"""
# Copyright (c) 2025 PaddlePaddle Authors. 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.
"""

import threading
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Dict, List, Optional

import paddle
import paddle.nn.functional as F
from paddle import nn

from fastdeploy.config import FDConfig
from fastdeploy.model_executor.guided_decoding.base_guided_decoding import (
    LogitsProcessorBase,
)
from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
from fastdeploy.model_executor.layers.sample.ops import (
    apply_penalty_multi_scores,
    apply_speculative_penalty_multi_scores,
    min_p_sampling,
    top_k_top_p_sampling,
)
from fastdeploy.platforms import current_platform
from fastdeploy.worker.output import LogprobsTensors, SamplerOutput


class SamplerProcessor:
    """
    SamplingProcessor for guided decoding.
    """

    def __init__(self):
        self.async_step = None
        self.token_bitmask = None
        self.logits_processor: Dict[int, Optional[Any]] = dict()
        self.executor = ThreadPoolExecutor()
        self.logits_lock = threading.Lock()

    def add_logits_processor(
        self,
        ids: int,
        future: Optional[Any] = None,
        prefill_tokens: List[int] = [],
    ):
        """add logits processor to SamplerProcessor"""
        with self.logits_lock:
            if future is None:
                if ids in self.logits_processor:
                    del self.logits_processor[ids]
                return

            if isinstance(future, LogitsProcessorBase):
                self.logits_processor[ids] = future
                for token in prefill_tokens:
                    self.logits_processor[ids].accept_token(token)
            elif future.done():
                self.logits_processor[ids] = future.result()
                for token in prefill_tokens:
                    self.logits_processor[ids].accept_token(token)
            else:
                self.logits_processor[ids] = [future, prefill_tokens]

    def update_vocab_mask(self, skip_idx_list: List[int] = []):
        """update vocab mask. (cpu-heavy operation)"""
        if len(self.logits_processor) == 0:
            return

        with self.logits_lock:
            for idx, processor in self.logits_processor.items():
                if processor is None:
                    del self.logits_processor[idx]
                    continue

                if not isinstance(processor, LogitsProcessorBase):
                    future, prefill_tokens = self.logits_processor[idx]
                    self.logits_processor[idx] = future.result()
                    for token in prefill_tokens:
                        self.logits_processor[idx].accept_token(token)

            available_processors = None
            for processor in self.logits_processor.values():
                if processor.is_terminated():
                    continue
                available_processors = processor
            if available_processors is None:
                return

        # allocate token bitmask
        self.token_bitmask = available_processors.allocate_token_bitmask()

        with self.logits_lock:
            # fill token bitmask
            for idx, processor in self.logits_processor.items():
                if processor.is_terminated() or idx in skip_idx_list:
                    continue

                processor.fill_token_bitmask(self.token_bitmask, idx)

    def apply_token_mask(self, logits: paddle.Tensor, skip_idx_list: List[int] = []):
        """apply token mask to logits"""
        if len(self.logits_processor) == 0 or self.token_bitmask is None:
            return logits

        # self.async_step.result()
        available_processors = None
        with self.logits_lock:
            for processor in self.logits_processor.values():
                if processor.is_terminated():
                    continue
                available_processors = processor
        if available_processors is None:
            return logits

        indices = list(self.logits_processor.keys())
        mask_idx = [i for i in indices if i not in skip_idx_list]
        return available_processors.apply_token_mask(logits, self.token_bitmask, indices=mask_idx)

    def _accept_token(self, idx: int, token: int):
        """accept token"""
        if idx not in self.logits_processor:
            raise ValueError(f"Invalid index, idx: {idx}, logit_processors.keys: {self.logits_processor.keys()}")

        if self.logits_processor[idx].is_terminated():
            return

        self.logits_processor[idx].accept_token(token)

    def update_output_tokens(self, next_tokens: paddle.Tensor, skip_idx_list: List[int] = []):
        """update output tokens"""
        if len(self.logits_processor) == 0:
            return

        token_ids = next_tokens.numpy().tolist()
        with self.logits_lock:
            for idx in self.logits_processor.keys():
                token = token_ids[idx][0]
                if token < 0 or self.logits_processor[idx] is None or idx in skip_idx_list:
                    continue

                self._accept_token(idx, token)

