# Copyright 2025 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.
"""Blt modular model, inheriting from Mllama where appropriate."""

from collections.abc import Callable

import torch
import torch.distributions
import torch.nn as nn
import torch.nn.functional as F

from ... import initialization as init
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...masking_utils import create_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, logging
from ...utils.generic import OutputRecorder, check_model_inputs, maybe_autocast
from ..cohere2.modeling_cohere2 import rotate_half  # noqa: F401
from ..llama.modeling_llama import LlamaRotaryEmbedding
from ..mllama.modeling_mllama import (
    MllamaForCausalLM,
    MllamaPreTrainedModel,
    MllamaSelfAttentionDecoderLayer,
    MllamaTextCrossAttention,
    MllamaTextMLP,
    MllamaTextRMSNorm,
    MllamaTextSelfAttention,
    eager_attention_forward,
)
from .configuration_blt import (
    BltConfig,
    BltGlobalTransformerConfig,
    BltLocalDecoderConfig,
    BltLocalEncoderConfig,
    BltPatcherConfig,
)


logger = logging.get_logger(__name__)


def rolling_polynomial_hash(token_tensor, prime: int = 1000000007):
    """
    A polynomial rolling hash algorithm that converts sequences
    of tokens into hash values. The hash is computed as:
        hash = (token_0 * prime^0 + token_1 * prime^1 + ... + token_n * prime^n)

    The rolling hash allows the model to efficiently
    identify and encode recurring byte-level patterns in the input text.

    Args:
        token_tensor (torch.Tensor): [batch_size, seq_len, group_size] containing token IDs to hash
        prime (int): Prime number used as the base for the polynomial hash.

    Returns:
        torch.Tensor: Hash values of shape [batch_size, seq_len] where each value
                     represents the hash of the corresponding token group

    Example:
        >>> tokens = torch.tensor([[1, 2, 3], [4, 5, 6]])
        >>> hashes = rolling_polynomial_hash(tokens, prime=31)
        >>> # hash[0] = 1*31^0 + 2*31^1 + 3*31^2
        >>> # hash[1] = 4*31^0 + 5*31^1 + 6*31^2
    """
    prime_tensor = torch.tensor(prime, dtype=torch.int64, device=token_tensor.device)
    powers = torch.arange(token_tensor.shape[-1], device=token_tensor.device)
    prime_powers = prime_tensor**powers
    return torch.sum(token_tensor * prime_powers, dim=-1)


def byte_group_hash_function(
    token_ids: torch.Tensor, group_size: int = 2, prime: int = 1000000007, max_hash: int = 30000
):
    """Hash token groups and map to range [0, max_hash]."""
    with torch.no_grad():
        batch_size, seq_len = token_ids.shape
        # Add padding for sliding window
        padding = torch.zeros(batch_size, group_size - 1, dtype=torch.int64, device=token_ids.device)
        padded_tokens = torch.cat([padding, token_ids], dim=1)

        # Create sliding windows and compute hashes
        windows = padded_tokens.unfold(1, group_size, 1)
        hashes = rolling_polynomial_hash(windows, prime)
        hash_values = hashes % max_hash

    return hash_values


def compute_hash_embeddings(
    local_encoder_tokens: torch.Tensor,
    local_encoder,
    encoder_hash_tok_embedding: nn.Embedding,
    encoder_hash_byte_group_nb_functions: int,
    encoder_hash_byte_group_size: list,
    encoder_hash_byte_group_vocab: int,
) -> torch.Tensor:
    """Compute token embeddings enhanced with hash-based embeddings."""
    # Available primes for hash functions
    primes = [
        1000000007,
        5915587277,
        1500450271,
        3267000013,
        5754853343,
        4093082899,
        9576890767,
        3628273133,
        2860486313,
        5463458053,
        3367900313,
    ]

    embeddings = local_encoder.embed_tokens(local_encoder_tokens)
    embedding_idx = 0
    for func_nb in range(encoder_hash_byte_group_nb_functions):
        prime = primes[func_nb % len(primes)]  # Cycle through primes if more functions than primes
        for group_size in encoder_hash_byte_group_size:
            hash_ids = byte_group_hash_function(local_encoder_tokens, group_size, prime, encoder_hash_byte_group_vocab)
            # Apply offset to get the correct slice of the fused embedding
            offset_hash_ids = hash_ids + embedding_idx * encoder_hash_byte_group_vocab
            embeddings += encoder_hash_tok_embedding(offset_hash_ids).to(embeddings.device)
            embedding_idx += 1

    return embeddings


def _prepare_patch_cross_attention_mask(
    patch_ids: torch.Tensor,
    num_patches: int,
    sequence_length: int,
    patches_as_queries: bool = False,
    cross_attn_k: int = 1,
    dtype: torch.dtype = torch.float32,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Prepare cross-attention mask for patch-based attention, following mllama's robust approach.

