# coding=utf-8
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The Salesforce Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the BSD-3-clause license (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import inspect
import math
from functools import partial
from typing import Callable, Optional, Tuple

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.distributed.fleet.utils import recompute

from ...utils.initializer import normal_, ones_, zeros_
from ...utils.log import logger
from ..activations import ACT2FN
from ..model_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
)
from ..model_utils import PretrainedModel
from .configuration import BlipTextConfig

__all__ = [
    "BlipTextPretrainedModel",
    "BlipTextModel",
    "BlipTextLMHeadModel",
]


def apply_chunking_to_forward(
    forward_fn: Callable[..., paddle.Tensor], chunk_size: int, chunk_dim: int, *input_tensors
) -> paddle.Tensor:
    """
    This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension
    `chunk_dim`. It then applies a layer `forward_fn` to each chunk independently to save memory.

    If the `forward_fn` is independent across the `chunk_dim` this function will yield the same result as directly
    applying `forward_fn` to `input_tensors`.

    Args:
        forward_fn (`Callable[..., paddle.Tensor]`):
            The forward function of the model.
        chunk_size (`int`):
            The chunk size of a chunked tensor: `num_chunks = len(input_tensors[0]) / chunk_size`.
        chunk_dim (`int`):
            The dimension over which the `input_tensors` should be chunked.
        input_tensors (`Tuple[paddle.Tensor]`):
            The input tensors of `forward_fn` which will be chunked

    Returns:
        `paddle.Tensor`: A tensor with the same shape as the `forward_fn` would have given if applied`.


    Examples:

    ```python
    # rename the usual forward() fn to forward_chunk()
    def forward_chunk(self, hidden_states):
        hidden_states = self.decoder(hidden_states)
        return hidden_states


    # implement a chunked forward function
    def forward(self, hidden_states):
        return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
    ```"""

    assert len(input_tensors) > 0, f"{input_tensors} has to be a tuple/list of tensors"

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
    if num_args_in_forward_chunk_fn != len(input_tensors):
        raise ValueError(
            f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(input_tensors)} input "
            "tensors are given"
        )

    if chunk_size > 0:
        tensor_shape = input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"All input tenors have to be of the same shape: {tensor_shape}, "
                    f"found shape {input_tensor.shape[chunk_dim]}"
                )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
                f"size {chunk_size}"
            )

        num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, axis=chunk_dim) for input_tensor in input_tensors)
        # apply forward fn to every tuple
        output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
        # concatenate output at same dimension
        return paddle.concat(output_chunks, axis=chunk_dim)

    return forward_fn(*input_tensors)


# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L52
class BlipTextEmbeddings(nn.Layer):
    """Construct the embeddings from word and position embeddings."""

    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.word_embeddings = nn.Embedding(
            config.vocab_size, config.hidden_size
        )  # , padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", paddle.arange(config.max_position_embeddings, dtype="int64").reshape((1, -1))
        )
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")

        self.config = config

    def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
        if input_ids is not None:
            input_shape = input_ids.shape
        else:
            input_shape = inputs_embeds.shape[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]

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

        embeddings = inputs_embeds

        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L97
class BlipTextSelfAttention(nn.Layer):
    def __init__(self, config: BlipTextConfig, is_cross_attention: bool = False):
        super().__init__()
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention heads (%d)"
                % (config.hidden_size, config.num_attention_heads)
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.scale = math.sqrt(self.attention_head_size)
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        if is_cross_attention:
            self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)
            self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)
        else:
            self.key = nn.Linear(config.hidden_size, self.all_head_size)
            self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

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

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = paddle.concat([past_key_value[0], key_layer], axis=2)
            value_layer = paddle.concat([past_key_value[1], value_layer], axis=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = paddle.matmul(query_layer, key_layer, transpose_y=True)

        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            seq_length = hidden_states.shape[1]
            position_ids_l = paddle.arange(seq_length, dtype=paddle.int64).reshape([-1, 1])
            position_ids_r = paddle.arange(seq_length, dtype=paddle.int64).reshape([1, -1])
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.cast(query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = paddle.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = paddle.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = paddle.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

        attention_scores = attention_scores / self.scale
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BlipTextModel forward() function)
            attention_scores = attention_scores + attention_mask

