#   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.

import collections
import os
import shutil

import numpy as np
import numpy.testing as npt
import pytensor
import pytest

from pymc.backends import base
from tests import models


class ModelBackendSetupTestCase:
    """Set up a backend trace.

    Provides the attributes
    - test_point
    - model
    - strace
    - draws

    Children must define
    - backend
    - name
    - shape

    Children may define
    - sampler_vars
    """

    def setup_method(self):
        self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
        with self.model:
            self.strace = self.backend(self.name)
        self.draws, self.chain = 3, 0
        if not hasattr(self, "sampler_vars"):
            self.sampler_vars = None
        if self.sampler_vars is not None:
            self.strace.setup(self.draws, self.chain, self.sampler_vars)
        else:
            self.strace.setup(self.draws, self.chain)

    def test_append_invalid(self):
        if self.sampler_vars is not None:
            with pytest.raises(ValueError):
                self.strace.setup(self.draws, self.chain)
            with pytest.raises(ValueError):
                vars = self.sampler_vars + [{"a": bool}]
                self.strace.setup(self.draws, self.chain, vars)
        else:
            with pytest.raises((ValueError, TypeError)):
                self.strace.setup(self.draws, self.chain, [{"a": bool}])

    def test_append(self):
        if self.sampler_vars is None:
            self.strace.setup(self.draws, self.chain)
            assert len(self.strace) == 0
        else:
            self.strace.setup(self.draws, self.chain, self.sampler_vars)
            assert len(self.strace) == 0

    def test_double_close(self):
        self.strace.close()
        self.strace.close()

    def teardown_method(self):
        if self.name is not None:
            remove_file_or_directory(self.name)


class StatsTestCase:
    """Test for init and setup of backups.

    Provides the attributes
    - test_point
    - model
    - draws

    Children must define
    - backend
    - name
    - shape
    """

    def setup_method(self):
        self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
        self.draws, self.chain = 3, 0

    def test_bad_dtype(self):
        bad_vars = [{"a": np.float64}, {"a": bool}]
        good_vars = [{"a": np.float64}, {"a": np.float64}]
        with self.model:
            strace = self.backend(self.name)
        with pytest.raises((ValueError, TypeError)):
            strace.setup(self.draws, self.chain, bad_vars)
        strace.setup(self.draws, self.chain, good_vars)
        assert strace.stat_names == {"a"}

    def teardown_method(self):
        if self.name is not None:
            remove_file_or_directory(self.name)


class ModelBackendSampledTestCase:
    """Setup and sample a backend trace.

    Provides the attributes
    - test_point
    - model
    - mtrace (MultiTrace object)
    - draws
    - expected
        Expected values mapped to chain number and variable name.
    - stat_dtypes

    Children must define
    - backend
    - name
    - shape

    Children may define
    - sampler_vars
    - write_partial_chain
    """

    @classmethod
    def setup_class(cls):
        cls.test_point, cls.model, _ = models.beta_bernoulli(cls.shape)

        if hasattr(cls, "write_partial_chain") and cls.write_partial_chain is True:
            cls.chain_vars = [cls.model.rvs_to_values[v] for v in cls.model.unobserved_RVs[1:]]
        else:
            cls.chain_vars = [cls.model.rvs_to_values[v] for v in cls.model.unobserved_RVs]

        with cls.model:
            strace0 = cls.backend(cls.name, vars=cls.chain_vars)
            strace1 = cls.backend(cls.name, vars=cls.chain_vars)

        if not hasattr(cls, "sampler_vars"):
            cls.sampler_vars = None

        cls.draws = 5
        if cls.sampler_vars is not None:
            strace0.setup(cls.draws, chain=0, sampler_vars=cls.sampler_vars)
            strace1.setup(cls.draws, chain=1, sampler_vars=cls.sampler_vars)
        else:
            strace0.setup(cls.draws, chain=0)
            strace1.setup(cls.draws, chain=1)

        varnames = list(cls.test_point.keys())
        shapes = {varname: value.shape for varname, value in cls.test_point.items()}
        dtypes = {varname: value.dtype for varname, value in cls.test_point.items()}

        cls.expected = {0: {}, 1: {}}
        for varname in varnames:
            mcmc_shape = (cls.draws,) + shapes[varname]
            values = np.arange(cls.draws * np.prod(shapes[varname]), dtype=dtypes[varname])
            cls.expected[0][varname] = values.reshape(mcmc_shape)
            cls.expected[1][varname] = values.reshape(mcmc_shape) * 100

        if cls.sampler_vars is not None:
            cls.expected_stats = {0: [], 1: []}
            for vars in cls.sampler_vars:
                stats = {}
                cls.expected_stats[0].append(stats)
                cls.expected_stats[1].append(stats)
                for key, dtype in vars.items():
                    if dtype == bool:
                        stats[key] = np.zeros(cls.draws, dtype=dtype)
                    else:
                        stats[key] = np.arange(cls.draws, dtype=dtype)