    This function creates masks that control which patches can attend to which other patches,
    with support for query/key role swapping and cross-attention multipliers.

    Args:
        patch_ids (torch.Tensor): Tensor of shape [batch_size, seq_len] containing patch ids.
        num_patches (int): Total number of patches.
        sequence_length (int): Length of the sequence.
        patches_as_queries (bool): If True, patches are used as queries, otherwise as keys.
        cross_attn_k (int): Cross-attention multiplier for repeating patches.
        dtype (torch.dtype): Data type for the output mask.

    Returns:
        Tuple[torch.Tensor, torch.Tensor]:
            - cross_attention_mask: 4D tensor [batch_size, 1, q_len, kv_len]
    """
    batch_size, seq_len = patch_ids.shape
    device = patch_ids.device

    # Determine query and key lengths based on configuration
    if patches_as_queries:
        q_len = num_patches * cross_attn_k
        kv_len = sequence_length
        # Create patch-to-sequence mapping
        q_patch_ids = (
            torch.arange(num_patches, device=device)
            .unsqueeze(0)
            .unsqueeze(-1)
            .expand(batch_size, num_patches, seq_len)
        )
        kv_patch_ids = patch_ids.unsqueeze(1).expand(batch_size, num_patches, seq_len)
    else:
        q_len = sequence_length
        kv_len = num_patches * cross_attn_k
        # Create sequence-to-patch mapping
        q_patch_ids = patch_ids.unsqueeze(-1).expand(batch_size, seq_len, num_patches)
        kv_patch_ids = (
            torch.arange(num_patches, device=device).unsqueeze(0).unsqueeze(0).expand(batch_size, seq_len, num_patches)
        )

    # Create base attention mask - boolean mask where True means "should attend"
    # Exact patch matching
    cross_attention_mask = q_patch_ids == kv_patch_ids

    # Handle cross_attn_k multiplier by repeating along appropriate dimension
    repeat_dim = 1 if patches_as_queries else -1
    cross_attention_mask = cross_attention_mask.repeat_interleave(cross_attn_k, dim=repeat_dim)

    # Validate dimensions
    expected_shape = (batch_size, q_len, kv_len)
    if cross_attention_mask.shape != expected_shape:
        raise ValueError(
            f"Cross attention mask shape {cross_attention_mask.shape} doesn't match expected {expected_shape}"
        )

    # Reshape so it can be used by attn module - add head dimension
    cross_attention_mask = cross_attention_mask.unsqueeze(1)  # [batch_size, 1, q_len, kv_len]

    # Invert the mask (following mllama pattern exactly)
    # True -> 0.0 (attend), False -> 1.0 (will become -inf)
    inverted_cross_attn_mask = 1.0 - cross_attention_mask.to(dtype)
    cross_attention_mask = inverted_cross_attn_mask.masked_fill(
        inverted_cross_attn_mask.to(torch.bool), torch.finfo(dtype).min
    )

    return cross_attention_mask


def process_patch_lengths(patch_lengths: torch.Tensor, max_patch_length: int | None) -> torch.Tensor:
    """
    Splits patch lengths into smaller segments if they exceed `max_patch_length`.
    Pads the result to uniform length across the batch.

    Args:
        patch_lengths (torch.Tensor): [batch_size, num_patches] tensor of patch lengths.
        max_patch_length (int, optional): Maximum allowed length per patch.

    Returns:
        torch.Tensor: [batch_size, max_len] tensor of split and padded patch lengths.
    """
    if max_patch_length is None:
        return patch_lengths

    batch_size = patch_lengths.size(0)
    processed = []

    for seq in patch_lengths:
        splits = []
        for length in seq[seq > 0]:
            length = length.item()
            full_chunks, remainder = divmod(length, max_patch_length)
            splits.extend([max_patch_length] * full_chunks)
            if remainder:
                splits.append(remainder)
        processed.append(splits)

