# coding=utf-8
# Copyright 2023 The HuggingFace Inc. & Google 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.
"""Pix2Struct modeling file"""

import math
from typing import Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPooling,
    CausalLMOutputWithCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    DUMMY_INPUTS,
    DUMMY_MASK,
    auto_docstring,
    is_torch_flex_attn_available,
    is_torchdynamo_compiling,
    logging,
)
from .configuration_pix2struct import Pix2StructConfig, Pix2StructTextConfig, Pix2StructVisionConfig


if is_torch_flex_attn_available():
    from torch.nn.attention.flex_attention import BlockMask

    from ...integrations.flex_attention import make_flex_block_causal_mask


logger = logging.get_logger(__name__)

# General docstring


# Adapted from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Pix2Struct
class Pix2StructLayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
        # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated
        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
        # half-precision inputs is done in fp32

        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


try:
    from apex.normalization import FusedRMSNorm

    Pix2StructLayerNorm = FusedRMSNorm

    logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of Pix2StructLayerNorm")
except ImportError:
    # using the normal Pix2StructLayerNorm
    pass
except Exception:
    logger.warning("Discovered apex but it failed to load, falling back to Pix2StructLayerNorm")


class Pix2StructVisionEmbeddings(nn.Module):
    r"""
    Construct the embeddings from patch. In `Pix2Struct` the input is different from classic Vision-transformer models.
    Here the input is a sequence of `seq_len` flattened patches that also combines padding patches (tokens). Each patch
    is represented by a vector of `hidden_size` values.
    """

    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.patch_projection = nn.Linear(config.patch_embed_hidden_size, config.hidden_size)

        self.row_embedder = nn.Embedding(config.seq_len, config.hidden_size)
        self.column_embedder = nn.Embedding(config.seq_len, config.hidden_size)

        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, flattened_patches: torch.Tensor) -> torch.Tensor:
        # the row and column indices are stored in the first and second position of the flattened_patches
        # flattened_patches: `batch_size`, `seq_len`, `hidden_size` + 2
        row_indices = flattened_patches[:, :, 0].long()
        col_indices = flattened_patches[:, :, 1].long()

        flattened_patches = flattened_patches[:, :, 2:]

        embeddings = self.patch_projection(flattened_patches)
        row_embeddings = self.row_embedder(row_indices)
        col_embeddings = self.column_embedder(col_indices)

        # sum all embeddings together
        embeddings = embeddings + row_embeddings + col_embeddings

        embeddings = self.dropout(embeddings)

        return embeddings


class Pix2StructVisionAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_attention_heads
        self.dropout = config.attention_dropout
        self.inner_dim = self.n_heads * self.key_value_proj_dim

        self.query = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
        self.key = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
        self.value = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
        self.output = nn.Linear(self.inner_dim, self.hidden_size, bias=False)

        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        output_attentions=False,
    ):
        """
        Self-attention block
        """
        # Input is (batch_size, seq_length, dim)
        # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
        batch_size, seq_length = hidden_states.shape[:2]

        def to_projection_shape(states):
            """projection"""
            return states.contiguous().view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

        # get query states
        # (batch_size, n_heads, seq_length, dim_per_head)
        query_states = to_projection_shape(self.query(hidden_states))

        # get key/value states
        key_states = to_projection_shape(self.key(hidden_states))
        value_states = to_projection_shape(self.value(hidden_states))

        # compute scores
        # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
        scores = torch.matmul(query_states, key_states.transpose(3, 2))

        if position_bias is None:
            position_bias = torch.zeros(
                (1, self.n_heads, seq_length, seq_length), device=scores.device, dtype=scores.dtype
            )
            if self.gradient_checkpointing and self.training:
                position_bias.requires_grad = True

            if attention_mask.dim() == 2:
                position_bias = position_bias + attention_mask[:, None, None, :].to(position_bias.device)
            elif attention_mask is not None:
                # (batch_size, n_heads, seq_length, key_length)
                position_bias = position_bias + attention_mask.to(position_bias.device)
            elif not is_torchdynamo_compiling():
                attention_mask = torch.ones(
                    (batch_size, seq_length), device=position_bias.device, dtype=position_bias.dtype
                )
                position_bias = position_bias + attention_mask.to(position_bias.device)

            position_bias = 1 - position_bias

        position_bias_masked = position_bias.masked_fill(position_bias == 1, torch.finfo(scores.dtype).min)
        scores += position_bias_masked
        scores = torch.max(scores, torch.tensor(torch.finfo(scores.dtype).min))