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

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

        context_layer = paddle.matmul(attention_probs_dropped, value_layer)

        context_layer = context_layer.transpose([0, 2, 1, 3])
        new_context_layer_shape = context_layer.shape[:-2] + [
            self.all_head_size,
        ]
        context_layer = context_layer.reshape(new_context_layer_shape)

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

        outputs = outputs + (past_key_value,)
        return outputs


# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert -> BlipText
class BlipTextSelfOutput(nn.Layer):
    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#242
class BlipTextAttention(nn.Layer):
    def __init__(self, config: BlipTextConfig, is_cross_attention: bool = False):
        super().__init__()
        self.self = BlipTextSelfAttention(config, is_cross_attention)
        self.output = BlipTextSelfOutput(config)
        self.pruned_heads = set()

    def forward(
        self,
        hidden_states: paddle.Tensor,
        attention_mask: Optional[paddle.Tensor] = None,
        encoder_hidden_states: Optional[paddle.Tensor] = None,
        encoder_attention_mask: Optional[paddle.Tensor] = None,
        past_key_value: Optional[Tuple[Tuple[paddle.Tensor]]] = None,
        output_attentions: Optional[bool] = False,
    ):
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert -> BlipText
class BlipTextIntermediate(nn.Layer):
    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert -> BlipText
class BlipTextOutput(nn.Layer):
    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BlipTextLayer(nn.Layer):
    def __init__(self, config: BlipTextConfig, layer_num: int):
        super().__init__()
        self.config = config
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = BlipTextAttention(config)
        self.layer_num = layer_num
        if self.config.is_decoder:
            self.crossattention = BlipTextAttention(config, is_cross_attention=self.config.is_decoder)
        self.intermediate = BlipTextIntermediate(config)
        self.output = BlipTextOutput(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
        )
        attention_output = self_attention_outputs[0]

        outputs = self_attention_outputs[1:-1]
        present_key_value = self_attention_outputs[-1]

        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
        )
        outputs = (layer_output,) + outputs

        outputs = outputs + (present_key_value,)

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L386
class BlipTextEncoder(nn.Layer):
    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.config = config
        self.layer = nn.LayerList([BlipTextLayer(config, i) for i in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.is_decoder else None

        next_decoder_cache = () if use_cache else None

        for i in range(self.config.num_hidden_layers):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            past_key_value = past_key_values[i] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                if use_cache:
                    logger.warning(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = recompute(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                )

            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)
            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,
                    next_decoder_cache,
                    all_hidden_states,
                    all_self_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->BlipText
class BlipTextPooler(nn.Layer):
    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

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


# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->BlipText
class BlipTextPredictionHeadTransform(nn.Layer):
    def __init__(self, config: BlipTextConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)

    def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->BlipText
class BlipTextLMPredictionHead(nn.Layer):
    def __init__(self, config: BlipTextConfig, embedding_weights=None):
        super().__init__()
        self.transform = BlipTextPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder_weight = (
            self.create_parameter(
                shape=[config.vocab_size, config.hidden_size], dtype=self.transform.weight.dtype, is_bias=False
            )
            if embedding_weights is None
            else embedding_weights
        )

        self.bias = self.create_parameter(
            shape=[
                config.vocab_size,
            ],
            dtype=self.decoder_weight.dtype,
            is_bias=True,
        )

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = paddle.matmul(hidden_states, self.decoder_weight, transpose_y=True) + self.bias
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->BlipText
class BlipTextOnlyMLMHead(nn.Layer):
    """
    Perform language modeling task.

    Args:
        config (:class:`BlipTextConfig`):
            An instance of BlipTextConfig used to construct BlipTextLMHeadModel.
        embedding_weights (Tensor, optional):
            Decoding weights used to map hidden_states to logits of the masked token prediction.
            Its data type should be float32 and its shape is [vocab_size, hidden_size].
            Defaults to `None`, which means use the same weights of the embedding layer.