    # Find max length to pad to
    max_len = max(len(splits) for splits in processed)
    padded = torch.zeros((batch_size, max_len), dtype=patch_lengths.dtype, device=patch_lengths.device)

    for i, splits in enumerate(processed):
        if splits:
            padded[i, : len(splits)] = torch.tensor(splits, dtype=patch_lengths.dtype, device=patch_lengths.device)

    # Trim zero columns
    if (padded != 0).any(dim=0).sum() < padded.shape[1]:
        last_nonzero = (padded != 0).any(dim=0).nonzero().max().item() + 1
        padded = padded[:, :last_nonzero]

    return padded


class BltMLP(MllamaTextMLP):
    pass


class BltRMSNorm(MllamaTextRMSNorm):
    pass


class BltRotaryEmbedding(LlamaRotaryEmbedding):
    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.repeat_interleave(freqs, 2, dim=-1)  # diff from Llama: we interleave() instead of cat()
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


class BltTransformerLayer(MllamaSelfAttentionDecoderLayer):
    def __init__(self, config, layer_idx: int):
        super().__init__()

        self.self_attn = BltSelfAttention(config=config, layer_idx=layer_idx)
        self.mlp = BltMLP(config)
        self.input_layernorm = BltRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = BltRMSNorm(config.hidden_size, eps=config.rms_norm_eps)


class BltSelfAttention(MllamaTextSelfAttention):
    def __init__(self, config: BltConfig, layer_idx: int):
        super().__init__(config, layer_idx)


class BltCrossAttention(MllamaTextCrossAttention):
    """Cross-attention module for Blt, following transformers style"""

    def __init__(self, config: BltConfig, layer_idx: int, hidden_size: int | None = None):
        super().__init__()
        self.is_causal = False
        self.q_norm = BltRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.k_norm = BltRMSNorm(self.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cross_attention_states: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        bsz, q_len, _ = hidden_states.size()
        query_states = self.q_norm(hidden_states)
        query_states = self.q_proj(query_states)
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

        cross_attention_states = self.k_norm(cross_attention_states)
        key_states = self.k_proj(cross_attention_states)
        value_states = self.v_proj(cross_attention_states)
        key_states = key_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        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, q_len, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        attn_output = attn_output + hidden_states
        return attn_output, attn_weights


@auto_docstring
class BltPreTrainedModel(MllamaPreTrainedModel):
    config: BltConfig
    _supports_attention_backend = False
    _supports_flash_attn = False
    _supports_flex_attn = False
    _no_split_modules = ["BltTransformerLayer"]
    _can_record_outputs = {
        "hidden_states": OutputRecorder(BltTransformerLayer, index=0, layer_name="local_decoder"),
        "attentions": OutputRecorder(BltSelfAttention, index=1, layer_name="local_decoder"),
    }

    # Weight initialization is adapted from:
    # - https://github.com/facebookresearch/blt/blob/main/bytelatent/model/blt.py
    # - https://github.com/pytorch/torchtitan/blob/main/torchtitan/experiments/transformers_modeling_backend/model/model.py
    #
    # Both implementations use truncated normal initialization with std ~ 1 / sqrt(d_model)
    # (or 1 / sqrt(hidden_dim) for FFN outputs), and unit initialization for normalization layers.
    # We follow the same scheme here, but expressed in the Transformers APIs.

    @torch.no_grad()
    def _init_weights(self, module):
        """
        Initialize BLT weights following the original ByteLatentTransformer:

        - Most weights are drawn from a truncated normal.
        - Scale is ~ 1 / sqrt(model_dim) (or 1 / sqrt(hidden_dim) for FFN outputs).
        - Norm layers are set to weight = 1, bias = 0.
        """
        class_name = module.__class__.__name__

        # Norms: RMSNorm / LayerNorm
        if isinstance(module, (BltRMSNorm, nn.LayerNorm)) or "RMSNorm" in class_name or "LayerNorm" in class_name:
            if getattr(module, "weight", None) is not None:
                init.ones_(module.weight)
            if getattr(module, "bias", None) is not None:
                init.zeros_(module.bias)
            return

        # Embeddings (encoder / patcher / hash embeddings)
        if isinstance(module, nn.Embedding):
            hidden_size = getattr(self.config, "hidden_size", None)
            if hidden_size is None and hasattr(self.config, "encoder_config"):
                hidden_size = getattr(self.config.encoder_config, "hidden_size", None)
            if hidden_size is None:
                hidden_size = module.embedding_dim

            std = hidden_size**-0.5
            init.trunc_normal_(
                module.weight,
                mean=0.0,
                std=std,
                a=-3 * std,
                b=3 * std,
            )
            if module.padding_idx is not None:
                init.zeros_(module.weight[module.padding_idx])
            return

