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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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from typing import Callable, Optional

import torch
from transformers.cache_utils import Cache
from transformers.modeling_flash_attention_utils import _flash_attention_forward
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
from transformers.utils import logging

from verl.utils.ulysses import (
    gather_heads_scatter_seq,
    gather_seq_scatter_heads,
    get_ulysses_sequence_parallel_world_size,
    validate_ulysses_config,
)

logger = logging.get_logger(__name__)


def qwen2_flash_attn_forward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Cache] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    cache_position: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
):
    """
    Adapted from transformers 4.47.1 to support Ulysses sequence parallelism.

    NOTE: This function is only tested on transformers versions between 4.45.0 and 4.47.1.
    """
    bsz, q_len, _ = hidden_states.size()

    query_states = self.q_proj(hidden_states)
    key_states = self.k_proj(hidden_states)
    value_states = self.v_proj(hidden_states)

    query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
    key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
    value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

    ########## AlltoAll for Ulysses ##########
    ulysses_sp_size = get_ulysses_sequence_parallel_world_size()

    if ulysses_sp_size > 1:
        validate_ulysses_config(self.num_heads, ulysses_sp_size)

        # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim)
        query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1)
        key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1)
        value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1)

    full_q_len = query_states.size(2)  # full seq length

    if position_embeddings is None:
        logger.warning_once(
            "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
            "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
            "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
            "removed and `position_embeddings` will be mandatory."
        )
        cos, sin = self.rotary_emb(value_states, position_ids)
    else:
        cos, sin = position_embeddings
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

    if past_key_value is not None:
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

    # repeat k/v heads if n_kv_heads < n_heads
    key_states = repeat_kv(key_states, self.num_key_value_groups)
    value_states = repeat_kv(value_states, self.num_key_value_groups)
    dropout_rate = 0.0 if not self.training else self.attention_dropout

    # In PEFT, usually we cast the layer norms in float32 for training stability reasons
    # therefore the input hidden states gets silently casted in float32. Hence, we need
    # cast them back in float16 just to be sure everything works as expected.
    input_dtype = query_states.dtype
    if input_dtype == torch.float32:
        if torch.is_autocast_enabled():
            target_dtype = torch.get_autocast_gpu_dtype()
        # Handle the case where the model is quantized
        elif hasattr(self.config, "_pre_quantization_dtype"):
            target_dtype = self.config._pre_quantization_dtype
        else:
            target_dtype = self.q_proj.weight.dtype

        logger.warning_once(
            f"The input hidden states seems to be silently casted in float32, this might be related to "
            f"the fact you have upcasted embedding or layer norm layers in float32. We will cast back the "
            f"input in {target_dtype}."
        )

        query_states = query_states.to(target_dtype)
        key_states = key_states.to(target_dtype)
        value_states = value_states.to(target_dtype)

    # Reashape to the expected shape for Flash Attention
    query_states = query_states.transpose(1, 2)
    key_states = key_states.transpose(1, 2)
    value_states = value_states.transpose(1, 2)

    if (
        self.config.use_sliding_window
        and getattr(self.config, "sliding_window", None) is not None
        and self.layer_idx >= self.config.max_window_layers
    ):
        sliding_window = self.config.sliding_window
    else:
        sliding_window = None

    attn_output = _flash_attention_forward(
        query_states,
        key_states,
        value_states,
        attention_mask,
        full_q_len,
        position_ids=position_ids,
        dropout=dropout_rate,
        sliding_window=sliding_window,
        is_causal=self.is_causal,
        use_top_left_mask=self._flash_attn_uses_top_left_mask,
    )

    # use full_q_len to reshape
    attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous()
    ########## AlltoAll for Ulysses ##########
    if ulysses_sp_size > 1:
        attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2)
    attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value


def qwen2_attn_forward(
    self,
    hidden_states: torch.Tensor,
    position_embeddings: tuple[torch.Tensor, torch.Tensor],
    attention_mask: Optional[torch.Tensor],
    past_key_value: Optional[Cache] = None,
    cache_position: Optional[torch.LongTensor] = None,
    **kwargs,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
    """
    Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0.

    NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.50.0.
    """
    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS

    bsz, q_len, _ = hidden_states.shape
    hidden_shape = (bsz, q_len, -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)

    ########## AlltoAll for Ulysses ##########
    ulysses_sp_size = get_ulysses_sequence_parallel_world_size()

    if ulysses_sp_size > 1:
        validate_ulysses_config(self.config.num_attention_heads, ulysses_sp_size)

        # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim)
        query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1)
        key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1)
        value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1)

    full_q_len = query_states.size(2)

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

    if past_key_value is not None:
        # sin and cos are specific to RoPE models; cache_position needed for the static cache
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

    sliding_window = None
    if (
        self.config.use_sliding_window
        and getattr(self.config, "sliding_window", None) is not None
        and self.layer_idx >= self.config.max_window_layers
    ):
        sliding_window = self.config.sliding_window

    from transformers.models.qwen2.modeling_qwen2 import eager_attention_forward

    attention_interface: Callable = eager_attention_forward
    if self.config._attn_implementation != "eager":
        if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
            logger.warning_once(
                "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. "
                "Falling back to eager attention. This warning can be removed using the argument "
                '`attn_implementation="eager"` when loading the model.'
            )
        else:
            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.attention_dropout,
        scaling=self.scaling,
        sliding_window=sliding_window,  # main diff with Llama
        **kwargs,
    )

    attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous()
    ########## AlltoAll for Ulysses ##########
    if ulysses_sp_size > 1:
        # (bsz, seq_len, n_head/n, head_dim) -> (bsz, seq_len/n, n_head, head_dim)
        attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2)
    attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
    attn_output = self.o_proj(attn_output)
    return attn_output, attn_weights