# Copyright 2020 The JAX Authors.
#
# 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
#
#     https://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.

from typing import List, Tuple

import jaxlib.mlir.ir as ir
import jaxlib.mlir.dialects.stablehlo as hlo


from .hlo_helpers import custom_call
from .cpu import _ducc_fft
import numpy as np

from jaxlib import xla_client

for _name, _value in _ducc_fft.registrations().items():
  xla_client.register_custom_call_target(_name, _value, platform="cpu")

FftType = xla_client.FftType


_C2C = 0
_C2R = 1
_R2C = 2


def _ducc_fft_descriptor(
    shape: List[int], dtype, fft_type: FftType, fft_lengths: List[int]
) -> Tuple[bytes, np.dtype, List[int]]:
  n = len(shape)
  assert len(fft_lengths) >= 1
  assert len(fft_lengths) <= n, (fft_lengths, n)

  forward = fft_type in (FftType.FFT, FftType.RFFT)
  is_double = np.finfo(dtype).dtype == np.float64
  if fft_type == FftType.RFFT:
    ducc_fft_type = _R2C

    assert dtype in (np.float32, np.float64), dtype
    out_dtype = np.dtype(np.complex64 if dtype == np.float32 else np.complex128)

    assert shape[-len(fft_lengths):] == fft_lengths, (shape, fft_lengths)
    out_shape = list(shape)
    out_shape[-1] = out_shape[-1] // 2 + 1

  elif fft_type == FftType.IRFFT:
    ducc_fft_type = _C2R
    assert np.issubdtype(dtype, np.complexfloating), dtype

    out_dtype = np.dtype(np.float32 if dtype == np.complex64 else np.float64)

    assert shape[-len(fft_lengths):-1] == fft_lengths[:-1]
    out_shape = list(shape)
    out_shape[-1] = fft_lengths[-1]
    assert (out_shape[-1] // 2 + 1) == shape[-1]
  else:
    ducc_fft_type = _C2C

    assert np.issubdtype(dtype, np.complexfloating), dtype
    out_dtype = dtype

    assert shape[-len(fft_lengths):] == fft_lengths, (shape, fft_lengths)
    out_shape = shape

  # PocketFft does not allow size 0 dimensions.
  if 0 in shape or 0 in out_shape:
    return b"", out_dtype, out_shape

  # Builds a PocketFftDescriptor flatbuffer. This descriptor is passed to the
  # C++ kernel to describe the FFT to perform.
  strides_in = []
  stride = 1
  for d in reversed(shape):
    strides_in.append(stride)
    stride *= d

  strides_out = []
  stride = 1
  for d in reversed(out_shape):
    strides_out.append(stride)
    stride *= d

  axes = [n - len(fft_lengths) + d for d in range(len(fft_lengths))]

  scale = 1. if forward else (1. / np.prod(fft_lengths))
  descriptor = _ducc_fft.ducc_fft_descriptor(
    shape=shape if fft_type != FftType.IRFFT else out_shape,
    is_double=is_double,
    fft_type=ducc_fft_type,
      fft_lengths=fft_lengths,
    strides_in=list(reversed(strides_in)),
    strides_out=list(reversed(strides_out)),
    axes=axes,
    forward=forward,
    scale=scale)

  return descriptor, out_dtype, out_shape


def ducc_fft_hlo(a, dtype, *, fft_type: FftType, fft_lengths: List[int]):
  """DUCC FFT kernel for CPU."""
  a_type = ir.RankedTensorType(a.type)
  n = len(a_type.shape)

  fft_lengths = list(fft_lengths)
  descriptor_bytes, out_dtype, out_shape = _ducc_fft_descriptor(
      list(a_type.shape), dtype, fft_type, fft_lengths)

  if out_dtype == np.float32:
    out_type = ir.F32Type.get()
  elif out_dtype == np.float64:
    out_type = ir.F64Type.get()
  elif out_dtype == np.complex64:
    out_type = ir.ComplexType.get(ir.F32Type.get())
  elif out_dtype == np.complex128:
    out_type = ir.ComplexType.get(ir.F64Type.get())
  else:
    raise ValueError(f"Unknown output type {out_dtype}")

  if 0 in a_type.shape or 0 in out_shape:
    zero = hlo.ConstantOp(
        ir.DenseElementsAttr.get(
            np.array(0, dtype=out_dtype), type=out_type))
    return hlo.BroadcastOp(
        zero,
        ir.DenseElementsAttr.get(np.asarray(out_shape, np.int64))).result

  u8_type = ir.IntegerType.get_unsigned(8)
  descriptor = hlo.ConstantOp(
      ir.DenseElementsAttr.get(
          np.frombuffer(descriptor_bytes, dtype=np.uint8), type=u8_type))
  layout = tuple(range(n - 1, -1, -1))
  return custom_call(
      "ducc_fft",
      [ir.RankedTensorType.get(out_shape, out_type)],
      [descriptor, a],
      operand_layouts=[[0], layout],
      result_layouts=[layout])