        # Self-attention / cross-attention projections
        if isinstance(module, (BltSelfAttention, BltCrossAttention)) or class_name in (
            "MllamaTextSelfAttention",
            "MllamaTextCrossAttention",
        ):
            dim = getattr(self.config, "hidden_size", None)
            if dim is None and hasattr(module, "hidden_size"):
                dim = module.hidden_size
            if dim is None:
                for name in ("q_proj", "k_proj", "v_proj", "o_proj", "dense"):
                    proj = getattr(module, name, None)
                    if proj is not None and hasattr(proj, "weight"):
                        dim = proj.weight.shape[-1]
                        break
            if dim is None:
                return

            std = dim**-0.5

            # Input projections (q, k, v)
            for proj_name in ("q_proj", "k_proj", "v_proj"):
                proj = getattr(module, proj_name, None)
                if proj is not None and hasattr(proj, "weight"):
                    init.trunc_normal_(
                        proj.weight,
                        mean=0.0,
                        std=std,
                        a=-3 * std,
                        b=3 * std,
                    )
                    if getattr(proj, "bias", None) is not None:
                        init.zeros_(proj.bias)

            # Output projection: o_proj or dense
            o_proj = getattr(module, "o_proj", getattr(module, "dense", None))
            if o_proj is not None and hasattr(o_proj, "weight"):
                init.trunc_normal_(
                    o_proj.weight,
                    mean=0.0,
                    std=std,
                    a=-3 * std,
                    b=3 * std,
                )
                if getattr(o_proj, "bias", None) is not None:
                    init.zeros_(o_proj.bias)
            return

        # MLP / FFN blocks
        if isinstance(module, BltMLP) or class_name == "MllamaTextMLP":
            hidden_size = getattr(self.config, "hidden_size", None)
            if hidden_size is None and hasattr(self.config, "decoder_config"):
                hidden_size = getattr(self.config.decoder_config, "hidden_size", None)
            if hidden_size is None and hasattr(self.config, "encoder_config"):
                hidden_size = getattr(self.config.encoder_config, "hidden_size", None)

            # Input-side std
            in_std = None
            if hidden_size is not None:
                in_std = hidden_size**-0.5

            gate_proj = getattr(module, "gate_proj", getattr(module, "fc1", None))
            up_proj = getattr(module, "up_proj", None)
            down_proj = getattr(module, "down_proj", getattr(module, "fc2", None))

            # gate / input projections
            for proj in (gate_proj, up_proj):
                if proj is not None and hasattr(proj, "weight"):
                    std = in_std or (proj.weight.shape[1] ** -0.5)
                    init.trunc_normal_(
                        proj.weight,
                        mean=0.0,
                        std=std,
                        a=-3 * std,
                        b=3 * std,
                    )
                    if getattr(proj, "bias", None) is not None:
                        init.zeros_(proj.bias)

            # output/ down projections
            if down_proj is not None and hasattr(down_proj, "weight"):
                hidden_dim = down_proj.weight.shape[1]
                out_std = hidden_dim**-0.5
                init.trunc_normal_(
                    down_proj.weight,
                    mean=0.0,
                    std=out_std,
                    a=-3 * out_std,
                    b=3 * out_std,
                )
                if getattr(down_proj, "bias", None) is not None:
                    init.zeros_(down_proj.bias)
            return

        # Generic Linear layers (projections, lm_head, etc.)
        if isinstance(module, nn.Linear):
            fan_in = module.in_features
            std = fan_in**-0.5
            init.trunc_normal_(
                module.weight,
                mean=0.0,
                std=std,
                a=-3 * std,
                b=3 * std,
            )
            if module.bias is not None:
                init.zeros_(module.bias)
            return

        if isinstance(module, BltRotaryEmbedding):
            rope_fn = (
                ROPE_INIT_FUNCTIONS[module.rope_type]
                if module.rope_type != "default"
                else module.compute_default_rope_parameters
            )
            buffer_value, _ = rope_fn(module.config)
            init.copy_(module.inv_freq, buffer_value)
            init.copy_(module.original_inv_freq, buffer_value)

    def _update_causal_mask(self, module):
        raise AttributeError("No need to inherit it!")

    def _prepare_4d_causal_attention_mask_with_cache_position(self, module):
        raise AttributeError("No need to inherit it!")


