from __future__ import annotations

import torch
from torch import Tensor

from sentence_transformers.models.Module import Module


class Pooling(Module):
    """
    Performs pooling (max or mean) on the token embeddings.

    Using pooling, it generates from a variable sized sentence a fixed sized sentence embedding. This layer also allows
    to use the CLS token if it is returned by the underlying word embedding model. You can concatenate multiple poolings
    together.

    Args:
        word_embedding_dimension: Dimensions for the word embeddings
        pooling_mode: Either "cls", "lasttoken", "max", "mean",
            "mean_sqrt_len_tokens", or "weightedmean". If set,
            overwrites the other pooling_mode_* settings
        pooling_mode_cls_token: Use the first token (CLS token) as text
            representations
        pooling_mode_max_tokens: Use max in each dimension over all
            tokens.
        pooling_mode_mean_tokens: Perform mean-pooling
        pooling_mode_mean_sqrt_len_tokens: Perform mean-pooling, but
            divide by sqrt(input_length).
        pooling_mode_weightedmean_tokens: Perform (position) weighted
            mean pooling. See `SGPT: GPT Sentence Embeddings for
            Semantic Search <https://huggingface.co/papers/2202.08904>`_.
        pooling_mode_lasttoken: Perform last token pooling. See `SGPT:
            GPT Sentence Embeddings for Semantic Search
            <https://huggingface.co/papers/2202.08904>`_ and `Text and Code
            Embeddings by Contrastive Pre-Training
            <https://huggingface.co/papers/2201.10005>`_.
        include_prompt: If set to false, the prompt tokens are not
            included in the pooling. This is useful for reproducing
            work that does not include the prompt tokens in the pooling
            like INSTRUCTOR, but otherwise not recommended.
    """

    POOLING_MODES = (
        "cls",
        "lasttoken",
        "max",
        "mean",
        "mean_sqrt_len_tokens",
        "weightedmean",
    )

    config_keys = [
        "word_embedding_dimension",
        "pooling_mode_cls_token",
        "pooling_mode_mean_tokens",
        "pooling_mode_max_tokens",
        "pooling_mode_mean_sqrt_len_tokens",
        "pooling_mode_weightedmean_tokens",
        "pooling_mode_lasttoken",
        "include_prompt",
    ]

    def __init__(
        self,
        word_embedding_dimension: int,
        pooling_mode: str | None = None,
        pooling_mode_cls_token: bool = False,
        pooling_mode_max_tokens: bool = False,
        pooling_mode_mean_tokens: bool = True,
        pooling_mode_mean_sqrt_len_tokens: bool = False,
        pooling_mode_weightedmean_tokens: bool = False,
        pooling_mode_lasttoken: bool = False,
        include_prompt: bool = True,
    ) -> None:
        super().__init__()

        if pooling_mode is not None:  # Set pooling mode by string
            pooling_mode = pooling_mode.lower()

            if pooling_mode not in self.POOLING_MODES:
                raise ValueError(
                    f"Set invalid pooling mode: {pooling_mode}. Valid pooling modes are: {self.POOLING_MODES}."
                )

            pooling_mode_cls_token = pooling_mode == "cls"
            pooling_mode_max_tokens = pooling_mode == "max"
            pooling_mode_mean_tokens = pooling_mode == "mean"
            pooling_mode_mean_sqrt_len_tokens = pooling_mode == "mean_sqrt_len_tokens"
            pooling_mode_weightedmean_tokens = pooling_mode == "weightedmean"
            pooling_mode_lasttoken = pooling_mode == "lasttoken"

        self.word_embedding_dimension = word_embedding_dimension
        self.pooling_mode_cls_token = pooling_mode_cls_token
        self.pooling_mode_mean_tokens = pooling_mode_mean_tokens
        self.pooling_mode_max_tokens = pooling_mode_max_tokens
        self.pooling_mode_mean_sqrt_len_tokens = pooling_mode_mean_sqrt_len_tokens
        self.pooling_mode_weightedmean_tokens = pooling_mode_weightedmean_tokens
        self.pooling_mode_lasttoken = pooling_mode_lasttoken

        self.include_prompt = include_prompt

        pooling_mode_multiplier = sum(
            [
                pooling_mode_cls_token,
                pooling_mode_max_tokens,
                pooling_mode_mean_tokens,
                pooling_mode_mean_sqrt_len_tokens,
                pooling_mode_weightedmean_tokens,
                pooling_mode_lasttoken,
            ]
        )
        self.pooling_output_dimension = pooling_mode_multiplier * word_embedding_dimension

    def __repr__(self) -> str:
        return f"Pooling({self.get_config_dict()})"

    def get_pooling_mode_str(self) -> str:
        """Returns the pooling mode as string"""
        modes = []
        if self.pooling_mode_cls_token:
            modes.append("cls")
        if self.pooling_mode_mean_tokens:
            modes.append("mean")
        if self.pooling_mode_max_tokens:
            modes.append("max")
        if self.pooling_mode_mean_sqrt_len_tokens:
            modes.append("mean_sqrt_len_tokens")
        if self.pooling_mode_weightedmean_tokens:
            modes.append("weightedmean")
        if self.pooling_mode_lasttoken:
            modes.append("lasttoken")

        return "+".join(modes)

    def forward(self, features: dict[str, Tensor]) -> dict[str, Tensor]:
        token_embeddings = features["token_embeddings"]
        attention_mask = (
            features["attention_mask"]
            if "attention_mask" in features
            else torch.ones(token_embeddings.shape[:-1], device=token_embeddings.device, dtype=torch.int64)
        )
        if not self.include_prompt and "prompt_length" in features:
            prompt_length = features["prompt_length"]
            # prompt_length is either:
            # * an int (in inference)
            # * a tensor of shape (bs), all the same value (in training with an IterableDataset)
            # * a tensor of shape (1) (in training with a Dataset)
            # We turn all into an int
            if isinstance(prompt_length, torch.Tensor):
                prompt_length = prompt_length[0].item()