        # (batch_size, n_heads, seq_length, key_length)
        attn_weights = nn.functional.softmax(scores, dim=-1, dtype=torch.float32).type_as(scores)

        # (batch_size, n_heads, seq_length, key_length)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.matmul(attn_weights, value_states)

        # (batch_size, seq_length, dim)
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)

        attn_output = self.output(attn_output)

        outputs = (attn_output,) + (position_bias,)

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


# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5DenseGatedActDense->Pix2StructVisionMlp,T5Config->Pix2StructVisionConfig,config.d_model->config.hidden_size,dropout_rate->dropout_rate
class Pix2StructVisionMlp(nn.Module):
    def __init__(self, config: Pix2StructVisionConfig):
        super().__init__()
        self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]

    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)

        # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
        # See https://github.com/huggingface/transformers/issues/20287
        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
        if (
            isinstance(self.wo.weight, torch.Tensor)
            and hidden_states.dtype != self.wo.weight.dtype
            and self.wo.weight.dtype != torch.int8
        ):
            hidden_states = hidden_states.to(self.wo.weight.dtype)

        hidden_states = self.wo(hidden_states)
        return hidden_states


class Pix2StructVisionLayer(GradientCheckpointingLayer):
    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = Pix2StructVisionAttention(config)
        self.mlp = Pix2StructVisionMlp(config)
        self.pre_mlp_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pre_attention_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
        residual = hidden_states

        # in Pix2StructVision, layernorm is applied before self-attention
        hidden_states = self.pre_attention_layer_norm(hidden_states)

        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # first residual connection
        hidden_states = attention_output + residual

        # in Pix2StructVision, layernorm is also applied after self-attention
        layer_output = self.pre_mlp_layer_norm(hidden_states)
        layer_output = self.mlp(layer_output) + hidden_states  # second residual connection

        outputs = (layer_output,) + outputs

        return outputs


class Pix2StructVisionEncoder(nn.Module):
    def __init__(self, config: Pix2StructVisionConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([Pix2StructVisionLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

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

            layer_outputs = layer_module(hidden_states, attention_mask, 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,)

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


@auto_docstring
class Pix2StructPreTrainedModel(PreTrainedModel):
    config: Pix2StructConfig
    input_modalities = ["image", "text"]

    _can_compile_fullgraph = False

    @property
    def dummy_inputs(self):
        input_ids = torch.tensor(DUMMY_INPUTS)
        input_mask = torch.tensor(DUMMY_MASK)
        dummy_inputs = {
            "decoder_input_ids": input_ids,
            "input_ids": input_ids,
            "decoder_attention_mask": input_mask,
        }
        return dummy_inputs

    def _init_weights(self, module):
        """Initialize the weights"""
        factor = self.config.initializer_factor  # Used for testing weights initialization
        if isinstance(module, Pix2StructLayerNorm):
            module.weight.data.fill_(factor * 1.0)
        elif isinstance(module, Pix2StructTextDenseGatedActDense):
            hidden_size = (
                self.config.text_config.hidden_size
                if isinstance(self.config, Pix2StructConfig)
                else self.config.hidden_size
            )
            d_ff = self.config.text_config.d_ff if isinstance(self.config, Pix2StructConfig) else self.config.d_ff