class BltLocalEncoder(BltPreTrainedModel):
    config: BltLocalEncoderConfig
    _can_record_outputs = {
        "encoder_attentions": OutputRecorder(BltSelfAttention, index=1, layer_name="local_encoder"),
    }

    def __init__(self, config: BltLocalEncoderConfig):
        super().__init__(config)
        self.gradient_checkpointing = False
        self.config = config
        self.layers = nn.ModuleList(
            [BltTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.rotary_emb = BltRotaryEmbedding(config=config)
        self.patch_embedding_projection = nn.Linear(
            in_features=config.hidden_size,
            out_features=config.hidden_size * config.cross_attn_k,
            bias=False,
        )
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.cross_attn_layers = nn.ModuleList()
        layers_to_add = config.num_hidden_layers if config.cross_attn_all_layers else 1
        for layer_idx in range(layers_to_add):
            self.cross_attn_layers.append(
                BltCrossAttention(config=config, layer_idx=layer_idx, hidden_size=config.hidden_size)
            )

        self.post_init()

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        patch_embeds: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        encoder_attention_mask: torch.Tensor | None = None,
        num_patches: int | None = None,
        patch_ids: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        batch_size = inputs_embeds.shape[0]
        hidden_states = F.dropout(inputs_embeds, p=self.config.dropout, training=self.training)

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

        position_embeddings = self.rotary_emb(hidden_states, position_ids)
        hidden_states = F.dropout(hidden_states, p=self.config.dropout, training=self.training)

        for idx, layer in enumerate(self.layers):
            hidden_states = layer(
                hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                cache_position=cache_position,
                **kwargs,
            )
            if idx == len(self.layers) - 1 or self.config.cross_attn_all_layers:
                patch_embeds = self.patch_reduce(hidden_states, num_patches, patch_ids)
                patch_embeds = self.patch_embedding_projection(patch_embeds)
                patch_embeds = patch_embeds.reshape(
                    batch_size, patch_embeds.shape[1] * self.config.cross_attn_k, self.config.hidden_size
                )
                layer_idx = idx if self.config.cross_attn_all_layers else 0
                cross_attention_output, _ = self.cross_attn_layers[layer_idx](
                    hidden_states=patch_embeds,
                    cross_attention_states=hidden_states,
                    attention_mask=encoder_attention_mask,
                    **kwargs,
                )
                patch_embeds = patch_embeds + cross_attention_output
        encoder_cross_states = patch_embeds
        return hidden_states, encoder_cross_states

    def patch_reduce(self, hidden_states, max_num_patches, patch_ids):
        """
        Reduce variable length patches to single embedding per patch
        Note: this works with variable number of patches for different sequences in the batch
        It handles variable length patches by assuming that patch_lengths will be 0 for any
        extra patches on the *right*. Since there can be a variable number of patches
        this function also return the number of patches for each sequence in the batch.
        Any embeddings on the right that are not allocated to a patch
        (i.e. if the sum(patch_lengths[i]) < seq_len for any i)
        will be sent to a dummy patch, which is trimmed before returning.
        """
        batch_size = hidden_states.shape[0]
        embedding_dim = hidden_states.shape[-1]

        patch_ids = patch_ids.unsqueeze(-1).expand(-1, -1, hidden_states.shape[-1])

        reduced_embeddings = torch.zeros(
            (batch_size, max_num_patches, embedding_dim), dtype=hidden_states.dtype, device=hidden_states.device
        )

        reduced_embeddings = reduced_embeddings.scatter_reduce(
            src=hidden_states,
            dim=1,
            index=patch_ids,
            reduce="amax",
            include_self=False,
        )
        reduced_embeddings = reduced_embeddings[:, :max_num_patches, :]

        return reduced_embeddings


class BltLocalDecoder(BltPreTrainedModel):
    config: BltLocalDecoderConfig

    def __init__(self, config: BltLocalDecoderConfig):
        super().__init__(config)
        self.gradient_checkpointing = False
        self.config = config
        self.cross_attn_decoder = True
        self.layers = nn.ModuleList(
            [BltTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.rotary_emb = BltRotaryEmbedding(config=config)
        self.patch_embedding_projection = nn.Linear(
            in_features=config.hidden_size_global,
            out_features=config.hidden_size * config.cross_attn_k,
            bias=False,
        )
        self.norm = BltRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.cross_attn_layers = nn.ModuleList()
        layers_to_add = config.num_hidden_layers if config.cross_attn_all_layers else 1
        for layer_idx in range(layers_to_add):
            self.cross_attn_layers.append(
                BltCrossAttention(config=config, layer_idx=layer_idx, hidden_size=config.hidden_size)
            )

        self.post_init()

