["pixel_values"] = pixel_values

        return model_inputs

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        cache_position: torch.Tensor,
        batch_size: int,
        **kwargs,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
                `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache,
                to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )

        return causal_mask


__all__ = ["GotOcr2PreTrainedModel", "GotOcr2Model", "GotOcr2ForConditionalGeneration"]