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

import math
from collections.abc import Callable
from dataclasses import dataclass

import numpy as np
import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...masking_utils import create_bidirectional_mask, create_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from .configuration_pegasus_x import PegasusXConfig


logger = logging.get_logger(__name__)


@dataclass
class DimensionInfo:
    """Wrapper for dimension info."""

    batch_size: int  # batch size
    seq_len: int  # token length
    block_size: int  # block size
    num_heads: int  # num heads
    hidden_dim: int  # hidden dim
    dim_per_head: int  # dim per head
    num_blocks: int  # num blocks
    global_len: int  # global length
    padded_seq_len: int  # padded token seq length


# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    """
    Shift input ids one token to the right.
    """
    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


# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->PegasusX
class PegasusXScaledWordEmbedding(nn.Embedding):
    """
    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.embed_scale = embed_scale

    def forward(self, input_ids: torch.Tensor):
        return super().forward(input_ids) * self.embed_scale


class PegasusXSinusoidalPositionalEmbedding(nn.Module):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, embed_dim, max_scale: int = 10000.0):
        super().__init__()
        self.embed_dim = embed_dim
        self.max_scale = max_scale

    @deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds")
    @torch.no_grad()
    def forward(
        self, inputs_embeds: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None
    ) -> torch.Tensor:
        """`input_ids_shape` is expected to be [bsz x seqlen]."""
        batch_size, seq_len = inputs_embeds.shape[:2]
        if position_ids is None:
            position_ids = torch.arange(
                past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=inputs_embeds.device
            )[:, None]

        pe = torch.zeros((seq_len, self.embed_dim), device=inputs_embeds.device, dtype=inputs_embeds.dtype)
        half_d_feature = self.embed_dim // 2
        div_term = torch.exp(
            torch.arange(half_d_feature, device=inputs_embeds.device, dtype=torch.int64).type_as(inputs_embeds)
            * -(np.log(float(self.max_scale)) / (half_d_feature - 1))
        )
        pe[:, :half_d_feature] = torch.sin(position_ids * div_term)
        pe[:, half_d_feature:] = torch.cos(position_ids * div_term)
        return pe[None].expand(batch_size, -1, -1)


# Copied from transformers.models.bert.modeling_bert.eager_attention_forward
def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float | None = None,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    if scaling is None:
        scaling = query.size(-1) ** -0.5

    # Take the dot product between "query" and "key" to get the raw attention scores.
    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling

    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->PegasusX
class PegasusXAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
        is_causal: bool = False,
        config: PegasusXConfig | None = None,
        layer_idx: int | None = None,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal
        self.layer_idx = layer_idx
        if layer_idx is None and self.is_decoder:
            logger.warning_once(
                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
                "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        attention_mask: torch.Tensor | None = None,
        # TODO: we need a refactor so that the different attention modules can get their specific kwargs
        # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        """Input shape: Batch x Time x Channel"""

        # 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

        # determine input shapes
        bsz, tgt_len = hidden_states.shape[:-1]
        src_len = key_value_states.shape[1] if is_cross_attention else tgt_len

        q_input_shape = (bsz, tgt_len, -1, self.head_dim)
        kv_input_shape = (bsz, src_len, -1, self.head_dim)

        # get query proj
        query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)

        is_updated = False
        if past_key_values is not None:
            if 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 is not None 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.k_proj(current_states)
            value_states = self.v_proj(current_states)
            key_states = key_states.view(*kv_input_shape).transpose(1, 2)
            value_states = value_states.view(*kv_input_shape).transpose(1, 2)

            if past_key_values is not None:
                key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx)
                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
                if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache):
                    past_key_values.is_updated[self.layer_idx] = True

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights


class PegasusXGlobalLocalAttention(nn.Module):
    """Global + Local attention. For use with Encoder only."""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        block_size: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.block_size = block_size
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        token_hidden_states: torch.Tensor,
        global_hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
        """Input shape: Batch x Time x Channel"""
        dim = DimensionInfo(
            batch_size=token_hidden_states.shape[0],
            seq_len=token_hidden_states.shape[1],
            block_size=self.block_size,
            num_heads=self.num_heads,
            hidden_dim=token_hidden_states.shape[2],
            dim_per_head=self.head_dim,
            num_blocks=token_hidden_states.shape[1] // self.block_size,
            global_len=global_hidden_states.shape[1],
            padded_seq_len=token_hidden_states.shape[1],
        )