    """

    def __init__(self, config: BlipTextConfig, embedding_weights=None):
        super().__init__()
        self.predictions = BlipTextLMPredictionHead(config, embedding_weights)

    def forward(self, sequence_output: paddle.Tensor) -> paddle.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L548
class BlipTextPretrainedModel(PretrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BlipTextConfig
    base_model_prefix = "bert"
    _keys_to_ignore_on_load_missing = [r"position_ids"]
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            normal_(module.weight, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            zeros_(module.bias)
            ones_(module.weight)
        if isinstance(module, nn.Linear) and module.bias is not None:
            zeros_(module.bias)

    def gradient_checkpointing_enable(self):
        """
        Activates gradient checkpointing for the current model.

        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".
        """
        if not self.supports_gradient_checkpointing:
            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
        self.apply(partial(self._set_gradient_checkpointing, value=True))

    def gradient_checkpointing_disable(self):
        """
        Deactivates gradient checkpointing for the current model.

        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".
        """
        if self.supports_gradient_checkpointing:
            self.apply(partial(self._set_gradient_checkpointing, value=False))

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, BlipTextEncoder):
            module.gradient_checkpointing = value


# Adapted from https://github.com/salesforce/BLIP/blob/3a29b7410476bf5f2ba0955827390eb6ea1f4f9d/models/med.py#L571
class BlipTextModel(BlipTextPretrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in [Attention is
    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `is_decoder` set to `True`; an
    `encoder_hidden_states` is then expected as an input to the forward pass.

    This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
    Refer to the superclass documentation for the generic methods.

    This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
    /docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
    and refer to the Paddle documentation for all matter related to general usage and behavior.

    Args:
        config (:class:`BlipTextConfig`):
            An instance of BlipTextConfig used to construct BlipTextModel.
    """

    def __init__(self, config: BlipTextConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.config = config

        self.embeddings = BlipTextEmbeddings(config)
        self.encoder = BlipTextEncoder(config)
        self.pooler = BlipTextPooler(config) if add_pooling_layer else None

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

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    @property
    def dtype(self):
        return self.embeddings.word_embeddings.weight.dtype

    def get_extended_attention_mask(
        self, attention_mask: paddle.Tensor, input_shape: Tuple[int], is_decoder: bool
    ) -> paddle.Tensor:
        if attention_mask.ndim == 3:
            extended_attention_mask = attention_mask.unsqueeze(1)
        elif attention_mask.ndim == 2:
            # Provided a padding mask of dimensions [batch_size, seq_length]
            # - if the model is a decoder, apply a causal mask in addition to the padding mask
            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
            if is_decoder:
                batch_size, seq_length = input_shape
                seq_ids = paddle.arange(seq_length)
                causal_mask = paddle.tile(
                    seq_ids.unsqueeze(axis=[0, 1]), [batch_size, seq_length, 1]
                ) <= seq_ids.unsqueeze(axis=[0, 2])
                causal_mask = causal_mask.cast(attention_mask.dtype)

                if causal_mask.shape[1] < attention_mask.shape[1]:
                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
                    causal_mask = paddle.concat(
                        [
                            paddle.ones(
                                [batch_size, seq_length, prefix_seq_len],
                                dtype=causal_mask.dtype,
                            ),
                            causal_mask,
                        ],
                        axis=-1,
                    )

                extended_attention_mask = causal_mask.unsqueeze(1) * attention_mask.unsqueeze([1, 2])
            else:
                extended_attention_mask = attention_mask.unsqueeze([1, 2])
        else:
            raise ValueError(
                f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
            )

        extended_attention_mask = extended_attention_mask.cast(self.dtype)
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask

    def invert_attention_mask(self, encoder_attention_mask):
        if encoder_attention_mask.ndim == 4:
            encoder_extended_attention_mask = encoder_attention_mask
        elif encoder_attention_mask.ndim == 3:
            encoder_extended_attention_mask = encoder_attention_mask.unsqueeze(1)
        elif encoder_attention_mask.ndim == 2:
            encoder_extended_attention_mask = encoder_attention_mask.unsqueeze([1, 2])
        encoder_extended_attention_mask = encoder_extended_attention_mask.cast(self.dtype)  # fp16 compatibility

        if self.dtype == paddle.float16:
            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
        elif self.dtype == paddle.float32:
            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
        else:
            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4

        return encoder_extended_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        inputs_embeds=None,
        encoder_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        is_decoder=False,
    ):
        r"""
        input_ids (`paddle.Tensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`BertTokenizer`]. See [`PretrainedTokenizer.encode`] and
            [`PretrainedTokenizer.__call__`] for details.

