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import logging
import os

import torch
from sglang.srt.distributed import (
    get_pp_group,
    get_tp_group,
    get_world_group,
    set_custom_all_reduce,
    set_mscclpp_all_reduce,
    set_torch_symm_mem_all_reduce,
)
from sglang.srt.layers.dp_attention import (
    get_attention_tp_group,
    initialize_dp_attention,
)
from sglang.srt.model_executor.model_runner import ModelRunner
from sglang.srt.utils import (
    cpu_has_amx_support,
    get_available_gpu_memory,
    get_bool_env_var,
    is_hip,
    is_npu,
    monkey_patch_p2p_access_check,
)

from .patch import (
    init_distributed_environment,
    initialize_dp_attention,
    initialize_model_parallel,
)

_is_hip = is_hip()
_is_npu = is_npu()
_is_cpu_amx_available = cpu_has_amx_support()

# Use a small KV cache pool size for tests in CI
SGLANG_CI_SMALL_KV_SIZE = os.getenv("SGLANG_CI_SMALL_KV_SIZE", None)

# Detect stragger ranks in model loading
UNBALANCED_MODEL_LOADING_TIMEOUT_S = 300

logger = logging.getLogger(__name__)


class SGLangRunner(ModelRunner):

    def init_torch_distributed(self):
        logger.info("Init torch distributed begin.")

        try:
            torch.get_device_module(self.device).set_device(self.gpu_id)
        except Exception:
            logger.warning(
                f"Context: {self.device=} {self.gpu_id=} {os.environ.get('CUDA_VISIBLE_DEVICES')=} {self.tp_rank=} {self.tp_size=}"
            )
            raise

        if self.device == "cuda":
            if self.server_args.elastic_ep_backend == "mooncake":
                backend = "mooncake"
                if self.server_args.mooncake_ib_device:
                    mooncake_ib_device = self.server_args.mooncake_ib_device.split(",")
                    try:
                        from mooncake import ep as mooncake_ep

                        mooncake_ep.set_device_filter(mooncake_ib_device)
                    except:
                        pass  # A warning will be raised in `init_distributed_environment`
            else:
                backend = "nccl"
        elif self.device == "xpu":
            backend = "xccl"
        elif self.device == "hpu":
            backend = "hccl"
        elif self.device == "cpu":
            backend = "gloo"
        elif self.device == "npu":
            backend = "hccl"

        before_avail_memory = get_available_gpu_memory(self.device, self.gpu_id)
        if not self.server_args.enable_p2p_check:
            monkey_patch_p2p_access_check()

        if self.server_args.dist_init_addr:
            dist_init_method = f"tcp://{self.server_args.dist_init_addr}"
        else:
            dist_init_method = f"tcp://127.0.0.1:{self.dist_port}"
        set_custom_all_reduce(not self.server_args.disable_custom_all_reduce)
        set_mscclpp_all_reduce(self.server_args.enable_mscclpp)
        set_torch_symm_mem_all_reduce(self.server_args.enable_torch_symm_mem)

        if not self.is_draft_worker:
            if self.device == "cpu":
                if _is_cpu_amx_available:
                    # Bind OpenMP threads to CPU cores
                    torch.ops.sgl_kernel.init_cpu_threads_env(self.local_omp_cpuid)

                    # Set local size to hint SGLang to use shared memory based AllReduce
                    os.environ["LOCAL_SIZE"] = str(self.tp_size)
                    torch.ops.sgl_kernel.initialize(self.tp_size, self.tp_rank)

                    @torch.library.register_fake("sgl_kernel::shm_allgather")
                    def _(data, dim):
                        return torch.cat([data] * self.tp_size, dim=dim)

                else:
                    logger.warning(
                        "init_cpu_threads_env and shared memory based AllReduce is disabled since intel amx backend is not available"
                    )

            # Only initialize the distributed environment on the target model worker.
            init_distributed_environment(
                backend=backend,
                world_size=self.tp_size * self.pp_size,
                rank=self.tp_size * self.pp_rank + self.tp_rank,
                local_rank=self.gpu_id,
            )
            # NOTE: Updated for sglang 0.5.9
            # - Removed torch_compile parameter (no longer supported)
            # - Added new parameters: attention_data_parallel_size, attention_context_model_parallel_size, moe_data_model_parallel_size

