# -*- coding: utf-8 -*-
#
#    Project: Azimuthal integration
#             https://github.com/silx-kit
#
#    Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
#    Principal author:       Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <http://www.gnu.org/licenses/>.
#

__author__ = "Jerome Kieffer"
__license__ = "GPLv3"
__date__ = "27/10/2016"
__copyright__ = "2012, ESRF, Grenoble"
__contact__ = "jerome.kieffer@esrf.fr"

import gc
import logging
import threading
import numpy

from .opencl import ocl, pyopencl, allocate_cl_buffers, release_cl_buffers
from .utils import concatenate_cl_kernel
if pyopencl:
    mf = pyopencl.mem_flags
else:
    raise ImportError("pyopencl is not installed")

try:
    from .ext.fastcrc import crc32
except:
    from zlib import crc32
logger = logging.getLogger("pyFAI.ocl_azim_csr")


class OCL_CSR_Integrator(object):

    def __init__(self, lut, image_size, devicetype="all",
                 block_size=32,
                 platformid=None, deviceid=None,
                 checksum=None, profile=False,
                 empty=None):
        """
        :param lut: 3-tuple of arrays
            data: coefficient of the matrix in a 1D vector of float32 - size of nnz
            indices: Column index position for the data (same size as data)
            indptr: row pointer indicates the start of a given row. len nbin+1
        :param image_size:
        :param devicetype: can be "cpu","gpu","acc" or "all"
        :param block_size: the chosen size for WORKGROUP_SIZE
        :param platformid: number of the platform as given by clinfo
        :type platformid: int
        :param deviceid: number of the device as given by clinfo
        :type deviceid: int
        :param checksum: pre - calculated checksum to prevent re - calculating it :)
        :param profile: store profiling elements
        :param empty: value to be assigned to bins without contribution from any pixel
        """
        self._sem = threading.Semaphore()
        self._data = lut[0]
        self._indices = lut[1]
        self._indptr = lut[2]
        self.bins = self._indptr.shape[0] - 1
        if self._data.shape[0] != self._indices.shape[0]:
            raise RuntimeError("data.shape[0] != indices.shape[0]")
        self.data_size = self._data.shape[0]
        self.size = image_size
        self.profile = profile
        self.empty = empty or 0.0
        if not checksum:
            checksum = crc32(self._data)
        self.on_device = {"data": checksum,
                          "dark": None,
                          "flat": None,
                          "polarization": None,
                          "solidangle": None}
        self._cl_kernel_args = {}
        self._cl_mem = {}
        self.events = []
        if (platformid is None) and (deviceid is None):
            platformid, deviceid = ocl.select_device(devicetype)
        elif platformid is None:
            platformid = 0
        elif deviceid is None:
            deviceid = 0
        self.platform = ocl.platforms[platformid]
        self.device = self.platform.devices[deviceid]
        self.device_type = self.device.type
        self.BLOCK_SIZE = min(block_size, self.device.max_work_group_size)
        self.workgroup_size = self.BLOCK_SIZE,  # Note this is a tuple
        self.wdim_bins = (self.bins * self.BLOCK_SIZE),
        self.wdim_data = (self.size + self.BLOCK_SIZE - 1) & ~(self.BLOCK_SIZE - 1),  # noqa
        try:
            devices = [pyopencl.get_platforms()[platformid].get_devices()[deviceid]]  # noqa
            self._ctx = pyopencl.Context(devices=devices)
            if self.profile:
                properties = pyopencl.command_queue_properties.PROFILING_ENABLE
                self._queue = pyopencl.CommandQueue(self._ctx, properties=properties)  # noqa
            else:
                self._queue = pyopencl.CommandQueue(self._ctx)
            self._allocate_buffers()
            self._compile_kernels()
            self._set_kernel_arguments()
        except pyopencl.MemoryError as error:
            raise MemoryError(error)

        ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["data"], self._data)  # noqa
        if self.profile:
            self.events.append(("copy Coefficient data", ev))
        ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["indices"], self._indices)  # noqa
        if self.profile:
            self.events.append(("copy Row Index data", ev))
        ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["indptr"], self._indptr)  # noqa
        if self.profile:
            self.events.append(("copy Column Pointer data", ev))

    def __del__(self):
        """
        Destructor: release all buffers
        """
        self._free_kernels()
        self._free_buffers()
        self._queue = None
        self._ctx = None
        gc.collect()

    def _allocate_buffers(self):
        """Allocate OpenCL buffers required for a specific configuration

        Note that an OpenCL context also requires some memory, as well
        as Event and other OpenCL functionalities which cannot and are
        not taken into account here. The memory required by a context
        varies depending on the device. Typical for GTX580 is 65Mb but
        for a 9300m is ~15Mb In addition, a GPU will always have at
        least 3-5Mb of memory in use. Unfortunately, OpenCL does NOT
        have a built-in way to check the actual free memory on a
        device, only the total memory.
        """
        buffers = [
            ("data", mf.READ_WRITE, numpy.float32, self.data_size),
            ("indices", mf.READ_WRITE, numpy.int32, self.data_size),
            ("indptr", mf.READ_WRITE, numpy.int32, self.bins + 1),
            ("outData", mf.WRITE_ONLY, numpy.float32, self.bins),
            ("outCount", mf.WRITE_ONLY, numpy.float32, self.bins),
            ("outMerge", mf.WRITE_ONLY, numpy.float32, self.bins),
            ("image_raw", mf.READ_ONLY, numpy.float32, self.size),
            ("image", mf.READ_WRITE, numpy.float32, self.size),
            ("dark", mf.READ_ONLY, numpy.float32, self.size),
            ("flat", mf.READ_ONLY, numpy.float32, self.size),
            ("polarization", mf.READ_ONLY, numpy.float32, self.size),
            ("solidangle", mf.READ_ONLY, numpy.float32, self.size),
        ]

        if self.size < self.BLOCK_SIZE:
            raise RuntimeError("Fatal error in _allocate_buffers. size (%d) must be >= BLOCK_SIZE (%d)\n", self.size, self.BLOCK_SIZE)  # noqa

        self._cl_mem = allocate_cl_buffers(buffers, self.device, self._ctx)

    def _free_buffers(self):
        """
        free all memory allocated on the device
        """
        self._cl_mem = release_cl_buffers(self._cl_mem)

    def _compile_kernels(self, kernel_file=None):
        """
        Call the OpenCL compiler
        :param kernel_file: path to the kernel (by default use the one in the src directory)  # noqa
        """
        # concatenate all needed source files into a single openCL module
        kernel_file = kernel_file or "ocl_azim_CSR.cl"
        kernel_src = concatenate_cl_kernel(["preprocess.cl", kernel_file])

        compile_options = "-D NBINS=%i  -D NIMAGE=%i -D WORKGROUP_SIZE=%i -D ON_CPU=%i" % \
                          (self.bins, self.size, self.BLOCK_SIZE, int(self.device_type == "CPU"))  # noqa
        logger.info("Compiling file %s with options %s", kernel_file, compile_options)  # noqa
        try:
            self._program = pyopencl.Program(self._ctx, kernel_src).build(options=compile_options)  # noqa
        except pyopencl.MemoryError as error:
            raise MemoryError(error)

    def _free_kernels(self):
        """
        free all kernels
        """
        for kernel in self._cl_kernel_args:
            self._cl_kernel_args[kernel] = []
        self._program = None

    def _set_kernel_arguments(self):
        """Tie arguments of OpenCL kernel-functions to the actual kernels