        for idx in range(cls.draws):
            point0 = {varname: cls.expected[0][varname][idx, ...] for varname in varnames}
            point1 = {varname: cls.expected[1][varname][idx, ...] for varname in varnames}
            if cls.sampler_vars is not None:
                stats1 = [
                    {key: val[idx] for key, val in stats.items()} for stats in cls.expected_stats[0]
                ]
                stats2 = [
                    {key: val[idx] for key, val in stats.items()} for stats in cls.expected_stats[1]
                ]
                strace0.record(point=point0, sampler_stats=stats1)
                strace1.record(point=point1, sampler_stats=stats2)
            else:
                strace0.record(point=point0)
                strace1.record(point=point1)
        strace0.close()
        strace1.close()
        cls.mtrace = base.MultiTrace([strace0, strace1])

        cls.stat_dtypes = {}
        cls.stats_counts = collections.Counter()
        for stats in cls.sampler_vars or []:
            cls.stat_dtypes.update(stats)
            cls.stats_counts.update(stats.keys())

    @classmethod
    def teardown_class(cls):
        if cls.name is not None:
            remove_file_or_directory(cls.name)

    def test_varnames_nonempty(self):
        # Make sure the test_point has variables names because many
        # tests rely on looping through these and would pass silently
        # if the loop is never entered.
        assert list(self.test_point.keys())

    def test_stat_names(self):
        names = set()
        for vars in self.sampler_vars or []:
            names.update(vars.keys())
        assert self.mtrace.stat_names == names


class SamplingTestCase(ModelBackendSetupTestCase):
    """Test backend sampling.

    Children must define
    - backend
    - name
    - shape
    """

    def record_point(self, val):
        point = {varname: np.tile(val, value.shape) for varname, value in self.test_point.items()}
        if self.sampler_vars is not None:
            stats = [{key: dtype(val) for key, dtype in vars.items()} for vars in self.sampler_vars]
            self.strace.record(point=point, sampler_stats=stats)
        else:
            self.strace.record(point=point)

    def test_standard_close(self):
        for idx in range(self.draws):
            self.record_point(idx)
        self.strace.close()

        for varname in self.test_point.keys():
            npt.assert_equal(
                self.strace.get_values(varname)[0, ...], np.zeros(self.strace.var_shapes[varname])
            )
            last_idx = self.draws - 1
            npt.assert_equal(
                self.strace.get_values(varname)[last_idx, ...],
                np.tile(last_idx, self.strace.var_shapes[varname]),
            )
        if self.sampler_vars:
            for varname in self.strace.stat_names:
                vals = self.strace.get_sampler_stats(varname)
                assert vals.shape[0] == self.draws

    def test_missing_stats(self):
        if self.sampler_vars is not None:
            with pytest.raises(ValueError):
                self.strace.record(point=self.test_point)

    def test_clean_interrupt(self):
        self.record_point(0)
        self.strace.close()
        for varname in self.test_point.keys():
            assert self.strace.get_values(varname).shape[0] == 1
        for statname in self.strace.stat_names:
            assert self.strace.get_sampler_stats(statname).shape[0] == 1


class SelectionTestCase(ModelBackendSampledTestCase):
    """Test backend selection.

    Children must define
    - backend
    - name
    - shape
    """

    def test_get_values_default(self):
        for varname in self.test_point.keys():
            expected = np.concatenate([self.expected[chain][varname] for chain in [0, 1]])
            result = self.mtrace.get_values(varname)
            npt.assert_equal(result, expected)

    def test_get_values_nocombine_burn_keyword(self):
        burn = 2
        for varname in self.test_point.keys():
            expected = [self.expected[0][varname][burn:], self.expected[1][varname][burn:]]
            result = self.mtrace.get_values(varname, burn=burn, combine=False)
            npt.assert_equal(result, expected)

    def test_len(self):
        assert len(self.mtrace) == self.draws

    def test_dtypes(self):
        for varname in self.test_point.keys():
            assert (
                self.expected[0][varname].dtype == self.mtrace.get_values(varname, chains=0).dtype
            )

        for statname in self.mtrace.stat_names:
            assert (
                self.stat_dtypes[statname]
                == self.mtrace.get_sampler_stats(statname, chains=0).dtype
            )

    def test_get_values_nocombine_thin_keyword(self):
        thin = 2
        for varname in self.test_point.keys():
            expected = [self.expected[0][varname][::thin], self.expected[1][varname][::thin]]
            result = self.mtrace.get_values(varname, thin=thin, combine=False)
            npt.assert_equal(result, expected)