            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
                module.wi_0.bias.data.zero_()
            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
                module.wi_1.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None:
                module.wo.bias.data.zero_()
        elif isinstance(module, Pix2StructTextAttention):
            hidden_size = (
                self.config.text_config.hidden_size
                if isinstance(self.config, Pix2StructConfig)
                else self.config.hidden_size
            )
            key_value_proj_dim = (
                self.config.text_config.d_kv if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size
            )
            n_heads = (
                self.config.text_config.num_heads
                if isinstance(self.config, Pix2StructConfig)
                else self.config.num_heads
            )

            module.query.weight.data.normal_(mean=0.0, std=factor * ((hidden_size * key_value_proj_dim) ** -0.5))
            module.key.weight.data.normal_(mean=0.0, std=factor * (hidden_size**-0.5))
            module.value.weight.data.normal_(mean=0.0, std=factor * (hidden_size**-0.5))
            module.output.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
            if module.has_relative_attention_bias:
                module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
        elif isinstance(module, nn.Embedding):
            hidden_size = (
                self.config.text_config.hidden_size
                if isinstance(self.config, Pix2StructConfig)
                else self.config.hidden_size
            )

            module.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, Pix2StructTextModel):
            hidden_size = (
                self.config.text_config.hidden_size
                if isinstance(self.config, Pix2StructConfig)
                else self.config.hidden_size
            )

            module.lm_head.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
        elif isinstance(module, (nn.Linear, nn.Conv2d)):
            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
            # `trunc_normal_cpu` not implemented in `half` issues
            module.weight.data = nn.init.trunc_normal_(
                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
            ).to(module.weight.dtype)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, Pix2StructLayerNorm):
            if module.weight is not None:
                module.weight.data.fill_(1.0)
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    # Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right with T5->Pix2Struct
    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id

        if decoder_start_token_id is None:
            raise ValueError(
                "self.model.config.decoder_start_token_id has to be defined. In Pix2Struct it is usually set to the pad_token_id. "
                "See Pix2Struct docs for more information."
            )

        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
        shifted_input_ids[..., 0] = decoder_start_token_id

        if pad_token_id is None:
            raise ValueError("self.model.config.pad_token_id has to be defined.")
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

        return shifted_input_ids


@auto_docstring
class Pix2StructVisionModel(Pix2StructPreTrainedModel):
    config: Pix2StructVisionConfig
    main_input_name = "flattened_patches"
    input_modalities = "image"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Pix2StructVisionLayer"]

    def __init__(self, config: Pix2StructVisionConfig):
        super().__init__(config)
        self.config = config

        self.embeddings = Pix2StructVisionEmbeddings(config)
        self.encoder = Pix2StructVisionEncoder(config)

        self.layernorm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.patch_projection

    @auto_docstring
    def forward(
        self,
        flattened_patches: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPooling]:
        r"""
        flattened_patches (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_channels x patch_height x patch_width)`):
            Flattened and padded pixel values. These values can be obtained using [`AutoImageProcessor`]. See
            [`Pix2StructVisionImageProcessor.__call__`] for details. Check the [original
            paper](https://huggingface.co/papers/2210.03347) (figure 5) for more details.

        Example:

        ```python
        >>> import requests
        >>> from PIL import Image
        >>> from transformers import AutoProcessor, Pix2StructVisionModel

        >>> image_processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
        >>> model = Pix2StructVisionModel.from_pretrained("google/pix2struct-textcaps-base")

        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 2048, 768]
        ```
        """
        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 flattened_patches is None:
            raise ValueError("You have to specify flattened_patches")

        if attention_mask is None:
            # check where `flattened_patches` is not 0
            attention_mask = (flattened_patches.sum(dim=-1) != 0).float()

        embedding_output = self.embeddings(flattened_patches)

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)

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

        return BaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Pix2StructText,d_model->hidden_size
class Pix2StructTextDenseGatedActDense(nn.Module):
    def __init__(self, config: Pix2StructTextConfig):
        super().__init__()
        self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]

    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)

        # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
        # See https://github.com/huggingface/transformers/issues/20287
        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
        if (
            isinstance(self.wo.weight, torch.Tensor)
            and hidden_states.dtype != self.wo.weight.dtype
            and self.wo.weight.dtype != torch.int8
        ):
            hidden_states = hidden_states.to(self.wo.weight.dtype)

        hidden_states = self.wo(hidden_states)
        return hidden_states


class Pix2StructTextLayerFF(nn.Module):
    def __init__(self, config: Pix2StructTextConfig):
        super().__init__()
        self.DenseReluDense = Pix2StructTextDenseGatedActDense(config)

