import logging
from typing import List

import torch
import torch.distributed as dist
from torch import nn

from sglang.srt.distributed import get_tensor_model_parallel_group
from sglang.srt.layers.dp_attention import get_attention_tp_group
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
from sglang.srt.utils import crash_on_warnings, get_bool_env_var, is_cuda_available

if is_cuda_available():
    from sgl_kernel import (
        min_p_sampling_from_probs,
        top_k_renorm_prob,
        top_k_top_p_sampling_from_probs,
        top_p_renorm_prob,
    )


logger = logging.getLogger(__name__)

SYNC_TOKEN_IDS_ACROSS_TP = get_bool_env_var("SYNC_TOKEN_IDS_ACROSS_TP")


class Sampler(nn.Module):
    def __init__(self):
        super().__init__()
        self.use_nan_detectioin = global_server_args_dict["enable_nan_detection"]
        self.tp_sync_group = get_tensor_model_parallel_group().device_group

        if global_server_args_dict["enable_dp_attention"]:
            self.tp_sync_group = get_attention_tp_group().device_group

    def forward(
        self,
        logits_output: LogitsProcessorOutput,
        sampling_info: SamplingBatchInfo,
        return_logprob: bool,
        top_logprobs_nums: List[int],
    ):
        logits = logits_output.next_token_logits

        # Apply the custom logit processors if registered in the sampling info.
        if sampling_info.has_custom_logit_processor:
            self._apply_custom_logit_processor(logits, sampling_info)

        if self.use_nan_detectioin and torch.any(torch.isnan(logits)):
            logger.warning("Detected errors during sampling! NaN in the logits.")
            logits = torch.where(
                torch.isnan(logits), torch.full_like(logits, -1e5), logits
            )
            if crash_on_warnings():
                raise ValueError("Detected errors during sampling! NaN in the logits.")

        if sampling_info.is_all_greedy:
            # Use torch.argmax if all requests use greedy sampling
            batch_next_token_ids = torch.argmax(logits, -1)
            if return_logprob:
                logprobs = torch.nn.functional.log_softmax(logits, dim=-1)
        else:
            # Post process logits
            logits.div_(sampling_info.temperatures)
            probs = torch.softmax(logits, dim=-1)
            del logits

            if global_server_args_dict["sampling_backend"] == "flashinfer":
                if return_logprob:
                    # NOTE: the top_p_renorm_prob from flashinfer has numerical problems,
                    # https://github.com/flashinfer-ai/flashinfer/issues/708
                    # so we use the torch implementation.
                    logprobs = torch.log(
                        top_p_normalize_probs_torch(probs, sampling_info.top_ps)
                    )

                max_top_k_round, batch_size = 32, probs.shape[0]
                uniform_samples = torch.rand(
                    (max_top_k_round, batch_size), device=probs.device
                )
                if sampling_info.need_min_p_sampling:
                    probs = top_k_renorm_prob(probs, sampling_info.top_ks)
                    probs = top_p_renorm_prob(probs, sampling_info.top_ps)
                    batch_next_token_ids, success = min_p_sampling_from_probs(
                        probs, uniform_samples, sampling_info.min_ps
                    )
                else:
                    batch_next_token_ids, success = top_k_top_p_sampling_from_probs(
                        probs,
                        uniform_samples,
                        sampling_info.top_ks,
                        sampling_info.top_ps,
                        filter_apply_order="joint",
                    )

                if self.use_nan_detectioin and not torch.all(success):
                    logger.warning("Detected errors during sampling!")
                    batch_next_token_ids = torch.zeros_like(batch_next_token_ids)

            elif global_server_args_dict["sampling_backend"] == "pytorch":
                # A slower fallback implementation with torch native operations.
                batch_next_token_ids = top_k_top_p_min_p_sampling_from_probs_torch(
                    probs,
                    sampling_info.top_ks,
                    sampling_info.top_ps,
                    sampling_info.min_ps,
                    sampling_info.need_min_p_sampling,
                )
                if return_logprob:
                    logprobs = torch.log(
                        top_p_normalize_probs_torch(probs, sampling_info.top_ps)
                    )
            else:
                raise ValueError(
                    f"Invalid sampling backend: {global_server_args_dict['sampling_backend']}"
                )