            # Debug: Print the values
            dp_size = getattr(self.server_args, "dp_size", 1)
            attn_cp_size = getattr(self.server_args, "attn_cp_size", 1)
            moe_dp_size = getattr(self.server_args, "moe_dp_size", 1)
            print(
                f"[DEBUG] tp_size={self.tp_size}, dp_size={dp_size}, attn_cp_size={attn_cp_size}, moe_dp_size={moe_dp_size}"
            )

            initialize_model_parallel(
                tensor_model_parallel_size=self.tp_size,
                pipeline_model_parallel_size=self.pp_size,
                expert_model_parallel_size=self.moe_ep_size,
                attention_data_parallel_size=dp_size,
                attention_context_model_parallel_size=attn_cp_size,
                moe_data_model_parallel_size=moe_dp_size,
                duplicate_tp_group=self.server_args.enable_pdmux,
            )
            initialize_dp_attention(
                server_args=self.server_args,
                model_config=self.model_config,
            )

        min_per_gpu_memory = get_available_gpu_memory(
            self.device,
            self.gpu_id,
            distributed=get_world_group().world_size > 1,
            cpu_group=get_world_group().cpu_group,
        )
        self.tp_group = get_tp_group()
        self.pp_group = get_pp_group()
        self.attention_tp_group = get_attention_tp_group()

        # Check memory for tensor parallelism
        local_gpu_memory = get_available_gpu_memory(self.device, self.gpu_id)
        if self.tp_size > 1 and not self.is_draft_worker:
            if min_per_gpu_memory < local_gpu_memory * 0.9:
                if get_bool_env_var("SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK"):
                    logger.warning(
                        "The memory capacity is unbalanced. Some GPUs may be occupied by other processes. "
                        f"{min_per_gpu_memory=}, {local_gpu_memory=}, {local_gpu_memory * 0.9=}"
                    )
                else:
                    raise ValueError(
                        "The memory capacity is unbalanced. Some GPUs may be occupied by other processes. "
                        f"{min_per_gpu_memory=}, {local_gpu_memory=}, {local_gpu_memory * 0.9=}"
                    )

        logger.info(
            f"Init torch distributed ends. mem usage={(before_avail_memory - local_gpu_memory):.2f} GB"
        )
        return min_per_gpu_memory

	import logging
	import os

	import torch
	from sglang.srt.distributed import (
	get_pp_group,
	get_tp_group,
	get_world_group,
	set_custom_all_reduce,
	set_mscclpp_all_reduce,
	set_torch_symm_mem_all_reduce,
	)
	from sglang.srt.layers.dp_attention import (
	get_attention_tp_group,
	initialize_dp_attention,
	)
	from sglang.srt.model_executor.model_runner import ModelRunner
	from sglang.srt.utils import (
	cpu_has_amx_support,
	get_available_gpu_memory,
	get_bool_env_var,
	is_hip,
	is_npu,
	monkey_patch_p2p_access_check,
	)

	from .patch import (
	init_distributed_environment,
	initialize_dp_attention,
	initialize_model_parallel,
	)

	_is_hip = is_hip()
	_is_npu = is_npu()
	_is_cpu_amx_available = cpu_has_amx_support()

	# Use a small KV cache pool size for tests in CI
	SGLANG_CI_SMALL_KV_SIZE = os.getenv("SGLANG_CI_SMALL_KV_SIZE", None)

	# Detect stragger ranks in model loading
	UNBALANCED_MODEL_LOADING_TIMEOUT_S = 300

	logger = logging.getLogger(__name__)


	class SGLangRunner(ModelRunner):

	def init_torch_distributed(self):
	logger.info("Init torch distributed begin.")

	try:
	torch.get_device_module(self.device).set_device(self.gpu_id)
	except Exception:
	logger.warning(
	f"Context: {self.device=} {self.gpu_id=} {os.environ.get('CUDA_VISIBLE_DEVICES')=} {self.tp_rank=} {self.tp_size=}"
	)
	raise

	if self.device == "cuda":
	if self.server_args.elastic_ep_backend == "mooncake":
	backend = "mooncake"
	if self.server_args.mooncake_ib_device:
	mooncake_ib_device = self.server_args.mooncake_ib_device.split(",")
	try:
	from mooncake import ep as mooncake_ep