        # [batch_size, num_heads, padded_seq_len, dim_per_head]
        local_q = self._shape(
            self.q_proj(token_hidden_states) * self.scaling,
            seq_len=dim.padded_seq_len,
            bsz=dim.batch_size,
        )
        local_k = self._shape(
            self.k_proj(token_hidden_states),
            seq_len=dim.padded_seq_len,
            bsz=dim.batch_size,
        )
        local_v = self._shape(
            self.v_proj(token_hidden_states),
            seq_len=dim.padded_seq_len,
            bsz=dim.batch_size,
        )

        # [batch_size, num_heads, global_len, dim_per_head]
        global_q = self._shape(
            self.q_proj(global_hidden_states) * self.scaling,
            seq_len=dim.global_len,
            bsz=dim.batch_size,
        )
        global_k = self._shape(
            self.k_proj(global_hidden_states),
            seq_len=dim.global_len,
            bsz=dim.batch_size,
        )
        global_v = self._shape(
            self.v_proj(global_hidden_states),
            seq_len=dim.global_len,
            bsz=dim.batch_size,
        )

        global_attn_output, global_attn_probs = self.compute_global_attention_representations(
            global_q=global_q,
            global_k=global_k,
            global_v=global_v,
            local_k=local_k,
            local_v=local_v,
            mask=attention_mask,
            dim=dim,
        )
        local_attn_output, local_attn_probs = self.compute_local_attention_representations(
            global_k=global_k,
            global_v=global_v,
            local_q=local_q,
            local_k=local_k,
            local_v=local_v,
            mask=attention_mask,
            dim=dim,
        )

        # [batch_size, global_len, hidden_dim]
        global_attn_output = (
            global_attn_output.transpose(1, 2).contiguous().view(dim.batch_size, dim.global_len, dim.hidden_dim)
        )
        # [batch_size, global_len, hidden_dim]
        global_attn_output = self.out_proj(global_attn_output)
        # [batch_size, num_heads, block_size, num_heads, dim_per_head]
        local_attn_output = local_attn_output.permute(0, 2, 3, 1, 4).contiguous()
        # [batch_size, padded_seq_len, hidden_dim]
        local_attn_output = local_attn_output.view(dim.batch_size, dim.padded_seq_len, dim.hidden_dim)
        # [batch_size, padded_seq_len, hidden_dim]
        local_attn_output = self.out_proj(local_attn_output)

        attn_probs = {"global": global_attn_probs, "local": local_attn_probs}

        return local_attn_output, global_attn_output, attn_probs

    def compute_global_attention_representations(
        self, global_q, global_k, global_v, local_k, local_v, mask, dim: DimensionInfo
    ):
        """Compute attention representations for global tokens.

        Global tokens will attend to both global tokens as well as all input sequence tokens. Because the input
        sequence tokens are arranged in blocks for local attention, we unblock them and compute attention.

        Args:
            global_q (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
                query vectors from global tokens
            global_k (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
                key vectors from global tokens
            global_v (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
                value vectors from global tokens
            local_k (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
                key vectors from local tokens
            local_v (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
                value vectors from local tokens
            mask (`torch.FloatTensor`) of shape [batch_size, padded_seq_len]: attention mask
            dim (DimensionInfo): DimensionInfo wrapper for dimensions

        Returns:
            output of shape `[batch_sizes, length, features]`. where length will be padded to a multiple of block_size
        """
        # [batch_size, num_heads, global_len+padded_seq_len, dim_per_head]
        global_and_local_k = torch.cat([global_k, local_k], dim=2)
        # [batch_size, num_heads, global_len+padded_seq_len, dim_per_head]
        global_and_local_v = torch.cat([global_v, local_v], dim=2)

        # [batch_size, global_len+padded_seq_len]
        extended_mask = nn.functional.pad(mask, pad=(dim.global_len, 0), value=0)

        # [batch_size, num_heads, global_len, global_len+padded_seq_len]
        attn_weights = torch.einsum("BHGF,BHXF->BHGX", global_q, global_and_local_k)
        attn_weights = attn_weights + extended_mask[:, None, None, :]
        attn_probs = nn.functional.softmax(attn_weights, dim=-1)
        attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)

        # [batch_size, num_heads, global_len, F]
        attn_output = torch.einsum("BHGX,BHXF->BHGF", attn_probs, global_and_local_v)
        return attn_output, attn_probs

    def compute_local_attention_representations(
        self, global_k, global_v, local_q, local_k, local_v, mask, dim: DimensionInfo
    ):
        """Compute attention representations for local tokens.