    @check_model_inputs
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        patch_embeds: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        encoder_attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        batch_size = inputs_embeds.shape[0]
        hidden_states = inputs_embeds
        patch_embeds = self.patch_embedding_projection(patch_embeds)
        patch_embeds = patch_embeds.reshape(
            batch_size, patch_embeds.shape[1] * self.config.cross_attn_k, self.config.hidden_size
        )

        if patch_embeds is not None and not self.cross_attn_decoder:
            hidden_states = hidden_states + patch_embeds

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

        position_embeddings = self.rotary_emb(hidden_states, position_ids)
        hidden_states = F.dropout(hidden_states, p=self.config.dropout, training=self.training)

        for i, layer in enumerate(self.layers):
            if i == 0 or self.config.cross_attn_all_layers:
                cross_attention_output, _ = self.cross_attn_layers[i](
                    hidden_states=hidden_states,
                    cross_attention_states=patch_embeds,
                    attention_mask=encoder_attention_mask,
                    **kwargs,
                )
                hidden_states = hidden_states + cross_attention_output
            hidden_states = layer(
                hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                cache_position=cache_position,
                **kwargs,
            )
        logits = self.norm(hidden_states)
        return logits


class BltGlobalTransformer(BltPreTrainedModel):
    config: BltGlobalTransformerConfig
    _can_record_outputs = {
        "global_attentions": OutputRecorder(BltSelfAttention, index=1, layer_name="global_transformer"),
    }

    def __init__(self, config: BltGlobalTransformerConfig):
        super().__init__(config)
        self.config = config
        self.layers = nn.ModuleList()
        for layer_idx in range(config.num_hidden_layers):
            self.layers.append(BltTransformerLayer(config, layer_idx))
        self.rotary_emb = BltRotaryEmbedding(config=config)

        # Create token embedding projection (use nn.Identity() when no projection needed)
        if getattr(config, "encoder_cross_output_size", None) is not None:
            self.token_embedding_projection = nn.Linear(
                config.encoder_cross_output_size, config.hidden_size, bias=False
            )
        else:
            self.token_embedding_projection = nn.Identity()

        self.post_init()

    def forward(
        self,
        input_embeds: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        batch_size, seq_len, _ = input_embeds.shape
        hidden_states = self.token_embedding_projection(input_embeds)
        hidden_states = F.dropout(hidden_states, p=self.config.dropout, training=self.training)
        if position_ids is None:
            position_ids = (
                torch.arange(input_embeds.shape[1], device=input_embeds.device).unsqueeze(0).expand(batch_size, -1)
            )
        position_embeddings = self.rotary_emb(hidden_states, position_ids)
        for i, layer in enumerate(self.layers):
            hidden_states = layer(
                hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                cache_position=cache_position,
                **kwargs,
            )
        return hidden_states


class BltPatcher(BltPreTrainedModel):
    config: BltPatcherConfig

    def __init__(self, config: BltPatcherConfig):
        super().__init__(config)
        self.rotary_emb = BltRotaryEmbedding(config=self.config)
        self.layers = nn.ModuleList()
        for layer_idx in range(self.config.num_hidden_layers):
            self.layers.append(BltTransformerLayer(self.config, layer_idx))
        self.embed_tokens = nn.Embedding(self.config.vocab_size, self.config.hidden_size)
        self.norm = BltRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
        self.lm_head = nn.Linear(
            self.config.hidden_size,
            self.config.vocab_size,
            bias=False,
        )

        self.post_init()

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        patch_size: int | None = None,
        threshold: float | None = None,
        max_patch_length: int | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        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)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

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

        for layer in self.layers:
            hidden_states = layer(hidden_states, position_embeddings=position_embeddings, attention_mask=causal_mask)

        logits = self.lm_head(self.norm(hidden_states))
        prediction_entropies = torch.distributions.Categorical(logits=logits).entropy()

        batch_size, sequence_length = inputs_embeds.shape[:2]
        if patch_size is not None:
            patch_lengths = self.patch_lengths_from_entropies(
                entropies=prediction_entropies,
                sequence_length=sequence_length,
                patch_size=patch_size,
                threshold=threshold,
            )
        else:
            patch_lengths = torch.ones(
                (batch_size, sequence_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device
            )
        patch_lengths = process_patch_lengths(patch_lengths, max_patch_length)
        return prediction_entropies, patch_lengths, logits

    @staticmethod
    def patch_lengths_from_entropies(
        entropies,
        sequence_length,
        patch_size=None,
        threshold=None,
    ):
        """
        Computes patch lengths from token entropies.