    def pre_process(self, skip_idx_list: List[int] = []):
        """pre process before running"""
        # create async operation for guided decoding
        # TODO: support async
        self.update_vocab_mask(skip_idx_list)
        # self.async_step = self.executor.submit(self.update_vocab_mask)


class Sampler(nn.Layer):
    """
    Sampler for normal generation.
    """

    def __init__(self):
        """ """
        super().__init__()
        if (
            current_platform.is_cuda()
            or current_platform.is_xpu()
            or current_platform.is_iluvatar()
            or current_platform.is_gcu()
            or current_platform.is_dcu()
        ):
            self.forward = self.forward_cuda
        else:
            raise NotImplementedError

        self.processor = SamplerProcessor()

    def apply_logits_processor(
        self,
        ids: int,
        future: Optional[Any] = None,
        prefill_tokens: List[int] = [],
    ):
        """apply logits processor to sampler"""
        self.processor.add_logits_processor(ids, future, prefill_tokens)

    def pre_process(self, skip_idx_list: List[int] = []):
        """pre process before running"""
        self.processor.pre_process(skip_idx_list)

    def compute_logprobs(self, logits: paddle.Tensor) -> paddle.Tensor:
        """ """
        return F.log_softmax(logits, axis=-1)

    def gather_logprobs(
        self,
        logprobs: paddle.Tensor,
        num_logprobs: int,
        token_ids: paddle.Tensor,
    ) -> LogprobsTensors:
        """
        Gather logprobs for topk and sampled/prompt token.
        Args:
          logprobs: (num tokens) x (vocab) tensor
          num_logprobs: minimum number of logprobs to
                        retain per token
          token_ids: prompt tokens (if prompt logprobs)
                     or sampled tokens (if sampled
                     logprobs); 1D token ID tensor
                     with (num tokens) elements
                     Must be int64.
        Returns:
          Top-k int indices tensor, (num tokens) x (num_logprobs + 1)
          Top-k float logprobs tensor, (num tokens) x (num_logprobs + 1)
          Sampled token rank tensor, (num tokens)
        """
        assert token_ids.dtype == paddle.int64
        # Get with the logprob of the prompt or sampled token.
        token_logprobs = paddle.take_along_axis(logprobs, token_ids, axis=-1)

        # Compute the ranks of the actual token.
        token_ranks = (logprobs >= token_logprobs).sum(-1)

        if num_logprobs >= 1:
            # Find the topK values.
            topk_logprobs, topk_indices = paddle.topk(logprobs, num_logprobs, axis=-1)
            indices = paddle.concat([token_ids, topk_indices], axis=1)
            top_logprobs = paddle.concat([token_logprobs, topk_logprobs], axis=1)
        else:
            indices = token_ids
            top_logprobs = token_logprobs

        return LogprobsTensors(indices, top_logprobs, token_ranks)

    def forward_cuda(
        self,
        logits: paddle.Tensor,
        sampling_metadata: SamplingMetadata,
        skip_idx_list: List[int] = [],
    ) -> SamplerOutput:
        """ """
        num_logprobs = sampling_metadata.max_num_logprobs
        if num_logprobs is not None:
            raw_logprobs = self.compute_logprobs(logits)

        logits = self.processor.apply_token_mask(logits, skip_idx_list)

        logits = apply_penalty_multi_scores(
            sampling_metadata.pre_token_ids,
            sampling_metadata.prompt_ids,
            sampling_metadata.prompt_lens,
            logits,
            sampling_metadata.repetition_penalties,
            sampling_metadata.frequency_penalties,
            sampling_metadata.presence_penalties,
            sampling_metadata.temperature,
            sampling_metadata.bad_words_token_ids,
            sampling_metadata.step_idx,
            sampling_metadata.min_dec_lens,
            sampling_metadata.eos_token_ids,
        )

        probs = F.softmax(logits)

        probs = min_p_sampling(probs, sampling_metadata.min_p)