        attention_mask (`paddle.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**.

        position_ids (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

        inputs_embeds (`paddle.Tensor` of shape `(batch_size, 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.
        encoder_embeds (`paddle.Tensor` of shape `(batch_size, hidden_size)`, *optional*):
            Optionally, same as inputs_embeds.
        encoder_hidden_states  (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

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

        past_key_values (`tuple(tuple(paddle.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_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.
        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 [`BaseModelOutputWithPoolingAndCrossAttentions`] instead of a plain tuple.
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        """
        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 is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

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

        # cache_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if attention_mask is None:
            attention_mask = paddle.ones((batch_size, seq_length + past_key_values_length))

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: paddle.Tensor = self.get_extended_attention_mask(
            attention_mask, input_shape, is_decoder
        )

        # 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:
            if isinstance(encoder_hidden_states, (list, tuple)):
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].shape
            else:
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.shape
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)

            if isinstance(encoder_attention_mask, (list, tuple)):
                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
            elif encoder_attention_mask is None:
                encoder_attention_mask = paddle.ones(encoder_hidden_shape)
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
            else:
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        if encoder_embeds is None:
            embedding_output = self.embeddings(
                input_ids=input_ids,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                past_key_values_length=past_key_values_length,
            )
        else:
            embedding_output = encoder_embeds

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            if pooled_output is None:
                # note: we do not output pooled_output
                return (sequence_output,) + encoder_outputs[1:]
            else:
                return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L811
class BlipTextLMHeadModel(BlipTextPretrainedModel):
    """
    Bert Model with a `masked language modeling` head on top.

    Args:
        config (:class:`BlipTextConfig`):
            An instance of BlipTextConfig used to construct BlipTextLMHeadModel.

    """

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

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

        self.bert = BlipTextModel(config, add_pooling_layer=False)
        self.cls = BlipTextOnlyMLMHead(config, embedding_weights=self.bert.embeddings.word_embeddings.weight)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=True,
        reduction="mean",
    ):
        r"""
        input_ids (`paddle.Tensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`BertTokenizer`]. See [`PretrainedTokenizer.encode`] and
            [`PretrainedTokenizer.__call__`] for details.

        attention_mask (`paddle.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**.

        position_ids (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

        inputs_embeds (`paddle.Tensor` of shape `(batch_size, 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.
        encoder_hidden_states  (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

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

        past_key_values (`tuple(tuple(paddle.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_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.
        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 [`CausalLMOutputWithCrossAttentions`] instead of a plain tuple.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            use_cache = False

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            is_decoder=is_decoder,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        if return_logits:
            return prediction_scores[:, :-1, :]

        lm_loss = None
        if labels is not None:
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shifted_prediction_scores = prediction_scores[:, :-1, :]
            labels = labels[:, 1:]
            loss_fct = nn.CrossEntropyLoss(reduction=reduction)  # TODO label_smoothing=0.1
            lm_loss = loss_fct(shifted_prediction_scores.reshape([-1, self.config.vocab_size]), labels.flatten())
            if reduction == "none":
                lm_loss = lm_loss.reshape([prediction_scores.shape[0], -1]).sum(1)

        if not return_dict:
            # note: we do not output pooler
            if self.bert.pooler is None:
                output = (prediction_scores,) + outputs[1:]
            else:
                output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = paddle.ones(input_shape, dtype=input_ids.dtype)

        # cut decoder_input_ids if past_key_values is used
        if past_key_values is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past_key_values,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
            # we must set return_dict False
            "return_dict": False,
        }

    def prepare_attention_mask_for_generation(
        self,
        inputs: paddle.Tensor,
        pad_token_id: Optional[int],
        eos_token_id: Optional[int],
    ) -> paddle.Tensor:
        # do not create 4d attention mask
        is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [paddle.int32, paddle.int64]
        is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs.tolist())
        is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id != eos_token_id)

        # Check if input is input_ids and padded -> only then is attention_mask defined
        if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
            return (inputs != pad_token_id).cast("int64")
        else:
            return paddle.ones(inputs.shape[:2], dtype=paddle.int64)