        Depending on whether a threshold is provided, the function uses either:
        - Thresholding the entropy values (when `threshold` is set).
        """

        batch_size = entropies.shape[0]

        # Always include token 0 and 1 as starting tokens
        init_tokens = (
            torch.tensor([0, 1], dtype=torch.long, device=entropies.device).unsqueeze(0).repeat(batch_size, 1)
        )
        offset = init_tokens.shape[1]

        # Ignore first token entropy (BOS)
        entropies = entropies[:, 1:]

        # Threshold the entropy values to define patch start points
        patch_mask = entropies > threshold

        seq_len = patch_mask.shape[1]

        # Create patch IDs (token indices), and add a sentinel to ensure alignment
        token_indices = torch.arange(seq_len, device=entropies.device).unsqueeze(0).expand(batch_size, -1)
        sentinel = torch.full_like(token_indices, seq_len)
        padded_indices = torch.cat([token_indices, sentinel], dim=1)

        # Pad mask with inverse to align sentinel correctly
        padded_mask = torch.cat([patch_mask, ~patch_mask], dim=1)

        # Select indices where mask is True
        patch_starts = padded_indices[padded_mask].reshape(batch_size, seq_len)
        max_valid_patches = patch_mask.sum(dim=1).max()
        patch_starts = patch_starts[:, :max_valid_patches]

        # Offset patch starts to account for the two initial tokens
        patch_start_ids = torch.cat((init_tokens, patch_starts + offset), dim=1)

        # Compute patch end positions by shifting start positions
        last_token = torch.full_like(patch_start_ids[:, :1], sequence_length - 1)
        patch_ends = torch.cat((patch_start_ids[:, 1:] - 1, last_token), dim=1)

        patch_lengths = patch_ends - patch_start_ids + 1

        return patch_lengths


class BltModel(BltPreTrainedModel):
    def __init__(self, config: BltConfig):
        super().__init__(config)
        self.gradient_checkpointing = False

        self.config = config
        self.local_encoder = BltLocalEncoder(config.encoder_config)
        self.global_transformer = BltGlobalTransformer(config.global_config)
        self.local_decoder = BltLocalDecoder(config.decoder_config)
        num_embeddings = config.encoder_hash_byte_group_nb_functions * len(config.encoder_hash_byte_group_size)
        total_vocab_size = config.encoder_hash_byte_group_vocab * num_embeddings
        self.encoder_hash_tok_embedding = nn.Embedding(total_vocab_size, config.encoder_config.hidden_size)
        if self.config.patch_in_forward:
            self.patcher = BltPatcher(config.patcher_config)
            self.patcher.eval()
            for param in self.patcher.parameters():
                param.requires_grad = False
        else:
            self.patcher = None
        self.post_init()

    @check_model_inputs
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        patch_lengths: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

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

        # Extract input embeddings as early as possible
        if inputs_embeds is not None:
            encoder_embeds = inputs_embeds
            batch_size, sequence_length, _ = inputs_embeds.shape
        else:
            batch_size, sequence_length = input_ids.shape
            encoder_embeds = compute_hash_embeddings(
                input_ids,
                self.local_encoder,
                self.encoder_hash_tok_embedding,
                self.config.encoder_hash_byte_group_nb_functions,
                self.config.encoder_hash_byte_group_size,
                self.config.encoder_hash_byte_group_vocab,
            )

        if patch_lengths is None:
            if self.config.patching_mode == "entropy" and self.patcher is not None:
                if input_ids is None:
                    raise ValueError("input_ids is required for entropy-based patching")
                _, patch_lengths, _ = self.patcher(
                    input_ids,
                    patch_size=self.config.patch_size,
                    threshold=self.config.patching_threshold,
                    max_patch_length=self.config.max_patch_length,
                    patching_batch_size=self.config.patching_batch_size,
                    device=input_ids.device,
                )
            else:
                device = input_ids.device if input_ids is not None else inputs_embeds.device
                dtype = input_ids.dtype if input_ids is not None else inputs_embeds.dtype
                patch_lengths = process_patch_lengths(
                    torch.ones((batch_size, sequence_length + 1), dtype=dtype, device=device),
                    self.config.max_patch_length,
                )
        patch_ids = self._patch_ids_from_lengths(patch_lengths, sequence_length)
        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + encoder_embeds.shape[1], device=encoder_embeds.device
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=encoder_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values.self_attention_cache if past_key_values is not None else None,
            position_ids=position_ids,
        )