    def test_get_point(self):
        idx = 2
        result = self.mtrace.point(idx)
        for varname in self.test_point.keys():
            expected = self.expected[1][varname][idx]
            npt.assert_equal(result[varname], expected)

    def test_get_slice(self):
        expected = []
        for chain in [0, 1]:
            expected.append(
                {varname: self.expected[chain][varname][2:] for varname in self.mtrace.varnames}
            )
        result = self.mtrace[2:]
        for chain in [0, 1]:
            for varname in self.test_point.keys():
                npt.assert_equal(
                    result.get_values(varname, chains=[chain]), expected[chain][varname]
                )

    def test_get_slice_step(self):
        result = self.mtrace[:]
        assert len(result) == self.draws

        result = self.mtrace[::2]
        assert len(result) == self.draws // 2

    def test_get_slice_neg_step(self):
        if hasattr(self, "skip_test_get_slice_neg_step"):
            return

        result = self.mtrace[::-1]
        assert len(result) == self.draws

        result = self.mtrace[::-2]
        assert len(result) == self.draws // 2

    def test_get_neg_slice(self):
        expected = []
        for chain in [0, 1]:
            expected.append(
                {varname: self.expected[chain][varname][-2:] for varname in self.mtrace.varnames}
            )
        result = self.mtrace[-2:]
        for chain in [0, 1]:
            for varname in self.test_point.keys():
                npt.assert_equal(
                    result.get_values(varname, chains=[chain]), expected[chain][varname]
                )

    def test_get_values_one_chain(self):
        for varname in self.test_point.keys():
            expected = self.expected[0][varname]
            result = self.mtrace.get_values(varname, chains=[0])
            npt.assert_equal(result, expected)

    def test_get_values_nocombine_chains_reversed(self):
        for varname in self.test_point.keys():
            expected = [self.expected[1][varname], self.expected[0][varname]]
            result = self.mtrace.get_values(varname, chains=[1, 0], combine=False)
            npt.assert_equal(result, expected)

    def test_nchains(self):
        self.mtrace.nchains == 2

    def test_get_values_one_chain_int_arg(self):
        for varname in self.test_point.keys():
            npt.assert_equal(
                self.mtrace.get_values(varname, chains=[0]),
                self.mtrace.get_values(varname, chains=0),
            )

    def test_get_values_combine(self):
        for varname in self.test_point.keys():
            expected = np.concatenate([self.expected[chain][varname] for chain in [0, 1]])
            result = self.mtrace.get_values(varname, combine=True)
            npt.assert_equal(result, expected)

    def test_get_values_combine_burn_arg(self):
        burn = 2
        for varname in self.test_point.keys():
            expected = np.concatenate([self.expected[chain][varname][burn:] for chain in [0, 1]])
            result = self.mtrace.get_values(varname, combine=True, burn=burn)
            npt.assert_equal(result, expected)

    def test_get_values_combine_thin_arg(self):
        thin = 2
        for varname in self.test_point.keys():
            expected = np.concatenate([self.expected[chain][varname][::thin] for chain in [0, 1]])
            result = self.mtrace.get_values(varname, combine=True, thin=thin)
            npt.assert_equal(result, expected)

    def test_getitem_equivalence(self):
        mtrace = self.mtrace
        for varname in self.test_point.keys():
            npt.assert_equal(mtrace[varname], mtrace.get_values(varname, combine=True))
            npt.assert_equal(mtrace[varname, 2:], mtrace.get_values(varname, burn=2, combine=True))
            npt.assert_equal(
                mtrace[varname, 2::2], mtrace.get_values(varname, burn=2, thin=2, combine=True)
            )

    def test_selection_method_equivalence(self):
        varname = self.mtrace.varnames[0]
        mtrace = self.mtrace
        npt.assert_equal(mtrace.get_values(varname), mtrace[varname])
        npt.assert_equal(mtrace[varname], mtrace.__getattr__(varname))


class DumpLoadTestCase(ModelBackendSampledTestCase):
    """Test equality of a dumped and loaded trace with original.

    Children must define
    - backend
    - load_func
        Function to load dumped backend
    - name
    - shape
    """

    @classmethod
    def setup_class(cls):
        super().setup_class()
        try:
            with cls.model:
                cls.dumped = cls.load_func(cls.name)
        except:
            remove_file_or_directory(cls.name)
            raise

    @classmethod
    def teardown_class(cls):
        remove_file_or_directory(cls.name)

    def test_nchains(self):
        assert self.mtrace.nchains == self.dumped.nchains

    def test_varnames(self):
        trace_names = list(sorted(self.mtrace.varnames))
        dumped_names = list(sorted(self.dumped.varnames))
        assert trace_names == dumped_names

    def test_values(self):
        trace = self.mtrace
        dumped = self.dumped
        for chain in trace.chains:
            for varname in self.chain_vars:
                data = trace.get_values(varname, chains=[chain])
                dumped_data = dumped.get_values(varname, chains=[chain])
                npt.assert_equal(data, dumped_data)


class BackendEqualityTestCase(ModelBackendSampledTestCase):
    """Test equality of attributes from two backends.