        # Attach logprobs to logits_output (in-place modification)
        if return_logprob:
            if any(x > 0 for x in top_logprobs_nums):
                (
                    logits_output.next_token_top_logprobs_val,
                    logits_output.next_token_top_logprobs_idx,
                ) = get_top_logprobs(logprobs, top_logprobs_nums)

            logits_output.next_token_logprobs = logprobs[
                torch.arange(len(batch_next_token_ids), device=sampling_info.device),
                batch_next_token_ids,
            ]

        if SYNC_TOKEN_IDS_ACROSS_TP or sampling_info.grammars:
            # For performance reasons, SGLang does not sync the final token IDs across TP ranks by default.
            # This saves one all-reduce, but the correctness of this approach depends on the determinism of several operators:
            # the last all-reduce, the last lm_head matmul, and all sampling kernels.
            # These kernels are deterministic in most cases, but there are some rare instances where they are not deterministic.
            # In such cases, enable this env variable to prevent hanging due to TP ranks becoming desynchronized.
            # When using xgrammar, this becomes more likely so we also do the sync when grammar is used.

            torch.distributed.all_reduce(
                batch_next_token_ids,
                op=dist.ReduceOp.MIN,
                group=self.tp_sync_group,
            )

        return batch_next_token_ids.to(torch.int32)

    def _apply_custom_logit_processor(
        self, logits: torch.Tensor, sampling_batch_info: SamplingBatchInfo
    ):
        """Apply custom logit processors to the logits.
        This function will modify the logits in-place."""

        assert logits.shape[0] == len(sampling_batch_info), (
            f"The batch size of logits ({logits.shape[0]}) does not match the batch size of "
            f"sampling_batch_info ({len(sampling_batch_info)})"
        )

        for _, (
            processor,
            batch_mask,
        ) in sampling_batch_info.custom_logit_processor.items():
            # Get the batch indices that need to be processed
            batch_indices = batch_mask.nonzero(as_tuple=True)[0]

            assert batch_mask.shape[0] == len(sampling_batch_info), (
                f"The number of batch mask ({batch_mask.shape[0]}) does not match the number of "
                f"sampling_batch_info ({len(sampling_batch_info)})"
            )

            # Apply the processor to the logits
            logits[batch_mask] = processor(
                logits[batch_mask],
                [sampling_batch_info.custom_params[i] for i in batch_indices],
            )

            logger.debug(
                f"Custom logit processor {processor.__class__.__name__} is applied."
            )


def top_k_top_p_min_p_sampling_from_probs_torch(
    probs: torch.Tensor,
    top_ks: torch.Tensor,
    top_ps: torch.Tensor,
    min_ps: torch.Tensor,
    need_min_p_sampling: bool,
):
    """A top-k, top-p and min-p sampling implementation with native pytorch operations."""
    probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
    probs_sum = torch.cumsum(probs_sort, dim=-1)
    probs_sort[
        torch.arange(0, probs.shape[-1], device=probs.device).view(1, -1)
        >= top_ks.view(-1, 1)
    ] = 0.0
    probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0

    if need_min_p_sampling:
        min_p_thresholds = probs_sort[:, 0] * min_ps
        probs_sort[probs_sort < min_p_thresholds.view(-1, 1)] = 0.0

    sampled_index = torch.multinomial(probs_sort, num_samples=1)
    # int32 range is enough to represent the token ids
    probs_idx = probs_idx.to(torch.int32)
    batch_next_token_ids = torch.gather(probs_idx, dim=1, index=sampled_index).view(-1)
    return batch_next_token_ids


def top_p_normalize_probs_torch(
    probs: torch.Tensor,
    top_ps: torch.Tensor,
):
    # See also top_k_top_p_min_p_sampling_from_probs_torch
    probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
    probs_sum = torch.cumsum(probs_sort, dim=-1)
    probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
    return torch.zeros_like(probs_sort).scatter_(-1, probs_idx, probs_sort)


def get_top_logprobs(logprobs: torch.Tensor, top_logprobs_nums: List[int]):
    max_k = max(top_logprobs_nums)
    ret = logprobs.topk(max_k, dim=1)
    values = ret.values.tolist()
    indices = ret.indices.tolist()

    output_top_logprobs_val = []
    output_top_logprobs_idx = []
    for i, k in enumerate(top_logprobs_nums):
        output_top_logprobs_val.append(values[i][:k])
        output_top_logprobs_idx.append(indices[i][:k])
    return output_top_logprobs_val, output_top_logprobs_idx