        cross_attn_mask_enc = _prepare_patch_cross_attention_mask(
            patch_ids=patch_ids,
            num_patches=patch_lengths.shape[1],
            sequence_length=sequence_length,
            patches_as_queries=True,
            cross_attn_k=self.config.cross_attn_k,
            dtype=encoder_embeds.dtype,
        )
        encoder_hidden_states, encoder_cross_states = self.local_encoder(
            input_ids=input_ids,
            inputs_embeds=encoder_embeds,
            attention_mask=causal_mask,
            position_ids=position_ids,
            encoder_attention_mask=cross_attn_mask_enc,
            num_patches=patch_lengths.shape[1],
            patch_ids=patch_ids,
            past_key_values=past_key_values.self_attention_cache if past_key_values is not None else None,
            **kwargs,
        )
        encoder_cross_states = encoder_cross_states.view(batch_size, patch_lengths.shape[1], -1)
        global_cache_position = torch.arange(0, encoder_cross_states.shape[1], device=encoder_cross_states.device)
        global_position_ids = global_cache_position.unsqueeze(0)
        global_causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=encoder_cross_states,
            attention_mask=None,
            cache_position=global_cache_position,
            past_key_values=None,
            position_ids=None,
        )

        global_hidden_states = self.global_transformer(
            input_embeds=encoder_cross_states,
            attention_mask=global_causal_mask,
            position_ids=global_position_ids,
            **kwargs,
        )
        decoder_patch_ids = self._patch_ids_from_lengths(patch_lengths[:, 1:], sequence_length)
        cross_attn_mask_dec = _prepare_patch_cross_attention_mask(
            patch_ids=decoder_patch_ids,
            num_patches=patch_lengths.shape[1],
            sequence_length=sequence_length,
            patches_as_queries=False,
            cross_attn_k=self.config.cross_attn_k,
            dtype=encoder_embeds.dtype,
        )
        output = self.local_decoder(
            input_ids=input_ids,
            inputs_embeds=encoder_hidden_states,
            patch_embeds=global_hidden_states,
            attention_mask=causal_mask,
            position_ids=position_ids,
            past_key_values=past_key_values.cross_attention_cache if past_key_values is not None else None,
            cache_position=cache_position,
            encoder_attention_mask=cross_attn_mask_dec,
            **kwargs,
        )
        return BaseModelOutputWithPast(
            last_hidden_state=output,
            past_key_values=past_key_values,
        )

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

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

    def _patch_ids_from_lengths(self, patch_lengths: torch.Tensor, seq_len: int) -> torch.Tensor:
        batch_size = patch_lengths.shape[0]
        patch_starts = torch.cat(
            [
                torch.zeros(batch_size, 1, dtype=patch_lengths.dtype, device=patch_lengths.device),
                patch_lengths.cumsum(dim=-1)[:, :-1],
            ],
            dim=-1,
        )
        token_positions = torch.arange(seq_len, device=patch_lengths.device)
        return (patch_starts.unsqueeze(1) <= token_positions.unsqueeze(0).unsqueeze(-1)).sum(dim=-1) - 1


class BltForCausalLM(MllamaForCausalLM):
    config: BltConfig
    _can_compile_fullgraph = False
    base_model_prefix = "model"
    _tied_weights_keys = {"model.local_encoder.embed_tokens.weight": "lm_head.weight"}

    def __init__(self, config: BltConfig):
        super().__init__(config)
        self.vocab_size = config.vocab_size
        self.model = BltModel(config)
        self.lm_head = nn.Linear(config.decoder_config.hidden_size, config.vocab_size, bias=False)
        self.post_init()

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        cross_attention_states: torch.LongTensor | None = None,  # Keep for compatibility
        cross_attention_mask: torch.LongTensor | None = None,
        full_text_row_masked_out_mask: tuple[torch.Tensor, torch.Tensor] | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | CausalLMOutputWithPast:
        # Call parent forward but exclude cross_attention_states from model call
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            cross_attention_mask=cross_attention_mask,
            full_text_row_masked_out_mask=full_text_row_masked_out_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :]).float()

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

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = [
    "BltPreTrainedModel",
    "BltModel",
    "BltPatcher",
    "BltForCausalLM",
]