        self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    # Copied from transformers.models.t5.modeling_t5.T5LayerFF.forward
    def forward(self, hidden_states):
        forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.DenseReluDense(forwarded_states)
        hidden_states = hidden_states + self.dropout(forwarded_states)
        return hidden_states


class Pix2StructTextAttention(nn.Module):
    def __init__(
        self, config: Pix2StructTextConfig, has_relative_attention_bias=False, layer_idx: Optional[int] = None
    ):
        super().__init__()
        self.has_relative_attention_bias = has_relative_attention_bias
        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.relative_attention_max_distance = config.relative_attention_max_distance
        self.hidden_size = config.hidden_size
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.key_value_proj_dim
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
                "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.key = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.value = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.output = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

        if self.has_relative_attention_bias:
            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)

        self.gradient_checkpointing = False

    @staticmethod
    # Copied from transformers.models.t5.modeling_t5.T5Attention._relative_position_bucket
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        """
        Adapted from Mesh Tensorflow:
        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

        Translate relative position to a bucket number for relative attention. The relative position is defined as
        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
        This should allow for more graceful generalization to longer sequences than the model has been trained on

        Args:
            relative_position: an int32 Tensor
            bidirectional: a boolean - whether the attention is bidirectional
            num_buckets: an integer
            max_distance: an integer

        Returns:
            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
        """
        relative_buckets = 0
        if bidirectional:
            num_buckets //= 2
            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
            relative_position = torch.abs(relative_position)
        else:
            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
        # now relative_position is in the range [0, inf)

        # half of the buckets are for exact increments in positions
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact

        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
        relative_position_if_large = max_exact + (
            torch.log(relative_position.float() / max_exact)
            / math.log(max_distance / max_exact)
            * (num_buckets - max_exact)
        ).to(torch.long)
        relative_position_if_large = torch.min(
            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
        )

        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
        return relative_buckets

    # Adapted from transformers.models.t5.modeling_t5.T5Attention.compute_bias
    def compute_bias(self, query_length, key_length, device=None, cache_position=None):
        """Compute binned relative position bias"""
        if device is None:
            device = self.relative_attention_bias.weight.device
        if cache_position is None:
            context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
        else:
            context_position = cache_position[:, None].to(device)
        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
        relative_position = memory_position - context_position  # shape (query_length, key_length)
        relative_position_bucket = self._relative_position_bucket(
            relative_position,  # shape (query_length, key_length)
            bidirectional=False,
            num_buckets=self.relative_attention_num_buckets,
            max_distance=self.relative_attention_max_distance,
        )
        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
        return values

    # Adapted from transformers.models.t5.modeling_t5.T5Attention.forward
    def forward(
        self,
        hidden_states,
        mask=None,
        key_value_states=None,
        position_bias=None,
        past_key_values=None,
        query_length=None,
        use_cache=False,
        output_attentions=False,
        cache_position=None,
    ):
        """
        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
        """
        # Input is (batch_size, seq_length, dim)
        # Mask is (batch_size, 1, 1, key_length) (non-causal) or (batch_size, 1, seq_length, key_length) (causal decoder)
        batch_size, seq_length = hidden_states.shape[:2]

        # if key_value_states are provided this layer is used as a cross-attention layer for the decoder
        is_cross_attention = key_value_states is not None

        query_states = self.query(hidden_states)
        query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

        # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
        if past_key_values is not None and isinstance(past_key_values, EncoderDecoderCache):
            is_updated = past_key_values.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                curr_past_key_values = past_key_values.cross_attention_cache
            else:
                curr_past_key_values = past_key_values.self_attention_cache
        else:
            curr_past_key_values = past_key_values

        current_states = key_value_states if is_cross_attention else hidden_states
        if is_cross_attention and past_key_values and is_updated:
            # reuse k,v, cross_attentions
            key_states = curr_past_key_values.layers[self.layer_idx].keys
            value_states = curr_past_key_values.layers[self.layer_idx].values
        else:
            key_states = self.key(current_states)
            value_states = self.value(current_states)
            key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
            value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

            if past_key_values is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = curr_past_key_values.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )
                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
                if is_cross_attention:
                    past_key_values.is_updated[self.layer_idx] = True

        # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
        scores = torch.matmul(query_states, key_states.transpose(3, 2))

        if position_bias is None:
            key_length = key_states.shape[-2]
            # cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
            real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
            if not self.has_relative_attention_bias:
                position_bias = torch.zeros(
                    (1, self.n_heads, seq_length, key_length), device=scores.device, dtype=scores.dtype
                )
                if self.gradient_checkpointing and self.training:
                    position_bias.requires_grad = True
            else:
                position_bias = self.compute_bias(
                    real_seq_length, key_length, device=scores.device, cache_position=cache_position
                )
                position_bias = position_bias[:, :, -seq_length:, :]

            if mask is not None:
                causal_mask = mask[:, :, :, : key_states.shape[-2]]
                position_bias = position_bias + causal_mask

        position_bias_masked = position_bias
        scores += position_bias_masked

        # (batch_size, n_heads, seq_length, key_length)
        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.matmul(attn_weights, value_states)

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(batch_size, -1, self.inner_dim)
        attn_output = self.output(attn_output)

        outputs = (attn_output, position_bias)

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


# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5LayerNorm->Pix2StructLayerNorm,T5Attention->Pix2StructTextAttention,T5LayerSelfAttention->Pix2StructTextLayerSelfAttention,self.SelfAttention->self.attention,config.d_model->config.hidden_size
class Pix2StructTextLayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
        super().__init__()
        self.attention = Pix2StructTextAttention(
            config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
        )
        self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        past_key_values=None,
        use_cache=False,
        output_attentions=False,
        cache_position=None,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.attention(
            normed_hidden_states,
            mask=attention_mask,
            position_bias=position_bias,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
        return outputs


# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5LayerNorm->Pix2StructLayerNorm,T5Attention->Pix2StructTextAttention,T5LayerCrossAttention->Pix2StructTextLayerCrossAttention,self.EncDecAttention->self.attention,config.d_model->config.hidden_size
class Pix2StructTextLayerCrossAttention(nn.Module):
    def __init__(self, config, layer_idx: Optional[int] = None):
        super().__init__()
        self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
        self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        key_value_states,
        attention_mask=None,
        position_bias=None,
        past_key_values=None,
        use_cache=False,
        query_length=None,
        output_attentions=False,
        cache_position=None,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.attention(
            normed_hidden_states,
            mask=attention_mask,
            key_value_states=key_value_states,
            position_bias=position_bias,
            past_key_values=past_key_values,
            use_cache=use_cache,
            query_length=query_length,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


class Pix2StructTextBlock(GradientCheckpointingLayer):
    def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
        super().__init__()

        self.self_attention = Pix2StructTextLayerSelfAttention(
            config,
            has_relative_attention_bias=has_relative_attention_bias,
            layer_idx=layer_idx,
        )

        self.encoder_decoder_attention = Pix2StructTextLayerCrossAttention(
            config,
            layer_idx=layer_idx,
        )

        self.mlp = Pix2StructTextLayerFF(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        encoder_decoder_position_bias=None,
        past_key_values=None,
        use_cache=False,
        output_attentions=False,
        return_dict=True,
        cache_position=None,
    ):
        self_attention_outputs = self.self_attention(
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        hidden_states = self_attention_outputs[0]
        attention_outputs = self_attention_outputs[1:]  # Keep self-attention outputs and relative position weights

        # clamp inf values to enable fp16 training
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        do_cross_attention = encoder_hidden_states is not None
        if do_cross_attention:
            cross_attention_outputs = self.encoder_decoder_attention(
                hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                position_bias=encoder_decoder_position_bias,
                past_key_values=past_key_values,
                query_length=cache_position[-1] + 1,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            hidden_states = cross_attention_outputs[0]

            # clamp inf values to enable fp16 training
            if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

            # Keep cross-attention outputs and relative position weights
            attention_outputs = attention_outputs + cross_attention_outputs[1:]

        # Apply Feed Forward layer
        hidden_states = self.mlp(hidden_states)

        # clamp inf values to enable fp16 training
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        return outputs + attention_outputs