        set_kernel_arguments() is a private method, called by
        configure(). It uses the dictionary _cl_kernel_args. Note
        that by default, since TthRange is disabled, the integration
        kernels have tth_min_max tied to the tthRange argument slot.
        When setRange is called it replaces that argument with
        tthRange low and upper bounds. When unsetRange is called, the
        argument slot is reset to tth_min_max.
        """
        self._cl_kernel_args["corrections"] = [self._cl_mem["image"],
                                               numpy.int32(0),
                                               self._cl_mem["dark"],
                                               numpy.int32(0),
                                               self._cl_mem["flat"],
                                               numpy.int32(0),
                                               self._cl_mem["solidangle"],
                                               numpy.int32(0),
                                               self._cl_mem["polarization"],
                                               numpy.int32(0),
                                               numpy.float32(0),
                                               numpy.float32(0)]
        self._cl_kernel_args["csr_integrate"] = [self._cl_mem["image"],
                                                 self._cl_mem["data"],
                                                 self._cl_mem["indices"],
                                                 self._cl_mem["indptr"],
                                                 numpy.int32(0),
                                                 numpy.float32(0),
                                                 self._cl_mem["outData"],
                                                 self._cl_mem["outCount"],
                                                 self._cl_mem["outMerge"]]
        self._cl_kernel_args["memset_out"] = [self._cl_mem[i] for i in ["outData", "outCount", "outMerge"]]  # noqa
        self._cl_kernel_args["u8_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]  # noqa
        self._cl_kernel_args["s8_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]  # noqa
        self._cl_kernel_args["u16_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]  # noqa
        self._cl_kernel_args["s16_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]  # noqa
        self._cl_kernel_args["u32_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]  # noqa
        self._cl_kernel_args["s32_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]  # noqa

    def integrate(self, data, dummy=None, delta_dummy=None,
                  dark=None, flat=None,
                  solidAngle=None, polarization=None,
                  dark_checksum=None, flat_checksum=None,
                  solidAngle_checksum=None, polarization_checksum=None):
        events = []
        with self._sem:
            if data.dtype == numpy.uint8:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))  # noqa
                cast_to_float = self._program.u8_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["u8_to_float"])  # noqa
                events += [("copy image", copy_image), ("cast", cast_to_float)]
            elif data.dtype == numpy.int8:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))  # noqa
                cast_to_float = self._program.s8_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["s8_to_float"])  # noqa
                events += [("copy image", copy_image), ("cast", cast_to_float)]
            elif data.dtype == numpy.uint16:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))  # noqa
                cast_to_float = self._program.u16_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["u16_to_float"])  # noqa
                events += [("copy image", copy_image), ("cast", cast_to_float)]
            elif data.dtype == numpy.int16:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))  # noqa
                cast_to_float = self._program.s16_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["s16_to_float"])  # noqa
                events += [("copy image", copy_image), ("cast", cast_to_float)]
            elif data.dtype == numpy.uint32:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))  # noqa
                cast_to_float = self._program.u32_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["u32_to_float"])  # noqa
                events += [("copy image", copy_image), ("cast", cast_to_float)]
            elif data.dtype == numpy.int32:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))  # noqa
                cast_to_float = self._program.s32_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["s32_to_float"])  # noqa
                events += [("copy image", copy_image), ("cast", cast_to_float)]
            else:
                copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image"], numpy.ascontiguousarray(data, dtype=numpy.float32))  # noqa
                events += [("copy image", copy_image)]
            memset = self._program.memset_out(self._queue, self.wdim_bins, self.workgroup_size, *self._cl_kernel_args["memset_out"])  # noqa
            events.append(("memset", memset))

            if dummy is not None:
                do_dummy = numpy.int32(1)
                dummy = numpy.float32(dummy)
                if delta_dummy is None:
                    delta_dummy = numpy.float32(0)
                else:
                    delta_dummy = numpy.float32(abs(delta_dummy))
            else:
                do_dummy = numpy.int32(0)
                dummy = numpy.float32(numpy.nan)
                delta_dummy = numpy.float32(0)
            self._cl_kernel_args["corrections"][9] = do_dummy
            self._cl_kernel_args["corrections"][10] = dummy
            self._cl_kernel_args["corrections"][11] = delta_dummy
            self._cl_kernel_args["csr_integrate"][4] = do_dummy
            self._cl_kernel_args["csr_integrate"][5] = dummy