            # Check where the first non-pad token is located
            pad_lengths = attention_mask.to(torch.int32).argmax(dim=1)
            if pad_lengths.sum() == 0:
                # If no left-padding, we can directly set the first `prompt_length` tokens to 0
                attention_mask[:, :prompt_length] = 0
            else:
                # Otherwise, we set all pad + prompt tokens to 0
                prompt_and_pad_lengths = pad_lengths + prompt_length  # shape: (bs)
                seq_len = attention_mask.size(1)
                positions = torch.arange(seq_len, device=attention_mask.device).unsqueeze(0)  # shape: (1, seq_len)
                mask = positions < prompt_and_pad_lengths.unsqueeze(1)  # shape: (bs, seq_len)
                attention_mask[mask] = 0

        ## Pooling strategy
        output_vectors = []
        if self.pooling_mode_cls_token:
            cls_token = features.get("cls_token_embeddings", token_embeddings[:, 0])  # Take first token by default
            output_vectors.append(cls_token)
        if self.pooling_mode_max_tokens:
            input_mask_expanded = (
                attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
            )
            token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
            max_over_time = torch.max(token_embeddings, 1)[0]
            output_vectors.append(max_over_time)
        if self.pooling_mode_mean_tokens or self.pooling_mode_mean_sqrt_len_tokens:
            input_mask_expanded = (
                attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
            )
            sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)

            # If tokens are weighted (by WordWeights layer), feature 'token_weights_sum' will be present
            if "token_weights_sum" in features:
                sum_mask = features["token_weights_sum"].unsqueeze(-1).expand(sum_embeddings.size())
            else:
                sum_mask = input_mask_expanded.sum(1)

            sum_mask = torch.clamp(sum_mask, min=1e-9)

            if self.pooling_mode_mean_tokens:
                output_vectors.append(sum_embeddings / sum_mask)
            if self.pooling_mode_mean_sqrt_len_tokens:
                output_vectors.append(sum_embeddings / torch.sqrt(sum_mask))
        if self.pooling_mode_weightedmean_tokens:
            input_mask_expanded = (
                attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
            )
            # token_embeddings shape: bs, seq, hidden_dim
            weights = (
                torch.arange(start=1, end=token_embeddings.shape[1] + 1)
                .unsqueeze(0)
                .unsqueeze(-1)
                .expand(token_embeddings.size())
                .to(token_embeddings.dtype)
                .to(token_embeddings.device)
            )
            assert weights.shape == token_embeddings.shape == input_mask_expanded.shape
            input_mask_expanded = input_mask_expanded * weights

            sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)

            # If tokens are weighted (by WordWeights layer), feature 'token_weights_sum' will be present
            if "token_weights_sum" in features:
                sum_mask = features["token_weights_sum"].unsqueeze(-1).expand(sum_embeddings.size())
            else:
                sum_mask = input_mask_expanded.sum(1)

            sum_mask = torch.clamp(sum_mask, min=1e-9)
            output_vectors.append(sum_embeddings / sum_mask)
        if self.pooling_mode_lasttoken:
            bs, seq_len, hidden_dim = token_embeddings.shape
            # attention_mask shape: (bs, seq_len)
            # Get shape [bs] indices of the last token (i.e. the last token for each batch item)
            # Use flip and max() to get the last index of 1 in the attention mask

            if torch.jit.is_tracing():
                # Avoid tracing the argmax with int64 input that can not be handled by ONNX Runtime: https://github.com/microsoft/onnxruntime/issues/10068
                attention_mask = attention_mask.to(torch.int32)

            values, indices = attention_mask.flip(1).max(1)
            indices = torch.where(values == 0, seq_len - 1, indices)
            gather_indices = seq_len - indices - 1

            # Turn indices from shape [bs] --> [bs, 1, hidden_dim]
            gather_indices = gather_indices.unsqueeze(-1).repeat(1, hidden_dim)
            gather_indices = gather_indices.unsqueeze(1)
            assert gather_indices.shape == (bs, 1, hidden_dim)

            # Gather along the 1st dim (seq_len) (bs, seq_len, hidden_dim -> bs, hidden_dim)
            # Actually no need for the attention mask as we gather the last token where attn_mask = 1
            # but as we set some indices (which shouldn't be attended to) to 0 with clamp, we
            # use the attention mask to ignore them again
            input_mask_expanded = (
                attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
            )
            embedding = torch.gather(token_embeddings * input_mask_expanded, 1, gather_indices).squeeze(dim=1)
            output_vectors.append(embedding)

        output_vector = torch.cat(output_vectors, 1)
        features["sentence_embedding"] = output_vector
        return features

    def get_sentence_embedding_dimension(self) -> int:
        return self.pooling_output_dimension

    def save(self, output_path: str, *args, safe_serialization: bool = True, **kwargs) -> None:
        self.save_config(output_path)