        Local tokens will attend to both global tokens as well as all other tokens within the same local block. Hence,
        we need to tile and concatenate the global tokens to every local block

        Args:
            global_k (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
                key vectors from global tokens
            global_v (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]:
                value vectors from global tokens
            local_q (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
                query vectors from local tokens
            local_k (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
                key vectors from local tokens
            local_v (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]:
                value vectors from local tokens
            mask (`torch.FloatTensor`) of shape [batch_size, padded_seq_len]: attention mask
            dim (DimensionInfo): DimensionInfo wrapper for dimensions

        Returns:
            output of shape `[batch_sizes, length, features]`. where length will be padded to a multiple of block_size
        """
        # [batch_size, num_heads, num_blocks, block_size, dim_per_head]
        blocked_local_q = local_q.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head)
        # [batch_size, num_heads, num_blocks, block_size, dim_per_head]
        blocked_local_k = local_k.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head)
        # [batch_size, num_heads, num_blocks, block_size, dim_per_head]
        blocked_local_v = local_v.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head)

        # [batch_size, num_blocks, global_len+block_size]
        extended_mask = nn.functional.pad(
            mask.view(dim.batch_size, dim.num_blocks, dim.block_size),
            pad=(dim.global_len, 0),
            value=0,
        )

        # [batch_size, num_heads, num_blocks, block_size, global_len]
        blocked_local2global = torch.einsum("BHNKF,BHGF->BHNKG", blocked_local_q, global_k)
        # [batch_size, num_heads, num_blocks, block_size, block_size]
        blocked_local2local = torch.einsum("BHNKF,BHNXF->BHNKX", blocked_local_q, blocked_local_k)

        # [batch_size, num_heads, num_blocks, block_size, global_len+block_size]
        attn_weights = torch.cat([blocked_local2global, blocked_local2local], dim=-1)
        attn_weights = attn_weights + extended_mask[:, None, :, None, :]
        attn_probs = nn.functional.softmax(attn_weights, dim=-1)
        attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)

        # [batch_size, num_heads, num_blocks, block_size, global_len]
        local2global_attn_probs = attn_probs[:, :, :, :, : dim.global_len]
        # [batch_size, num_heads, num_blocks, block_size, block_size]
        local2local_attn_probs = attn_probs[:, :, :, :, dim.global_len :]

        # [batch_size, num_heads, num_blocks, block_size, dim_per_head]
        local2global_attn_output = torch.einsum("BHNKG,BHGF->BHNKF", local2global_attn_probs, global_v)
        # [batch_size, num_heads, num_blocks, block_size, dim_per_head]
        local2local_attn_output = torch.einsum("BHNKX,BHNXF->BHNKF", local2local_attn_probs, blocked_local_v)
        # [batch_size, num_heads, num_blocks, block_size, dim_per_head]
        attn_output = local2global_attn_output + local2local_attn_output
        return attn_output, attn_probs


class PegasusXEncoderLayer(GradientCheckpointingLayer):
    def __init__(self, stagger_blocks_this_layer: bool, config: PegasusXConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = PegasusXGlobalLocalAttention(
            embed_dim=self.embed_dim,
            num_heads=config.encoder_attention_heads,
            block_size=config.block_size,
            dropout=config.attention_dropout,
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.global_self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
        self.stagger_blocks_this_layer = stagger_blocks_this_layer
        self.block_size = config.block_size

    def forward(
        self,
        hidden_states: torch.Tensor,
        global_hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
            global_hidden_states (`torch.FloatTensor`): global token hidden states
                *(seq_len, num_global_tokens, embed_dim)*
            attention_mask (`torch.FloatTensor`): attention mask of size
                *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        global_residual = global_hidden_states

        hidden_states = self.self_attn_layer_norm(hidden_states)
        global_hidden_states = self.global_self_attn_layer_norm(global_hidden_states)

        if self.stagger_blocks_this_layer:
            # Pad the blocks to simulate staggering
            hidden_states, attention_mask = self.pad_local_tokens(
                hidden_states=hidden_states, attention_mask=attention_mask, block_size=self.block_size
            )

        hidden_states, global_hidden_states, _ = self.self_attn(
            token_hidden_states=hidden_states,
            global_hidden_states=global_hidden_states,
            attention_mask=attention_mask,
            **kwargs,
        )

        if self.stagger_blocks_this_layer:
            # Undo the padding
            hidden_states = self.unpad_local_tokens(padded_hidden_states=hidden_states, block_size=self.block_size)