    Children must define
    - backend0
    - backend1
    - name0
    - name1
    - shape
    """

    @classmethod
    def setup_class(cls):
        cls.backend = cls.backend0
        cls.name = cls.name0
        super().setup_class()
        cls.mtrace0 = cls.mtrace

        cls.backend = cls.backend1
        cls.name = cls.name1
        super().setup_class()
        cls.mtrace1 = cls.mtrace

    @classmethod
    def teardown_class(cls):
        for name in [cls.name0, cls.name1]:
            if name is not None:
                remove_file_or_directory(name)

    def test_chain_length(self):
        assert self.mtrace0.nchains == self.mtrace1.nchains
        assert len(self.mtrace0) == len(self.mtrace1)

    @pytest.mark.xfail(condition=(pytensor.config.floatX == "float32"), reason="Fails on float32")
    def test_dtype(self):
        for varname in self.test_point.keys():
            assert (
                self.mtrace0.get_values(varname, chains=0).dtype
                == self.mtrace1.get_values(varname, chains=0).dtype
            )

    def test_number_of_draws(self):
        for varname in self.test_point.keys():
            values0 = self.mtrace0.get_values(varname, combine=False, squeeze=False)
            values1 = self.mtrace1.get_values(varname, combine=False, squeeze=False)
            assert values0[0].shape[0] == self.draws
            assert values1[0].shape[0] == self.draws

    def test_get_item(self):
        for varname in self.test_point.keys():
            npt.assert_equal(self.mtrace0[varname], self.mtrace1[varname])

    def test_get_values(self):
        for varname in self.test_point.keys():
            for cf in [False, True]:
                npt.assert_equal(
                    self.mtrace0.get_values(varname, combine=cf),
                    self.mtrace1.get_values(varname, combine=cf),
                )

    def test_get_values_no_squeeze(self):
        for varname in self.test_point.keys():
            npt.assert_equal(
                self.mtrace0.get_values(varname, combine=False, squeeze=False),
                self.mtrace1.get_values(varname, combine=False, squeeze=False),
            )

    def test_get_values_combine_and_no_squeeze(self):
        for varname in self.test_point.keys():
            npt.assert_equal(
                self.mtrace0.get_values(varname, combine=True, squeeze=False),
                self.mtrace1.get_values(varname, combine=True, squeeze=False),
            )

    def test_get_values_with_burn(self):
        for varname in self.test_point.keys():
            for cf in [False, True]:
                npt.assert_equal(
                    self.mtrace0.get_values(varname, combine=cf, burn=3),
                    self.mtrace1.get_values(varname, combine=cf, burn=3),
                )
                # Burn to one value.
                npt.assert_equal(
                    self.mtrace0.get_values(varname, combine=cf, burn=self.draws - 1),
                    self.mtrace1.get_values(varname, combine=cf, burn=self.draws - 1),
                )

    def test_get_values_with_thin(self):
        for varname in self.test_point.keys():
            for cf in [False, True]:
                npt.assert_equal(
                    self.mtrace0.get_values(varname, combine=cf, thin=2),
                    self.mtrace1.get_values(varname, combine=cf, thin=2),
                )

    def test_get_values_with_burn_and_thin(self):
        for varname in self.test_point.keys():
            for cf in [False, True]:
                npt.assert_equal(
                    self.mtrace0.get_values(varname, combine=cf, burn=2, thin=2),
                    self.mtrace1.get_values(varname, combine=cf, burn=2, thin=2),
                )

    def test_get_values_with_chains_arg(self):
        for varname in self.test_point.keys():
            for cf in [False, True]:
                npt.assert_equal(
                    self.mtrace0.get_values(varname, chains=[0], combine=cf),
                    self.mtrace1.get_values(varname, chains=[0], combine=cf),
                )

    def test_get_point(self):
        npoint, spoint = self.mtrace0[4], self.mtrace1[4]
        for varname in self.test_point.keys():
            npt.assert_equal(npoint[varname], spoint[varname])

    def test_point_with_chain_arg(self):
        npoint = self.mtrace0.point(4, chain=0)
        spoint = self.mtrace1.point(4, chain=0)
        for varname in self.test_point.keys():
            npt.assert_equal(npoint[varname], spoint[varname])


def remove_file_or_directory(name):
    try:
        os.remove(name)
    except OSError:
        shutil.rmtree(name, ignore_errors=True)