	mooncake_ep.set_device_filter(mooncake_ib_device)
	except:
	pass # A warning will be raised in `init_distributed_environment`
	else:
	backend = "nccl"
	elif self.device == "xpu":
	backend = "xccl"
	elif self.device == "hpu":
	backend = "hccl"
	elif self.device == "cpu":
	backend = "gloo"
	elif self.device == "npu":
	backend = "hccl"

	before_avail_memory = get_available_gpu_memory(self.device, self.gpu_id)
	if not self.server_args.enable_p2p_check:
	monkey_patch_p2p_access_check()

	if self.server_args.dist_init_addr:
	dist_init_method = f"tcp://{self.server_args.dist_init_addr}"
	else:
	dist_init_method = f"tcp://127.0.0.1:{self.dist_port}"
	set_custom_all_reduce(not self.server_args.disable_custom_all_reduce)
	set_mscclpp_all_reduce(self.server_args.enable_mscclpp)
	set_torch_symm_mem_all_reduce(self.server_args.enable_torch_symm_mem)

	if not self.is_draft_worker:
	if self.device == "cpu":
	if _is_cpu_amx_available:
	# Bind OpenMP threads to CPU cores
	torch.ops.sgl_kernel.init_cpu_threads_env(self.local_omp_cpuid)

	# Set local size to hint SGLang to use shared memory based AllReduce
	os.environ["LOCAL_SIZE"] = str(self.tp_size)
	torch.ops.sgl_kernel.initialize(self.tp_size, self.tp_rank)

	@torch.library.register_fake("sgl_kernel::shm_allgather")
	def _(data, dim):
	return torch.cat([data] * self.tp_size, dim=dim)

	else:
	logger.warning(
	"init_cpu_threads_env and shared memory based AllReduce is disabled since intel amx backend is not available"
	)

	# Only initialize the distributed environment on the target model worker.
	init_distributed_environment(
	backend=backend,
	world_size=self.tp_size * self.pp_size,
	rank=self.tp_size * self.pp_rank + self.tp_rank,
	local_rank=self.gpu_id,
	)
	# NOTE: Updated for sglang 0.5.9
	# - Removed torch_compile parameter (no longer supported)
	# - Added new parameters: attention_data_parallel_size, attention_context_model_parallel_size, moe_data_model_parallel_size

	# Debug: Print the values
	dp_size = getattr(self.server_args, "dp_size", 1)
	attn_cp_size = getattr(self.server_args, "attn_cp_size", 1)
	moe_dp_size = getattr(self.server_args, "moe_dp_size", 1)
	print(
	f"[DEBUG] tp_size={self.tp_size}, dp_size={dp_size}, attn_cp_size={attn_cp_size}, moe_dp_size={moe_dp_size}"
	)

	initialize_model_parallel(
	tensor_model_parallel_size=self.tp_size,
	pipeline_model_parallel_size=self.pp_size,
	expert_model_parallel_size=self.moe_ep_size,
	attention_data_parallel_size=dp_size,
	attention_context_model_parallel_size=attn_cp_size,
	moe_data_model_parallel_size=moe_dp_size,
	duplicate_tp_group=self.server_args.enable_pdmux,
	)
	initialize_dp_attention(
	server_args=self.server_args,
	model_config=self.model_config,
	)

	min_per_gpu_memory = get_available_gpu_memory(
	self.device,
	self.gpu_id,
	distributed=get_world_group().world_size > 1,
	cpu_group=get_world_group().cpu_group,
	)
	self.tp_group = get_tp_group()
	self.pp_group = get_pp_group()
	self.attention_tp_group = get_attention_tp_group()

	# Check memory for tensor parallelism
	local_gpu_memory = get_available_gpu_memory(self.device, self.gpu_id)
	if self.tp_size > 1 and not self.is_draft_worker:
	if min_per_gpu_memory < local_gpu_memory * 0.9:
	if get_bool_env_var("SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK"):
	logger.warning(
	"The memory capacity is unbalanced. Some GPUs may be occupied by other processes. "
	f"{min_per_gpu_memory=}, {local_gpu_memory=}, {local_gpu_memory * 0.9=}"
	)
	else:
	raise ValueError(
	"The memory capacity is unbalanced. Some GPUs may be occupied by other processes. "
	f"{min_per_gpu_memory=}, {local_gpu_memory=}, {local_gpu_memory * 0.9=}"
	)

	logger.info(
	f"Init torch distributed ends. mem usage={(before_avail_memory - local_gpu_memory):.2f} GB"
	)
	return min_per_gpu_memory