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        global_hidden_states = nn.functional.dropout(global_hidden_states, p=self.dropout, training=self.training)
        global_hidden_states = global_residual + global_hidden_states

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        global_residual = global_hidden_states
        global_hidden_states = self.final_layer_norm(global_hidden_states)
        global_hidden_states = self.activation_fn(self.fc1(global_hidden_states))
        global_hidden_states = nn.functional.dropout(
            global_hidden_states, p=self.activation_dropout, training=self.training
        )
        global_hidden_states = self.fc2(global_hidden_states)
        global_hidden_states = nn.functional.dropout(global_hidden_states, p=self.dropout, training=self.training)
        global_hidden_states = global_residual + global_hidden_states

        return hidden_states, global_hidden_states

    @classmethod
    def pad_local_tokens(cls, hidden_states, attention_mask, block_size):
        # hidden_states: [batch_size, seq_len, hidden_dim]
        pad_size = block_size // 2
        mask_min_value = torch.finfo(hidden_states.dtype).min
        padded_hidden_states = torch.nn.functional.pad(
            hidden_states,
            pad=(0, 0, pad_size, pad_size),
        )
        padded_mask = torch.nn.functional.pad(
            attention_mask,
            pad=(pad_size, pad_size),
            value=mask_min_value,
        )
        return padded_hidden_states, padded_mask

    @classmethod
    def unpad_local_tokens(cls, padded_hidden_states, block_size):
        # padded_hidden_states: [batch_size, padded seq_len, hidden_dim]
        pad_size = block_size // 2
        return padded_hidden_states[:, pad_size:-pad_size, :]


class PegasusXDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: PegasusXConfig, layer_idx: int | None = None):
        super().__init__()
        self.embed_dim = config.d_model

        self.self_attn = PegasusXAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            bias=False,
            config=config,
            layer_idx=layer_idx,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = PegasusXAttention(
            self.embed_dim,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            bias=False,
            config=config,
            layer_idx=layer_idx,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        encoder_hidden_states: torch.Tensor | None = None,
        encoder_attention_mask: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = True,
        **kwargs,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
            attention_mask (`torch.FloatTensor`): attention mask of size
                *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape *(seq_len, batch, embed_dim)*
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
            past_key_values (`Cache`): cached past key and value projection states
            use_cache: Whether to us KV cache for decoding
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            **kwargs,
        )
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        # Cross-Attention Block
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)

            hidden_states, _ = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                past_key_values=past_key_values,
                **kwargs,
            )
            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        return hidden_states


@auto_docstring
class PegasusXPreTrainedModel(PreTrainedModel):
    config: PegasusXConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = [r"PegasusXEncoderLayer", r"PegasusXDecoderLayer"]
    _supports_flash_attn = True
    # Flaky logits
    _supports_sdpa = False
    _supports_flex_attn = True
    _can_compile_fullgraph = True


class PegasusXEncoder(PegasusXPreTrainedModel):
    _can_record_outputs = {"attentions": OutputRecorder(PegasusXGlobalLocalAttention, index=2)}

    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    [`PegasusXEncoderLayer`].

    Args:
        config: PegasusXConfig
        embed_tokens (nn.Embedding): output embedding
    """

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

        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop

        embed_dim = config.d_model
        padding_idx = config.pad_token_id
        self.max_source_positions = config.max_position_embeddings
        embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

        self.embed_tokens = PegasusXScaledWordEmbedding(
            config.vocab_size, embed_dim, padding_idx, embed_scale=embed_scale
        )

        self.embed_global = nn.Embedding(config.num_global_tokens, embed_dim)
        self.embed_positions = PegasusXSinusoidalPositionalEmbedding(embed_dim)
        self.layers = nn.ModuleList(
            [
                PegasusXEncoderLayer(
                    stagger_blocks_this_layer=i % 2 == 1 and config.stagger_local_blocks, config=config
                )
                for i in range(config.encoder_layers)
            ]
        )
        self.layer_norm = nn.LayerNorm(config.d_model)

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

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
        config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embeddings. If position embeddings are learned, increasing the size will add
                newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
                position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
                add correct vectors at the end following the position encoding algorithm, whereas reducing the size
                will remove vectors from the end.
        """
        logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...")
        self.config.max_position_embeddings = new_num_position_embeddings

        self.embed_positions = PegasusXSinusoidalPositionalEmbedding(self.config.d_model)
        self.embed_positions.to(self.device)

    def get_position_embeddings(self) -> nn.Embedding:
        """
        Returns the position embeddings matrix
        """
        return self.embed_positions

