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#           This file was automatically generated from src/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
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# Copyright 2026 The HuggingFace 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.

from collections.abc import Callable
from typing import Optional

import torch
from torch import nn
from torch.nn import functional as F

from ... import initialization as init
from ...integrations import (
    use_experts_implementation,
    use_kernel_forward_from_hub,
    use_kernel_func_from_hub,
    use_kernelized_func,
)
from ...masking_utils import create_bidirectional_sliding_window_mask
from ...modeling_layers import GenericForTokenClassification, GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring
from ...utils.generic import maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from .configuration_openai_privacy_filter import OpenAIPrivacyFilterConfig


@use_kernel_forward_from_hub("RMSNorm")
class OpenAIPrivacyFilterRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        OpenAIPrivacyFilterRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states) -> torch.Tensor:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return (self.weight * hidden_states).to(input_dtype)  # main diff with Llama

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class OpenAIPrivacyFilterRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: OpenAIPrivacyFilterConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_default_rope_parameters(
        config: OpenAIPrivacyFilterConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @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).to(x.device)
        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 = freqs
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

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


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = _apply_rotary_emb(q, cos, sin)
    k_embed = _apply_rotary_emb(k, cos, sin)
    return q_embed, k_embed


def _apply_rotary_emb(
    x: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
) -> torch.Tensor:
    # Interleaving layout instead of concatenated
    first_half, second_half = x[..., ::2], x[..., 1::2]
    first_ = first_half * cos - second_half * sin
    second_ = second_half * cos + first_half * sin
    return torch.stack((first_, second_), dim=-1).flatten(-2)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float | int = 0.0,
    **kwargs,
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)
    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    sinks = module.sinks.reshape(1, -1, 1, 1).expand(query.shape[0], -1, query.shape[-2], -1)
    combined_logits = torch.cat([attn_weights, sinks], dim=-1)

    # This was not in the original implementation and slightly affect results; it prevents overflow in BF16/FP16
    # when training with bsz>1 we clamp max values.

    combined_logits = combined_logits - combined_logits.max(dim=-1, keepdim=True).values
    probs = nn.functional.softmax(combined_logits, dim=-1, dtype=torch.float32)  # Softmax in fp32
    scores = probs[..., :-1]  # we drop the sink here
    attn_weights = nn.functional.dropout(scores, p=dropout, training=module.training).to(value_states.dtype)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()
    return attn_output, attn_weights


@use_kernelized_func(apply_rotary_pos_emb)
class OpenAIPrivacyFilterAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: OpenAIPrivacyFilterConfig):
        super().__init__()
        self.config = config
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = config.head_dim**-0.25
        self.attention_dropout = config.attention_dropout
        self.is_causal = False
        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )
        self.sliding_window = config.sliding_window + 1  # Account for FA symmetry using -1
        self.sinks = nn.Parameter(torch.empty(config.num_attention_heads))

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, None]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        # Unique: applying scale individually to each Q and K
        query_states = query_states * self.scaling
        key_states = key_states * self.scaling

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

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=1.0,  # scaling applied before
            sliding_window=self.sliding_window,
            s_aux=self.sinks,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


@use_experts_implementation(is_transposed=True, has_bias=True)
class OpenAIPrivacyFilterExperts(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate_size = config.intermediate_size
        self.num_experts = config.num_local_experts
        self.hidden_size = config.hidden_size
        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, 2 * self.intermediate_size))
        self.gate_up_proj_bias = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_size))
        self.down_proj = nn.Parameter(torch.empty((self.num_experts, self.intermediate_size, self.hidden_size)))
        self.down_proj_bias = nn.Parameter(torch.empty(self.num_experts, self.hidden_size))
        self.alpha = 1.702
        self.limit = 7.0

    def _apply_gate(self, gate_up: torch.Tensor) -> torch.Tensor:
        # Concatenated layout instead of interleaving
        gate, up = gate_up.chunk(2, dim=-1)
        gate = gate.clamp(min=None, max=self.limit)
        up = up.clamp(min=-self.limit, max=self.limit)
        glu = gate * torch.sigmoid(gate * self.alpha)
        gated_output = (up + 1) * glu
        return gated_output

    def forward(self, hidden_states: torch.Tensor, router_indices=None, routing_weights=None) -> torch.Tensor:
        original_dtype = hidden_states.dtype