        _, next_tokens = top_k_top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)

        logprobs_tensors = (
            None if num_logprobs is None else self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=next_tokens)
        )

        self.processor.update_output_tokens(next_tokens, skip_idx_list)

        sampler_output = SamplerOutput(
            # The sampled tokens are expanded to 2D tensor with shape
            # [num_requests, 1], where each row represents one generated
            # token per request.
            sampled_token_ids=next_tokens,
            logprobs_tensors=logprobs_tensors,
        )

        return sampler_output


class SpeculativeSampler(nn.Layer):
    """
    Sampler for speculative generation.
    """

    def __init__(self, fd_config: FDConfig):
        """ """
        super().__init__()
        if current_platform.is_cuda():
            self.forward = self.forward_cuda
        else:
            raise NotImplementedError
        self.speculative_verify_window = fd_config.speculative_config.verify_window
        self.speculative_max_candidate_len = fd_config.speculative_config.max_candidate_len
        self.speculative_benchmark_mode = fd_config.speculative_config.benchmark_mode

    def pre_process(self, skip_idx_list: List[int] = []):
        """pre process before running"""
        pass

    def apply_logits_processor(
        self,
        ids: int,
        future: Optional[Any] = None,
        prefill_tokens: List[int] = [],
    ):
        """apply logits processor to sampler"""
        pass

    def forward_cuda(
        self,
        logits: paddle.Tensor,
        sampling_metadata: SamplingMetadata,
        max_model_len: int,
        share_inputs: List[paddle.Tensor],
    ) -> paddle.Tensor:
        """ """

        from fastdeploy.model_executor.ops.gpu import speculate_verify, top_p_candidates

        logits = apply_speculative_penalty_multi_scores(
            sampling_metadata.pre_token_ids,
            logits,
            sampling_metadata.repetition_penalties,
            sampling_metadata.frequency_penalties,
            sampling_metadata.presence_penalties,
            sampling_metadata.temperature,
            sampling_metadata.bad_words_token_ids,
            sampling_metadata.step_idx,
            sampling_metadata.min_dec_lens,
            sampling_metadata.eos_token_ids,
            share_inputs["seq_lens_this_time"],
            share_inputs["output_padding_offset"],
            share_inputs["output_cum_offsets"],
            max_model_len,
        )

        probs = F.softmax(logits)

        verify_scores, verify_tokens, actual_candidate_len = top_p_candidates(
            probs,
            sampling_metadata.top_p,
            share_inputs["output_padding_offset"],
            self.speculative_max_candidate_len,
            max_model_len,
        )

        speculate_verify(
            share_inputs["accept_tokens"],
            share_inputs["accept_num"],
            share_inputs["step_idx"],
            share_inputs["stop_flags"],
            share_inputs["seq_lens_encoder"],
            share_inputs["seq_lens_decoder"],
            share_inputs[
                "draft_tokens"
            ],  # Both input and output, need to write the last 1 token accepted to position 0.
            share_inputs["seq_lens_this_time"],
            verify_tokens,
            verify_scores,
            share_inputs["max_dec_len"],
            sampling_metadata.eos_token_ids,
            share_inputs["is_block_step"],
            share_inputs["output_cum_offsets"],
            actual_candidate_len,
            share_inputs["actual_draft_token_num"],
            sampling_metadata.top_p,
            max_model_len,
            self.speculative_verify_window,
            True,  # enable_topp
            self.speculative_benchmark_mode,
        )

        return None


class MTPSampler(nn.Layer):
    """ """

    def __init__(self, fd_config: FDConfig):
        """ """
        super().__init__()
        if current_platform.is_cuda():
            self.forward = self.forward_cuda
        else:
            raise NotImplementedError

    def pre_process(self, skip_idx_list: List[int] = []):
        """pre process before running"""
        pass

    def apply_logits_processor(
        self,
        ids: int,
        future: Optional[Any] = None,
        prefill_tokens: List[int] = [],
    ):
        """apply logits processor to sampler"""
        pass

    def forward_cuda(
        self,
        logits: paddle.Tensor,
        sampling_metadata: SamplingMetadata,
        max_model_len: int,
        share_inputs: List[paddle.Tensor],
    ) -> paddle.Tensor:
        """ """
        logits = apply_speculative_penalty_multi_scores(
            sampling_metadata.pre_token_ids,
            logits,
            sampling_metadata.repetition_penalties,
            sampling_metadata.frequency_penalties,
            sampling_metadata.presence_penalties,
            sampling_metadata.temperature,
            sampling_metadata.bad_words_token_ids,
            sampling_metadata.step_idx,
            sampling_metadata.min_dec_lens,
            sampling_metadata.eos_token_ids,
            share_inputs["seq_lens_this_time"],
            share_inputs["seq_lens_encoder"],
            share_inputs["seq_lens_decoder"],
            max_model_len,
        )
        probs = F.softmax(logits)

        _, next_tokens = top_k_top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
        return next_tokens