# Copyright 2019 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 collections.abc import Sequence
from functools import partial
import math
from typing import Union

import numpy as np

from jax import lax

from jax._src import dispatch
from jax._src.api import jit, linear_transpose, ShapeDtypeStruct
from jax._src.core import Primitive, is_constant_shape
from jax._src.interpreters import ad
from jax._src.interpreters import batching
from jax._src.interpreters import mlir
from jax._src.lib.mlir.dialects import hlo
from jax._src.lib import xla_client
from jax._src.lib import ducc_fft
from jax._src.lib import version as jaxlib_version
from jax._src.numpy.util import promote_dtypes_complex, promote_dtypes_inexact

__all__ = [
  "fft",
  "fft_p",
]

def _str_to_fft_type(s: str) -> xla_client.FftType:
  if s in ("fft", "FFT"):
    return xla_client.FftType.FFT
  elif s in ("ifft", "IFFT"):
    return xla_client.FftType.IFFT
  elif s in ("rfft", "RFFT"):
    return xla_client.FftType.RFFT
  elif s in ("irfft", "IRFFT"):
    return xla_client.FftType.IRFFT
  else:
    raise ValueError(f"Unknown FFT type '{s}'")

@partial(jit, static_argnums=(1, 2))
def fft(x, fft_type: Union[xla_client.FftType, str], fft_lengths: Sequence[int]):
  if isinstance(fft_type, str):
    typ = _str_to_fft_type(fft_type)
  elif isinstance(fft_type, xla_client.FftType):
    typ = fft_type
  else:
    raise TypeError(f"Unknown FFT type value '{fft_type}'")

  if typ == xla_client.FftType.RFFT:
    if np.iscomplexobj(x):
      raise ValueError("only real valued inputs supported for rfft")
    x, = promote_dtypes_inexact(x)
  else:
    x, = promote_dtypes_complex(x)
  if len(fft_lengths) == 0:
    # XLA FFT doesn't support 0-rank.
    return x
  fft_lengths = tuple(fft_lengths)
  return fft_p.bind(x, fft_type=typ, fft_lengths=fft_lengths)

def _fft_impl(x, fft_type, fft_lengths):
  return dispatch.apply_primitive(fft_p, x, fft_type=fft_type, fft_lengths=fft_lengths)

_complex_dtype = lambda dtype: (np.zeros((), dtype) + np.zeros((), np.complex64)).dtype
_real_dtype = lambda dtype: np.finfo(dtype).dtype

def fft_abstract_eval(x, fft_type, fft_lengths):
  if len(fft_lengths) > x.ndim:
    raise ValueError(f"FFT input shape {x.shape} must have at least as many "
                    f"input dimensions as fft_lengths {fft_lengths}.")
  if fft_type == xla_client.FftType.RFFT:
    if x.shape[-len(fft_lengths):] != fft_lengths:
      raise ValueError(f"RFFT input shape {x.shape} minor dimensions must "
                      f"be equal to fft_lengths {fft_lengths}")
    shape = (x.shape[:-len(fft_lengths)] + fft_lengths[:-1]
             + (fft_lengths[-1] // 2 + 1,))
    dtype = _complex_dtype(x.dtype)
  elif fft_type == xla_client.FftType.IRFFT:
    if x.shape[-len(fft_lengths):-1] != fft_lengths[:-1]:
      raise ValueError(f"IRFFT input shape {x.shape} minor dimensions must "
                      "be equal to all except the last fft_length, got "
                      f"{fft_lengths=}")
    shape = x.shape[:-len(fft_lengths)] + fft_lengths
    dtype = _real_dtype(x.dtype)
  else:
    if x.shape[-len(fft_lengths):] != fft_lengths:
      raise ValueError(f"FFT input shape {x.shape} minor dimensions must "
                      f"be equal to fft_lengths {fft_lengths}")
    shape = x.shape
    dtype = x.dtype
  return x.update(shape=shape, dtype=dtype)

def _fft_lowering(ctx, x, *, fft_type, fft_lengths):
  if not is_constant_shape(fft_lengths):
    # TODO: https://github.com/openxla/stablehlo/issues/1366
    raise NotImplementedError("Shape polymorphism for FFT with non-constant fft_length is not implemented for TPU and GPU")
  return [
      hlo.FftOp(x, hlo.FftTypeAttr.get(fft_type.name),
                mlir.dense_int_elements(fft_lengths)).result
  ]


def _fft_lowering_cpu(ctx, x, *, fft_type, fft_lengths):
  x_aval, = ctx.avals_in
  if jaxlib_version < (0, 4, 13):
    if any(not is_constant_shape(a.shape) for a in (ctx.avals_in + ctx.avals_out)):
      raise NotImplementedError("Shape polymorphism for custom call is not implemented (fft); b/261671778; try updating your jaxlib.")
    return [ducc_fft.ducc_fft_hlo(x, x_aval.dtype, fft_type=fft_type,  # type: ignore
                                  fft_lengths=fft_lengths)]

  in_shape = x_aval.shape
  dtype = x_aval.dtype
  out_aval, = ctx.avals_out
  out_shape = out_aval.shape

  forward = fft_type in (xla_client.FftType.FFT, xla_client.FftType.RFFT)
  ndims = len(in_shape)
  assert len(fft_lengths) >= 1
  assert len(fft_lengths) <= ndims, (fft_lengths, ndims)
  assert len(in_shape) == len(out_shape) == ndims

