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import logging
import os
import sys

import cutlass_library


def _cuda_install_path_from_nvcc() -> str:
    import subprocess
    # Attempt to detect CUDA_INSTALL_PATH based on location of NVCC
    result = subprocess.run(['/usr/bin/which', 'nvcc'], capture_output=True)
    if result.returncode != 0:
        raise Exception(f'Unable to find nvcc via `which` utility.')

    cuda_install_path = result.stdout.decode('utf-8').split('/bin/nvcc')[0]
    if not os.path.isdir(cuda_install_path):
        raise Exception(f'Environment variable "CUDA_INSTALL_PATH" is not defined, '
                        f'and default path of {cuda_install_path} does not exist.')

    return cuda_install_path


CUTLASS_PATH = os.getenv("CUTLASS_PATH", cutlass_library.source_path)

# Alias CUTLASS_PATH as source_path
source_path = CUTLASS_PATH

_NVCC_VERSION = None
def nvcc_version():
    global _NVCC_VERSION
    if _NVCC_VERSION is None:
        import subprocess

        # Attempt to get NVCC version
        result = subprocess.run(['nvcc', '--version'], capture_output=True)
        if result.returncode != 0:
            raise Exception('Unable to run `nvcc --version')
        _NVCC_VERSION = str(result.stdout).split(" release ")[-1].split(",")[0]
    return _NVCC_VERSION

_CUDA_INSTALL_PATH = None
def cuda_install_path():
    """
    Helper method for on-demand fetching of the CUDA installation path. This allows
    the import of CUTLASS to proceed even if NVCC is not available, preferring to
    raise this error only when an operation that needs NVCC is being performed.
    """
    global _CUDA_INSTALL_PATH
    if _CUDA_INSTALL_PATH is None:
        _CUDA_INSTALL_PATH = os.getenv("CUDA_INSTALL_PATH", _cuda_install_path_from_nvcc())
    return _CUDA_INSTALL_PATH

CACHE_FILE = "compiled_cache.db"

from cutlass_library import (
    DataType,
    EpilogueScheduleType,
    KernelScheduleType,
    MathOperation,
    LayoutType,
    OpcodeClass,
    TileDescription,
    TileSchedulerType,
)

this = sys.modules[__name__]
this.logger = logging.getLogger(__name__)

# RMM is only supported for Python 3.9+
if (sys.version_info.major == 3 and sys.version_info.minor > 8) or sys.version_info.major > 3:
    try:
        import rmm
        this.use_rmm = True
    except ImportError:
        this.use_rmm = False
else:
    this.use_rmm = False


def set_log_level(level: int):
    """
    Sets the log level

    :param log_level: severity of logging level to use. See https://docs.python.org/3/library/logging.html#logging-levels for options
    :type log_level: int
    """
    this.logger.setLevel(level)

set_log_level(logging.ERROR)

from cutlass.library_defaults import OptionRegistry
from cutlass.backend.utils.device import device_cc

this._option_registry = None
def get_option_registry():
    """
    Helper method for on-demand initialization of the options registry. This avoids building
    the registry when CUTLASS is imported.
    """
    if this._option_registry is None:
        this.logger.info("Initializing option registry")
        this._option_registry = OptionRegistry(device_cc())
    return this._option_registry

this.__version__ = '4.0.0'

from cutlass.backend import create_memory_pool
from cutlass.emit.pytorch import pytorch
from cutlass.op.gemm import Gemm
from cutlass.op.conv import Conv2d, Conv2dFprop, Conv2dDgrad, Conv2dWgrad
from cutlass.op.gemm_grouped import GroupedGemm
from cutlass.op.op import OperationBase
from cutlass.backend.evt.ir.tensor import Tensor
from cutlass.utils.lazy_import import lazy_import


this.memory_pool = None
def get_memory_pool():
    """"
    Helper method for on-demand memory pool. This avoids allocating the memory pool unnecessarily
    whe CUTLASS is imported.
    """
    if this.use_rmm and this.memory_pool is None:
        this.memory_pool = create_memory_pool(init_pool_size=2 ** 30, max_pool_size=2 ** 32)
    return this.memory_pool


base_cuda = lazy_import("cuda")
cuda = lazy_import("cuda.cuda")
cudart = lazy_import("cuda.cudart")

this._device_id = None
this._nvcc_version = None

def check_cuda_versions():
    # Strip any additional information from the CUDA version
    _cuda_version = base_cuda.__version__.split("rc")[0]
    # Check that Python CUDA version exceeds NVCC version
    this._nvcc_version = nvcc_version()
    _cuda_list = _cuda_version.split('.')
    _nvcc_list = this._nvcc_version.split('.')
    for val_cuda, val_nvcc in zip(_cuda_list, _nvcc_list):
        if int(val_cuda) < int(val_nvcc):
            raise Exception(f"Python CUDA version of {_cuda_version} must be greater than or equal to NVCC version of {this._nvcc_version}")

    if len(_nvcc_list) > len(_cuda_list):
        if len(_nvcc_list) != len(_cuda_list) + 1:
            raise Exception(f"Malformatted NVCC version of {this._nvcc_version}")
        if _nvcc_list[:-1] == _cuda_list and int(_nvcc_list[-1]) != 0:
            raise Exception(f"Python CUDA version of {_cuda_version} must be greater than or equal to NVCC version of {this._nvcc_version}")

def initialize_cuda_context():
    check_cuda_versions()

    if this._device_id is not None:
        return

    if this.use_rmm:
        # This also covers initializing the CUDA context
        get_memory_pool()

    device_id = os.getenv("CUTLASS_CUDA_DEVICE_ID")
    if device_id is None:
        if not this.use_rmm:
            # Manually call cuInit() and create context by making a runtime API call
            err, = cudart.cudaFree(0)
            if err != cudart.cudaError_t.cudaSuccess:
                raise RuntimeError(f"cudaFree failed with error {err}")

        err, device_count = cuda.cuDeviceGetCount()
        if err != cuda.CUresult.CUDA_SUCCESS:
            raise Exception(f"cuDeviceGetCount failed with error {err}")
        if device_count <= 0:
            raise Exception("No CUDA devices found")
        device_id = 0

    this._device_id = int(device_id)


def device_id() -> int:
    initialize_cuda_context()
    return this._device_id