    @merge_with_config_defaults
    @capture_outputs
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        output_hidden_states=None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

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

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)

            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
        """
        # We need to treat this special because it only adds the last global state which is unique
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

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

        embed_pos = self.embed_positions(inputs_embeds)

        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        batch_size, seq_len, _ = hidden_states.shape

        # Setup mask
        if attention_mask is None:
            attention_mask = torch.ones(
                *inputs_embeds.shape[:-1], dtype=inputs_embeds.dtype, device=inputs_embeds.device
            )
        attention_mask = attention_mask.to(dtype=hidden_states.dtype)
        mask_min_value = torch.finfo(hidden_states.dtype).min
        inverted_mask = 1.0 - attention_mask
        attention_mask = inverted_mask.masked_fill(
            inverted_mask.to(torch.bool),
            mask_min_value,
        )

        # padding to block_size
        if seq_len % self.config.block_size != 0:
            pad_len = self.config.block_size - seq_len % self.config.block_size
            hidden_states = nn.functional.pad(hidden_states, pad=(0, 0, 0, pad_len), value=0)
            attention_mask = nn.functional.pad(attention_mask, pad=(0, pad_len), value=mask_min_value)

        # Global tokens
        global_hidden_states = self.embed_global(
            torch.arange(self.config.num_global_tokens, device=hidden_states.device)[None].expand(batch_size, -1)
        )

        encoder_states = () if output_hidden_states else None
        for encoder_layer in self.layers:
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:  # skip the layer
                    to_drop = True

            if to_drop:
                hidden_states, global_hidden_states = (None, None)
            else:
                hidden_states, global_hidden_states = encoder_layer(
                    hidden_states,
                    global_hidden_states,
                    attention_mask,
                    **kwargs,
                )

        # Undo padding-to-block-size
        hidden_states = hidden_states[:, :seq_len]

        hidden_states = self.layer_norm(hidden_states)

        if output_hidden_states:
            encoder_states = encoder_states + ((hidden_states, global_hidden_states),)

        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states)


class PegasusXDecoder(PegasusXPreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PegasusDecoderLayer`]

    Args:
        config: PegasusXConfig
        embed_tokens (nn.Embedding): output embedding
    """

    _can_record_outputs = {
        "hidden_states": PegasusXDecoderLayer,
        "attentions": OutputRecorder(PegasusXAttention, index=1, layer_name="self_attn"),
        "cross_attentions": OutputRecorder(PegasusXAttention, index=1, layer_name="encoder_attn"),
    }

    def __init__(self, config: PegasusXConfig):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.max_target_positions = config.max_position_embeddings
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        padding_idx = config.pad_token_id

        self.embed_tokens = PegasusXScaledWordEmbedding(
            config.vocab_size, config.d_model, padding_idx=padding_idx, embed_scale=embed_scale
        )

        self.embed_positions = PegasusXSinusoidalPositionalEmbedding(config.d_model)
        self.layers = nn.ModuleList([PegasusXDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
        self.layer_norm = nn.LayerNorm(config.d_model)

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

    @capture_outputs
    @merge_with_config_defaults
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        use_cache=None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

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

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
                selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)

            past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            inputs_embeds (`torch.FloatTensor` of
                shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
                `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
                control over how to convert `input_ids` indices into associated vectors than the model's internal
                embedding lookup matrix.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")

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

        # initialize `past_key_values`
        if use_cache and past_key_values is None:
            past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))

        batch_size, seq_length = inputs_embeds.size()[:-1]
        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0

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

        self_attn_cache = (
            past_key_values.self_attention_cache
            if isinstance(past_key_values, EncoderDecoderCache)
            else past_key_values
        )

        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            past_key_values=self_attn_cache,
        )
        encoder_attention_mask = create_bidirectional_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=encoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
        )

        # embed positions
        position_ids = torch.arange(seq_length, device=inputs_embeds.device) + past_key_values_length
        position_ids = position_ids.unsqueeze(1)
        position_ids = self.embed_positions(inputs_embeds, past_key_values_length, position_ids)
        position_ids = position_ids.to(inputs_embeds.device)
        hidden_states = inputs_embeds + position_ids
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        # decoder layers
        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:
                    continue

            hidden_states = decoder_layer(
                hidden_states,
                causal_mask,
                encoder_hidden_states,  # as a positional argument for gradient checkpointing
                encoder_attention_mask=encoder_attention_mask,
                past_key_values=past_key_values,
                use_cache=use_cache,
                **kwargs,
            )

        hidden_states = self.layer_norm(hidden_states)