        # Accumulate over fp32
        next_states = torch.zeros_like(hidden_states, dtype=torch.float32, device=hidden_states.device)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(
                router_indices, num_classes=self.num_experts
            )  # masking is also a class
            expert_mask = expert_mask.permute(2, 1, 0)
            # we sum on the top_k and on the sequence length to get which experts
            # are hit this time around
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        # Key change to original gpt oss is to stay in fp32 precision for all linear projections / muls
        for expert_idx in expert_hit:
            # expert_idx only have 1 element, so we can use scale for fast indexing
            expert_idx = expert_idx[0]
            # skip masking index
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate_up = (
                current_state.float() @ self.gate_up_proj[expert_idx].float()
                + self.gate_up_proj_bias[expert_idx].float()
            )
            gated_output = self._apply_gate(gate_up).float()
            out = gated_output.float() @ self.down_proj[expert_idx].float() + self.down_proj_bias[expert_idx].float()
            weighted_output = out * routing_weights[token_idx, top_k_pos, None].float()
            next_states.index_add_(0, token_idx, weighted_output)

        return next_states.to(original_dtype)


class OpenAIPrivacyFilterTopKRouter(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.top_k = config.num_experts_per_tok
        self.num_experts = config.num_local_experts
        self.hidden_dim = config.hidden_size
        self.weight = nn.Parameter(torch.zeros(self.num_experts, self.hidden_dim))
        self.bias = nn.Parameter(torch.zeros(self.num_experts))

    def forward(self, hidden_states):
        # Force fp32
        router_logits = F.linear(
            hidden_states.float(), self.weight.float(), self.bias.float()
        )  # (num_tokens, num_experts)
        router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=-1)  # (num_tokens, top_k)
        router_scores = torch.nn.functional.softmax(router_top_value, dim=1, dtype=router_top_value.dtype)
        # Additional scaling
        router_scores = router_scores / self.top_k
        return router_logits, router_scores, router_indices


class OpenAIPrivacyFilterMLP(nn.Module):
    """Similar to GPT Oss but with FP32 focus + added experts scaling"""

    def __init__(self, config):
        super().__init__()
        self.router = OpenAIPrivacyFilterTopKRouter(config)
        self.num_experts = config.num_experts_per_tok
        self.experts = OpenAIPrivacyFilterExperts(config)

    def forward(self, hidden_states):
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.reshape(-1, hidden_dim)
        _, router_scores, router_indices = self.router(hidden_states)
        hidden_states = self.experts(hidden_states, router_indices, router_scores)
        # Additional scaling
        hidden_states = hidden_states * self.num_experts
        hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim)
        return hidden_states, router_scores


class OpenAIPrivacyFilterEncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: OpenAIPrivacyFilterConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = OpenAIPrivacyFilterAttention(config)
        self.mlp = OpenAIPrivacyFilterMLP(config)
        self.input_layernorm = OpenAIPrivacyFilterRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = OpenAIPrivacyFilterRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states, _ = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states


@auto_docstring
class OpenAIPrivacyFilterPreTrainedModel(PreTrainedModel):
    config: OpenAIPrivacyFilterConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["OpenAIPrivacyFilterEncoderLayer"]
    _skip_keys_device_placement = None  # No cache
    _supports_flash_attn = True
    _supports_sdpa = False
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True

    _can_record_outputs = {
        "router_logits": OutputRecorder(OpenAIPrivacyFilterTopKRouter, index=0),
        "hidden_states": OpenAIPrivacyFilterEncoderLayer,
        "attentions": OpenAIPrivacyFilterAttention,
    }
    _keep_in_fp32_modules = []
    _compatible_flash_implementations = ["kernels-community/vllm-flash-attn3", "flash_attention_4"]
    _keep_in_fp32_modules_strict = ["sinks"]

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        std = self.config.initializer_range
        if isinstance(module, OpenAIPrivacyFilterExperts):
            init.normal_(module.gate_up_proj, mean=0.0, std=std)
            init.zeros_(module.gate_up_proj_bias)
            init.normal_(module.down_proj, mean=0.0, std=std)
            init.zeros_(module.down_proj_bias)
        elif isinstance(module, OpenAIPrivacyFilterAttention):
            init.normal_(module.sinks, mean=0.0, std=std)
        elif isinstance(module, OpenAIPrivacyFilterTopKRouter):
            init.normal_(module.weight, mean=0.0, std=std)
            init.normal_(module.bias, mean=0.0, std=std)

    def get_correct_experts_implementation(self, requested_experts: str | None) -> str:
        """The model is very sensitive to accumulation orders, hence we default to `eager` instead"""
        requested_experts = "eager" if requested_experts is None else requested_experts
        return super().get_correct_experts_implementation(requested_experts)


@auto_docstring
class OpenAIPrivacyFilterModel(OpenAIPrivacyFilterPreTrainedModel):
    def __init__(self, config: OpenAIPrivacyFilterConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([OpenAIPrivacyFilterEncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = OpenAIPrivacyFilterRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = OpenAIPrivacyFilterRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

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

    @merge_with_config_defaults
    @capture_outputs
    @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,
        **kwargs: Unpack[TransformersKwargs],
    ) -> 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 = self.embed_tokens(input_ids)
        hidden_states = inputs_embeds

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

        attention_mask = create_bidirectional_sliding_window_mask(
            config=self.config,
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
        )

        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=attention_mask,
                position_ids=position_ids,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)

        return BaseModelOutput(last_hidden_state=hidden_states)


class OpenAIPrivacyFilterForTokenClassification(GenericForTokenClassification, OpenAIPrivacyFilterPreTrainedModel): ...


__all__ = [
    "OpenAIPrivacyFilterForTokenClassification",
    "OpenAIPrivacyFilterModel",
    "OpenAIPrivacyFilterPreTrainedModel",
]