            if dark is not None:
                do_dark = numpy.int32(1)
                if not dark_checksum:
                    dark_checksum = crc32(dark)
                if dark_checksum != self.on_device["dark"]:
                    ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["dark"], numpy.ascontiguousarray(dark, dtype=numpy.float32))  # noqa
                    events.append("copy dark", ev)
                    self.on_device["dark"] = dark_checksum
            else:
                do_dark = numpy.int32(0)
            self._cl_kernel_args["corrections"][1] = do_dark

            if flat is not None:
                do_flat = numpy.int32(1)
                if not flat_checksum:
                    flat_checksum = crc32(flat)
                if self.on_device["flat"] != flat_checksum:
                    ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["flat"], numpy.ascontiguousarray(flat, dtype=numpy.float32))  # noqa
                    events.append("copy flat", ev)
                    self.on_device["flat"] = flat_checksum
            else:
                do_flat = numpy.int32(0)
            self._cl_kernel_args["corrections"][3] = do_flat

            if solidAngle is not None:
                do_solidAngle = numpy.int32(1)
                if not solidAngle_checksum:
                    solidAngle_checksum = crc32(solidAngle)
                if solidAngle_checksum != self.on_device["solidangle"]:
                    ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["solidangle"], numpy.ascontiguousarray(solidAngle, dtype=numpy.float32))  # noqa
                events.append(("copy solidangle", ev))
                self.on_device["solidangle"] = solidAngle_checksum
            else:
                do_solidAngle = numpy.int32(0)
            self._cl_kernel_args["corrections"][5] = do_solidAngle

            if polarization is not None:
                do_polarization = numpy.int32(1)
                if not polarization_checksum:
                    polarization_checksum = crc32(polarization)
                if polarization_checksum != self.on_device["polarization"]:
                    ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["polarization"], numpy.ascontiguousarray(polarization, dtype=numpy.float32))  # noqa
                    events.append(("copy polarization", ev))
                    self.on_device["polarization"] = polarization_checksum
            else:
                do_polarization = numpy.int32(0)
            self._cl_kernel_args["corrections"][7] = do_polarization

            copy_image.wait()
            if do_dummy + do_polarization + do_solidAngle + do_flat + do_dark > 0:  # noqa
                ev = self._program.corrections(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["corrections"])  # noqa
                events.append(("corrections", ev))

            integrate = self._program.csr_integrate_dis(self._queue, self.wdim_bins, self.workgroup_size, *self._cl_kernel_args["csr_integrate"])  # noqa
            events.append(("integrate", integrate))

            outMerge = numpy.empty(self.bins, dtype=numpy.float32)
            outData = numpy.empty(self.bins, dtype=numpy.float32)
            outCount = numpy.empty(self.bins, dtype=numpy.float32)
            ev = pyopencl.enqueue_copy(self._queue, outMerge,
                                       self._cl_mem["outMerge"])
            events.append(("copy D->H outMerge", ev))
            ev = pyopencl.enqueue_copy(self._queue, outData,
                                       self._cl_mem["outData"])
            events.append(("copy D->H outData", ev))
            ev = pyopencl.enqueue_copy(self._queue, outCount,
                                       self._cl_mem["outCount"])
            events.append(("copy D->H outCount", ev))
            ev.wait()

        if self.profile:
            self.events += events

        return outMerge, outData, outCount

    def log_profile(self):
        """If we are in profiling mode, prints out all timing for every
        single OpenCL call
        """
        t = 0.0
        if self.profile:
            for e in self.events:
                if "__len__" in dir(e) and len(e) >= 2:
                    et = 1e-6 * (e[1].profile.end - e[1].profile.start)
                    print("%50s:\t%.3fms" % (e[0], et))
                    t += et

        print("_" * 80)
        print("%50s:\t%.3fms" % ("Total execution time", t))