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


@auto_docstring
class PegasusXModel(PegasusXPreTrainedModel):
    _tied_weights_keys = {
        "encoder.embed_tokens.weight": "shared.weight",
        "decoder.embed_tokens.weight": "shared.weight",
    }

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

        vocab_size = config.vocab_size
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        padding_idx = config.pad_token_id
        self.shared = PegasusXScaledWordEmbedding(
            vocab_size, config.d_model, padding_idx=padding_idx, embed_scale=embed_scale
        )

        self.encoder = PegasusXEncoder(config)
        self.decoder = PegasusXDecoder(config)

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

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
        config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embeddings. If position embeddings are learned, increasing the size will add
                newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
                position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
                add correct vectors at the end following the position encoding algorithm, whereas reducing the size
                will remove vectors from the end.
        """
        self.config.max_position_embeddings = new_num_position_embeddings
        self.encoder.resize_position_embeddings(new_num_position_embeddings)
        self.decoder.resize_position_embeddings(new_num_position_embeddings)

    def get_position_embeddings(self) -> tuple[nn.Embedding]:
        """
        Returns the position embeddings matrix
        """
        return (self.encoder.get_position_embeddings(), self.decoder.get_position_embeddings())

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        decoder_input_ids: torch.Tensor | None = None,
        decoder_attention_mask: torch.Tensor | None = None,
        encoder_outputs: tuple[torch.FloatTensor] | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.Tensor | None = None,
        decoder_inputs_embeds: torch.Tensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | Seq2SeqModelOutput:
        r"""
        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)

            PEGASUS-X 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`).
        decoder_attention_mask (`torch.LongTensor` 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.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, PegasusModel

        >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-x-large")
        >>> model = PegasusModel.from_pretrained("google/pegasus-x-large")

        >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
        >>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt")
        >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 4, 1024]
        ```"""

        if encoder_outputs is None:
            encoder_outputs: BaseModelOutput = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                **kwargs,
            )
        elif 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,
            )

        decoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            **kwargs,
        )

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            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,
        )


@auto_docstring(
    custom_intro="""
    The PEGASUS-X for conditional generation (e.g. summarization).
    """
)
class PegasusXForConditionalGeneration(PegasusXPreTrainedModel, GenerationMixin):
    base_model_prefix = "model"
    _tied_weights_keys = {
        "lm_head.weight": "model.shared.weight",
    }

    def __init__(self, config: PegasusXConfig):
        super().__init__(config)
        self.model = PegasusXModel(config)
        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)

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

    def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
        config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embeddings. If position embeddings are learned, increasing the size will add
                newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
                position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
                add correct vectors at the end following the position encoding algorithm, whereas reducing the size
                will remove vectors from the end.
        """
        self.config.max_position_embeddings = new_num_position_embeddings
        self.model.encoder.resize_position_embeddings(new_num_position_embeddings)
        self.model.decoder.resize_position_embeddings(new_num_position_embeddings)

    def get_position_embeddings(self) -> tuple[nn.Embedding]:
        """
        Returns the position embeddings matrix
        """
        return (self.model.encoder.get_position_embeddings(), self.model.decoder.get_position_embeddings())

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        decoder_input_ids: torch.Tensor | None = None,
        decoder_attention_mask: torch.Tensor | None = None,
        encoder_outputs: tuple[torch.FloatTensor] | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.Tensor | None = None,
        decoder_inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | Seq2SeqLMOutput:
        r"""
        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)

            PEGASUS-X 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`).
        decoder_attention_mask (`torch.LongTensor` 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. 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]`.
        """
        if labels is not None:
            if use_cache:
                logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
            use_cache = False
            if decoder_input_ids is None and decoder_inputs_embeds is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )

        outputs: Seq2SeqModelOutput = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            **kwargs,
        )
        lm_logits = self.lm_head(outputs.last_hidden_state)

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))

        return Seq2SeqLMOutput(
            loss=masked_lm_loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)


# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->PegasusX
class PegasusXDecoderWrapper(PegasusXPreTrainedModel):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
    used in combination with the [`EncoderDecoderModel`] framework.
    """

    def __init__(self, config):
        super().__init__(config)
        self.decoder = PegasusXDecoder(config)
        self.post_init()

    def forward(self, *args, **kwargs):
        return self.decoder(*args, **kwargs)


__all__ = ["PegasusXForConditionalGeneration", "PegasusXModel", "PegasusXPreTrainedModel"]
