# Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...masking_utils import create_bidirectional_mask
from ...modeling_outputs import BaseModelOutput, MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
from ...modeling_rope_utils import RopeParameters
from ...processing_utils import Unpack
from ...utils import auto_docstring
from ...utils.generic import TransformersKwargs, can_return_tuple
from ..llama import LlamaConfig
from ..llama.modeling_llama import LlamaAttention, LlamaModel, LlamaPreTrainedModel, LlamaRMSNorm


class EuroBertConfig(LlamaConfig):
    r"""
    This is the configuration class to store the configuration of a [`EuroBertModel`]. It is used to instantiate an EuroBert
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m).

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 128256):
            Vocabulary size of the EuroBert model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`EuroBertModel`]
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the encoder and pooler.
        max_position_embeddings (`int`, *optional*, defaults to 8192):
            The maximum sequence length that this model might ever be used with. EuroBert supports up to 8192 tokens,
            EuroBert-pretrained up to 2048.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        bos_token_id (`int`, *optional*, defaults to 128000):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 128001):
            End of stream token id.
        pad_token_id (`int`, *optional*, defaults to 128001):
            Padding token id.
        mask_token_id (`int`, *optional*, defaults to 128002):
            Mask token id.
        pretraining_tp (`int`, *optional*, defaults to 1):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
        head_dim (`int`, *optional*):
            The attention head dimension. If None, it will default to hidden_size // num_attention_heads
        classifier_pooling (`str`, *optional*, defaults to `"late"`):
            The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late'].

    ```python
    >>> from transformers import EuroBertModel, EuroBertConfig

    >>> # Initializing a EuroBert eurobert-base style configuration
    >>> configuration = EuroBertConfig()

    >>> # Initializing a model from the eurobert-base style configuration
    >>> model = EuroBertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "eurobert"

    def __init__(
        self,
        vocab_size=128256,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=8192,
        initializer_range=0.02,
        rms_norm_eps=1e-05,
        bos_token_id=128000,
        eos_token_id=128001,
        pad_token_id=128001,
        mask_token_id=128002,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        attention_bias=False,
        attention_dropout=0.0,
        mlp_bias=False,
        head_dim=None,
        classifier_pooling="late",
        **kwargs,
    ):
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads
        kwargs.pop("use_cache", None)  # use_cache=True is not supported for EuroBert

        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            hidden_act=hidden_act,
            max_position_embeddings=max_position_embeddings,
            initializer_range=initializer_range,
            rms_norm_eps=rms_norm_eps,
            use_cache=False,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            pretraining_tp=pretraining_tp,
            tie_word_embeddings=tie_word_embeddings,
            rope_parameters=rope_parameters,
            attention_bias=attention_bias,
            attention_dropout=attention_dropout,
            mlp_bias=mlp_bias,
            head_dim=head_dim,
            **kwargs,
        )
        self.mask_token_id = mask_token_id
        self.classifier_pooling = classifier_pooling
        self.is_causal = False


class EuroBertRMSNorm(LlamaRMSNorm):
    def __init__(self, hidden_size, eps=1e-5):
        super().__init__(hidden_size, eps)


class EuroBertAttention(LlamaAttention):
    def __init__(self, config: EuroBertConfig, layer_idx: int):
        super().__init__(config, layer_idx)
        self.is_causal = False


class EuroBertPreTrainedModel(LlamaPreTrainedModel):
    pass


class EuroBertModel(LlamaModel):
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutput:
        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: torch.Tensor = self.embed_tokens(input_ids)

        if position_ids is None:
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)

        bidirectional_mask = create_bidirectional_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
        )

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        for encoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask=bidirectional_mask,
                position_embeddings=position_embeddings,
                position_ids=position_ids,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
        )


@auto_docstring
class EuroBertForMaskedLM(EuroBertPreTrainedModel):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config: EuroBertConfig):
        super().__init__(config)
        self.model = EuroBertModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, config.mlp_bias)

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

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor] | MaskedLMOutput:
        r"""
        Example:

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

        >>> model = EuroBertForMaskedLM.from_pretrained("EuroBERT/EuroBERT-210m")
        >>> tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m")

        >>> text = "The capital of France is <|mask|>."
        >>> inputs = tokenizer(text, return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> # To get predictions for the mask:
        >>> masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
        >>> predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
        >>> predicted_token = tokenizer.decode(predicted_token_id)
        >>> print("Predicted token:", predicted_token)
        Predicted token:  Paris
        ```"""
        outputs: BaseModelOutput = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )

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

        return MaskedLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring
class EuroBertForSequenceClassification(EuroBertPreTrainedModel):
    def __init__(self, config: EuroBertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.classifier_pooling = config.classifier_pooling

        self.model = EuroBertModel(config)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.GELU()
        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor] | SequenceClassifierOutput:
        encoder_output = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )
        last_hidden_state = encoder_output[0]

        if self.classifier_pooling in ["bos", "mean"]:
            if self.classifier_pooling == "bos":
                pooled_output = last_hidden_state[:, 0]

            elif self.classifier_pooling == "mean":
                if attention_mask is None:
                    pooled_output = last_hidden_state.mean(dim=1)
                else:
                    pooled_output = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1)
                    pooled_output /= attention_mask.sum(dim=1, keepdim=True)

            pooled_output = self.dense(pooled_output)
            pooled_output = self.activation(pooled_output)
            logits = self.classifier(pooled_output)

        elif self.classifier_pooling == "late":
            x = self.dense(last_hidden_state)
            x = self.activation(x)
            logits = self.classifier(x)
            if attention_mask is None:
                logits = logits.mean(dim=1)
            else:
                logits = (logits * attention_mask.unsqueeze(-1)).sum(dim=1)
                logits /= attention_mask.sum(dim=1, keepdim=True)

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=encoder_output.hidden_states,
            attentions=encoder_output.attentions,
        )


@auto_docstring
class EuroBertForTokenClassification(EuroBertPreTrainedModel):
    def __init__(self, config: EuroBertConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = EuroBertModel(config)

        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()

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

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

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | TokenClassifierOutput:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = [
    "EuroBertConfig",
    "EuroBertPreTrainedModel",
    "EuroBertModel",
    "EuroBertForMaskedLM",
    "EuroBertForSequenceClassification",
    "EuroBertForTokenClassification",
]