#   Copyright 2023 The PyMC Developers
#
#   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
#
#       http://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.

"""NumPy array trace backend

Store sampling values in memory as a NumPy array.
"""


from typing import Any, Dict, List, Optional

import numpy as np

from pymc.backends import base
from pymc.backends.base import MultiTrace
from pymc.model import Model, modelcontext


class NDArray(base.BaseTrace):
    """NDArray trace object

    Parameters
    ----------
    name: str
        Name of backend. This has no meaning for the NDArray backend.
    model: Model
        If None, the model is taken from the `with` context.
    vars: list of variables
        Sampling values will be stored for these variables. If None,
        `model.unobserved_RVs` is used.
    """

    def __init__(self, name=None, model=None, vars=None, test_point=None):
        super().__init__(name, model, vars, test_point)
        self.draw_idx = 0
        self.draws = None
        self.samples = {}
        self._stats = None

    # Sampling methods

    def setup(self, draws, chain, sampler_vars=None) -> None:
        """Perform chain-specific setup.

        Parameters
        ----------
        draws: int
            Expected number of draws
        chain: int
            Chain number
        sampler_vars: list of dicts
            Names and dtypes of the variables that are
            exported by the samplers.
        """
        super().setup(draws, chain, sampler_vars)

        self.chain = chain
        if self.samples:  # Concatenate new array if chain is already present.
            old_draws = len(self)
            self.draws = old_draws + draws
            self.draw_idx = old_draws
            for varname, shape in self.var_shapes.items():
                old_var_samples = self.samples[varname]
                new_var_samples = np.zeros((draws,) + shape, self.var_dtypes[varname])
                self.samples[varname] = np.concatenate((old_var_samples, new_var_samples), axis=0)
        else:  # Otherwise, make array of zeros for each variable.
            self.draws = draws
            for varname, shape in self.var_shapes.items():
                self.samples[varname] = np.zeros((draws,) + shape, dtype=self.var_dtypes[varname])

        if sampler_vars is None:
            return

        if self._stats is None:
            self._stats = []
            for sampler in sampler_vars:
                data: Dict[str, np.ndarray] = dict()
                self._stats.append(data)
                for varname, dtype in sampler.items():
                    data[varname] = np.zeros(draws, dtype=dtype)
        else:
            for data, vars in zip(self._stats, sampler_vars):
                if vars.keys() != data.keys():
                    raise ValueError("Sampler vars can't change")
                for varname, dtype in vars.items():
                    old = data[varname]
                    new = np.zeros(draws, dtype=dtype)
                    data[varname] = np.concatenate([old, new])

    def record(self, point, sampler_stats=None) -> None:
        """Record results of a sampling iteration.

        Parameters
        ----------
        point: dict
            Values mapped to variable names
        """
        for varname, value in zip(self.varnames, self.fn(point)):
            self.samples[varname][self.draw_idx] = value

        if self._stats is not None and sampler_stats is None:
            raise ValueError("Expected sampler_stats")
        if self._stats is None and sampler_stats is not None:
            raise ValueError("Unknown sampler_stats")
        if sampler_stats is not None:
            for data, vars in zip(self._stats, sampler_stats):
                for key, val in vars.items():
                    data[key][self.draw_idx] = val
        self.draw_idx += 1

    def _get_sampler_stats(
        self, varname: str, sampler_idx: int, burn: int, thin: int
    ) -> np.ndarray:
        return self._stats[sampler_idx][varname][burn::thin]

    def close(self):
        if self.draw_idx == self.draws:
            return
        # Remove trailing zeros if interrupted before completed all
        # draws.
        self.samples = {var: vtrace[: self.draw_idx] for var, vtrace in self.samples.items()}
        if self._stats is not None:
            self._stats = [
                {var: trace[: self.draw_idx] for var, trace in stats.items()}
                for stats in self._stats
            ]

    # Selection methods

    def __len__(self):
        if not self.samples:  # `setup` has not been called.
            return 0
        return self.draw_idx

    def get_values(self, varname: str, burn=0, thin=1) -> np.ndarray:
        """Get values from trace.

        Parameters
        ----------
        varname: str
        burn: int
        thin: int

        Returns
        -------
        A NumPy array
        """
        return self.samples[varname][burn::thin]

    def _slice(self, idx: slice):
        # Slicing directly instead of using _slice_as_ndarray to
        # support stop value in slice (which is needed by
        # iter_sample).

        # Only the first `draw_idx` value are valid because of preallocation
        idx = slice(*idx.indices(len(self)))

        sliced = NDArray(model=self.model, vars=self.vars)
        sliced.chain = self.chain
        sliced.samples = {varname: values[idx] for varname, values in self.samples.items()}
        sliced.sampler_vars = self.sampler_vars
        sliced.draw_idx = (idx.stop - idx.start) // idx.step

        if self._stats is None:
            return sliced
        sliced._stats = []
        for vars in self._stats:
            var_sliced: Dict[str, np.ndarray] = {}
            sliced._stats.append(var_sliced)
            for key, vals in vars.items():
                var_sliced[key] = vals[idx]

        return sliced

    def point(self, idx) -> Dict[str, Any]:
        """Return dictionary of point values at `idx` for current chain
        with variable names as keys.
        """
        idx = int(idx)
        return {varname: values[idx] for varname, values in self.samples.items()}


def _slice_as_ndarray(strace, idx):
    sliced = NDArray(model=strace.model, vars=strace.vars)
    sliced.chain = strace.chain

    # Happy path where we do not need to load everything from the trace
    if (idx.step is None or idx.step >= 1) and (idx.stop is None or idx.stop == len(strace)):
        start, stop, step = idx.indices(len(strace))
        sliced.samples = {
            v: strace.get_values(v, burn=idx.start, thin=idx.step) for v in strace.varnames
        }
        sliced.draw_idx = (stop - start) // step
    else:
        start, stop, step = idx.indices(len(strace))
        sliced.samples = {v: strace.get_values(v)[start:stop:step] for v in strace.varnames}
        sliced.draw_idx = (stop - start) // step

    return sliced


def point_list_to_multitrace(
    point_list: List[Dict[str, np.ndarray]], model: Optional[Model] = None
) -> MultiTrace:
    """transform point list into MultiTrace"""
    _model = modelcontext(model)
    varnames = list(point_list[0].keys())
    with _model:
        chain = NDArray(model=_model, vars=[_model[vn] for vn in varnames])
        chain.setup(draws=len(point_list), chain=0)

        # since we are simply loading a trace by hand, we need only a vacuous function for
        # chain.record() to use. This crushes the default.
        def point_fun(point):
            return [point[vn] for vn in varnames]

        chain.fn = point_fun
        for point in point_list:
            chain.record(point)
    return MultiTrace([chain])