  # PocketFft does not allow size 0 dimensions.
  if 0 in in_shape or 0 in out_shape:
    if fft_type == xla_client.FftType.RFFT:
      assert dtype in (np.float32, np.float64), dtype
      out_dtype = np.dtype(np.complex64 if dtype == np.float32 else np.complex128)

    elif fft_type == xla_client.FftType.IRFFT:
      assert np.issubdtype(dtype, np.complexfloating), dtype
      out_dtype = np.dtype(np.float32 if dtype == np.complex64 else np.float64)

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

    zero = mlir.ir_constant(np.array(0, dtype=out_dtype),
                            canonicalize_types=False)
    return [
        mlir.broadcast_in_dim(ctx, zero, out_aval, broadcast_dimensions=[])]

  strides_in = []
  stride = 1
  for d in reversed(in_shape):
    strides_in.append(stride)
    stride *= d
  strides_in = mlir.shape_tensor(
      mlir.eval_dynamic_shape(ctx, tuple(reversed(strides_in))))

  strides_out = []
  stride = 1
  for d in reversed(out_shape):
    strides_out.append(stride)
    stride *= d
  strides_out = mlir.shape_tensor(
      mlir.eval_dynamic_shape(ctx, tuple(reversed(strides_out))))

  # scale = 1. if forward else (1. / np.prod(fft_lengths)) as a f64[1] tensor
  double_type = mlir.ir.RankedTensorType.get((), mlir.ir.F64Type.get())
  size_fft_length_prod = np.prod(fft_lengths) if fft_lengths else 1
  size_fft_lengths, = mlir.eval_dynamic_shape_as_vals(ctx, (size_fft_length_prod,))
  size_fft_lengths = hlo.ConvertOp(double_type, size_fft_lengths)
  one = mlir.ir_constant(np.float64(1.), canonicalize_types=False)
  scale = one if forward else hlo.DivOp(one, size_fft_lengths)
  scale = hlo.ReshapeOp(
      mlir.ir.RankedTensorType.get((1,), mlir.ir.F64Type.get()),
      scale).result

  in_shape = mlir.shape_tensor(mlir.eval_dynamic_shape(ctx, in_shape))
  out_shape = mlir.shape_tensor(mlir.eval_dynamic_shape(ctx, out_shape))
  in_shape = in_shape if fft_type != xla_client.FftType.IRFFT else out_shape

  result_type = mlir.aval_to_ir_type(out_aval)
  return [ducc_fft.dynamic_ducc_fft_hlo(
      result_type, x,
      input_dtype=x_aval.dtype, ndims=ndims, input_shape=in_shape,
      strides_in=strides_in, strides_out=strides_out, scale=scale,
      fft_type=fft_type, fft_lengths=fft_lengths, result_shape=out_shape)]

def _naive_rfft(x, fft_lengths):
  y = fft(x, xla_client.FftType.FFT, fft_lengths)
  n = fft_lengths[-1]
  return y[..., : n//2 + 1]

@partial(jit, static_argnums=1)
def _rfft_transpose(t, fft_lengths):
  # The transpose of RFFT can't be expressed only in terms of irfft. Instead of
  # manually building up larger twiddle matrices (which would increase the
  # asymptotic complexity and is also rather complicated), we rely JAX to
  # transpose a naive RFFT implementation.
  dummy_shape = t.shape[:-len(fft_lengths)] + fft_lengths
  dummy_primal = ShapeDtypeStruct(dummy_shape, _real_dtype(t.dtype))
  transpose = linear_transpose(
      partial(_naive_rfft, fft_lengths=fft_lengths), dummy_primal)
  result, = transpose(t)
  assert result.dtype == _real_dtype(t.dtype), (result.dtype, t.dtype)
  return result

def _irfft_transpose(t, fft_lengths):
  # The transpose of IRFFT is the RFFT of the cotangent times a scaling
  # factor and a mask. The mask scales the cotangent for the Hermitian
  # symmetric components of the RFFT by a factor of two, since these components
  # are de-duplicated in the RFFT.
  x = fft(t, xla_client.FftType.RFFT, fft_lengths)
  n = x.shape[-1]
  is_odd = fft_lengths[-1] % 2
  full = partial(lax.full_like, t, dtype=x.dtype)
  mask = lax.concatenate(
      [full(1.0, shape=(1,)),
       full(2.0, shape=(n - 2 + is_odd,)),
       full(1.0, shape=(1 - is_odd,))],
      dimension=0)
  scale = 1 / math.prod(fft_lengths)
  out = scale * lax.expand_dims(mask, range(x.ndim - 1)) * x
  assert out.dtype == _complex_dtype(t.dtype), (out.dtype, t.dtype)
  # Use JAX's convention for complex gradients
  # https://github.com/google/jax/issues/6223#issuecomment-807740707
  return lax.conj(out)

def _fft_transpose_rule(t, operand, fft_type, fft_lengths):
  if fft_type == xla_client.FftType.RFFT:
    result = _rfft_transpose(t, fft_lengths)
  elif fft_type == xla_client.FftType.IRFFT:
    result = _irfft_transpose(t, fft_lengths)
  else:
    result = fft(t, fft_type, fft_lengths)
  return result,

def _fft_batching_rule(batched_args, batch_dims, fft_type, fft_lengths):
  x, = batched_args
  bd, = batch_dims
  x = batching.moveaxis(x, bd, 0)
  return fft(x, fft_type, fft_lengths), 0

fft_p = Primitive('fft')
fft_p.def_impl(_fft_impl)
fft_p.def_abstract_eval(fft_abstract_eval)
mlir.register_lowering(fft_p, _fft_lowering)
ad.deflinear2(fft_p, _fft_transpose_rule)
batching.primitive_batchers[fft_p] = _fft_batching_rule
mlir.register_lowering(fft_p, _fft_lowering_cpu, platform='cpu')