@auto_docstring(
    custom_intro="""
    The standalone text decoder of Pix2Struct
    """
)
class Pix2StructTextModel(Pix2StructPreTrainedModel):
    config: Pix2StructTextConfig
    input_modalities = "text"
    _no_split_modules = ["Pix2StructTextBlock"]
    _tied_weights_keys = ["lm_head.weight"]
    supports_gradient_checkpointing = True

    def __init__(self, config):
        super().__init__(config)
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)

        self.layer = nn.ModuleList(
            [
                Pix2StructTextBlock(config, has_relative_attention_bias=bool(i == 0), layer_idx=i)
                for i in range(config.num_layers)
            ]
        )
        self.final_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()
        self.gradient_checkpointing = False

    def set_input_embeddings(self, new_embeddings):
        self.embed_tokens = new_embeddings

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        labels: Optional[torch.LongTensor] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Union[tuple[torch.FloatTensor, ...], CausalLMOutputWithCrossAttentions]:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Pix2StructText is a model with relative position
            embeddings so you should be able to pad the inputs on both the right and the left.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.

            [What are input IDs?](../glossary#input-ids)

            To know more on how to prepare `input_ids` for pretraining take a look a [Pix2StructText
            Training](./t5#training).

        Example:

        ```python
        >>> from transformers import AutoProcessor, Pix2StructTextModel

        >>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
        >>> model = Pix2StructTextModel.from_pretrained("google/pix2struct-textcaps-base")

        >>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> loss = outputs.loss
        ```
        """
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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 self.gradient_checkpointing and self.training and use_cache:
            logger.warning(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
            )
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        if inputs_embeds is None:
            assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
            inputs_embeds = self.embed_tokens(input_ids)

        batch_size, seq_length = input_shape

        if use_cache and past_key_values is None:
            if self.config.is_encoder_decoder:
                past_key_values = EncoderDecoderCache(
                    DynamicCache(config=self.config), DynamicCache(config=self.config)
                )
            else:
                past_key_values = DynamicCache(config=self.config)

        past_key_values_length = 0
        if cache_position is not None:
            past_key_values_length = cache_position[0]
        elif past_key_values is not None:
            past_key_values_length = past_key_values.get_seq_length()

        if cache_position is None:
            cache_position = torch.arange(
                past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
            )

        if attention_mask is None:
            # required mask seq length can be calculated via length of past
            mask_seq_length = (
                past_key_values.get_seq_length() + seq_length if past_key_values is not None else seq_length
            )
            attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)

        if self.config.is_decoder:
            causal_mask = self._update_causal_mask(
                attention_mask,
                inputs_embeds,
                cache_position,
                past_key_values.self_attention_cache
                if isinstance(past_key_values, EncoderDecoderCache)
                else past_key_values,
                output_attentions,
            )
        else:
            causal_mask = attention_mask[:, None, None, :]
            causal_mask = causal_mask.to(dtype=inputs_embeds.dtype)
            causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if (output_attentions) else None
        position_bias = None
        encoder_decoder_position_bias = None

        hidden_states = self.dropout(inputs_embeds)

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

            layer_outputs = layer_module(
                hidden_states,
                causal_mask,
                position_bias,
                encoder_hidden_states,
                encoder_extended_attention_mask,
                encoder_decoder_position_bias,  # as a positional argument for gradient checkpointing
                past_key_values=past_key_values,
                use_cache=use_cache,
                output_attentions=output_attentions,
                cache_position=cache_position,
            )

            hidden_states = layer_outputs[0]

            # We share the position biases between the layers - the first layer store them
            # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
            # (cross-attention position bias), (cross-attention weights)
            position_bias = layer_outputs[1]
            if encoder_hidden_states is not None:
                encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[2],)
                if encoder_hidden_states is not None:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[4],)

        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        logits = self.lm_head(hidden_states)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        loss = None
        if labels is not None:
            # move labels to correct device
            labels = labels.to(logits.device)
            loss_fct = nn.CrossEntropyLoss(ignore_index=-100, reduction="mean")

            loss = loss_fct(logits.contiguous().view(-1, logits.size(-1)), labels.contiguous().view(-1))

        if not return_dict:
            return tuple(
                v
                for v in [
                    loss,
                    logits,
                    past_key_values,
                    all_hidden_states,
                    all_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=logits,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )

    # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
    def _update_causal_mask(
        self,
        attention_mask: Union[torch.Tensor, "BlockMask"],
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool = False,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and (attention_mask == 0.0).any():
                return attention_mask
            return None
        if self.config._attn_implementation == "flex_attention":
            if isinstance(attention_mask, torch.Tensor):
                attention_mask = make_flex_block_causal_mask(attention_mask)
            return attention_mask

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype = input_tensor.dtype
        sequence_length = input_tensor.shape[1]
        if using_compilable_cache:
            target_length = past_key_values.get_max_cache_shape()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type in ["cuda", "xpu", "npu"]
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            min_dtype = torch.finfo(dtype).min
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
    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


@auto_docstring(
    custom_intro="""
    A conditional generation model with a language modeling head. Can be used for sequence generation tasks.
    """
)
class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel, GenerationMixin):
    config: Pix2StructConfig
    main_input_name = "flattened_patches"
    _tied_weights_keys = ["decoder.lm_head.weight"]

    def __init__(self, config: Pix2StructConfig):
        super().__init__(config)

        self.encoder = Pix2StructVisionModel(config.vision_config)
        self.decoder = Pix2StructTextModel(config.text_config)

        self.is_vqa = config.is_vqa

        # Initialize weights and apply final processing
        self.post_init()

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

    def set_input_embeddings(self, new_embeddings):
        self.decoder.set_input_embeddings(new_embeddings)

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

    def set_output_embeddings(self, new_embeddings):
        self.decoder.set_output_embeddings(new_embeddings)

    def get_encoder(self):
        return self.encoder

    @auto_docstring
    def forward(
        self,
        flattened_patches: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Cache] = None,
        labels: Optional[torch.LongTensor] = None,
        decoder_inputs_embeds: Optional[torch.Tensor] = 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,
    ) -> Union[tuple[torch.FloatTensor], Seq2SeqModelOutput]:
        r"""
        flattened_patches (`torch.FloatTensor` of shape `(batch_size, seq_length, hidden_size)`):
            Flattened pixel patches. the `hidden_size` is obtained by the following formula: `hidden_size` =
            `num_channels` * `patch_size` * `patch_size`

            The process of flattening the pixel patches is done by `Pix2StructProcessor`.
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText
            Training](./t5#training).
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss for the decoder.

        Example:

        Inference:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration

        >>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
        >>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base")

        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(images=image, return_tensors="pt")

        >>> # autoregressive generation
        >>> generated_ids = model.generate(**inputs, max_new_tokens=50)
        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        >>> print(generated_text)
        A stop sign is on a street corner.

        >>> # conditional generation
        >>> text = "A picture of"
        >>> inputs = processor(text=text, images=image, return_tensors="pt", add_special_tokens=False)

        >>> generated_ids = model.generate(**inputs, max_new_tokens=50)
        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        >>> print(generated_text)
        A picture of a stop sign with a red stop sign
        ```

        Training:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration

        >>> processor = AutoProcessor.from_pretrained("google/pix2struct-base")
        >>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-base")

        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = "A stop sign is on the street corner."

        >>> inputs = processor(images=image, return_tensors="pt")
        >>> labels = processor(text=text, return_tensors="pt").input_ids

        >>> # forward pass
        >>> outputs = model(**inputs, labels=labels)
        >>> loss = outputs.loss
        >>> print(f"{loss.item():.5f}")
        5.94282
        ```"""
        use_cache = use_cache if use_cache is not None else self.config.text_config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                flattened_patches=flattened_patches,
                attention_mask=attention_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        hidden_states = encoder_outputs[0]

        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)
            decoder_attention_mask = (
                decoder_attention_mask
                if decoder_attention_mask is not None
                else decoder_input_ids.ne(self.config.pad_token_id).float()
            )
            # Always attend to the first token
            decoder_attention_mask[:, 0] = 1

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            labels=labels,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=decoder_outputs.loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


__all__ = [
    "Pix2StructPreTrainedModel",
    "Pix2StructForConditionalGeneration",
    "Pix2StructVisionModel",
    "Pix2StructTextModel",
]
