tests/models/patchtsmixer/test_modeling_patchtsmixer.py - main - githubmirror/transformers

tests/models/patchtsmixer/test_modeling_patchtsmixer.py
1 111 строк41 KB
Ilyas Moutawwakil
🚨 Modeling changes for export, compile, and hybrid-attention standardization (#46738)
30 июн 2026, 15:38
30 июн 2026, 15:38181beb3
# Copyright 2023 IBM and HuggingFace Inc. team. All rights reserved.
#
# 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.
"""Testing suite for the PyTorch PatchTSMixer model."""

import inspect
import itertools
import random
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download
from parameterized import parameterized

from transformers import is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from transformers.utils import check_torch_load_is_safe

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


TOLERANCE = 1e-4

if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING,
        MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING,
        PatchTSMixerConfig,
        PatchTSMixerForPrediction,
        PatchTSMixerForPretraining,
        PatchTSMixerForRegression,
        PatchTSMixerForTimeSeriesClassification,
        PatchTSMixerModel,
    )
    from transformers.models.patchtsmixer.modeling_patchtsmixer import (
        PatchTSMixerEncoder,
        PatchTSMixerForPredictionHead,
        PatchTSMixerForPredictionOutput,
        PatchTSMixerForRegressionOutput,
        PatchTSMixerForTimeSeriesClassificationOutput,
        PatchTSMixerLinearHead,
        PatchTSMixerPretrainHead,
    )


@require_torch
class PatchTSMixerModelTester:
    def __init__(
        self,
        parent,
        context_length: int = 32,
        patch_length: int = 8,
        num_input_channels: int = 3,
        patch_stride: int = 8,
        # d_model: int = 128,
        hidden_size: int = 8,
        # num_layers: int = 8,
        num_hidden_layers: int = 2,
        expansion_factor: int = 2,
        dropout: float = 0.5,
        mode: str = "common_channel",
        gated_attn: bool = True,
        norm_mlp="LayerNorm",
        swin_hier: int = 0,
        # masking related
        mask_type: str = "forecast",
        random_mask_ratio=0.5,
        mask_patches: list = [2, 3],
        forecast_mask_ratios: list = [1, 1],
        mask_value=0,
        masked_loss: bool = False,
        mask_mode: str = "mask_before_encoder",
        channel_consistent_masking: bool = True,
        scaling: str | bool | None = "std",
        # Head related
        head_dropout: float = 0.2,
        # forecast related
        prediction_length: int = 16,
        out_channels: int | None = None,
        # Classification/regression related
        # num_labels: int = 3,
        num_targets: int = 3,
        output_range: list | None = None,
        head_aggregation: str | None = None,
        # Trainer related
        batch_size=13,
        is_training=True,
        seed_number=42,
        num_parallel_samples=4,
    ):
        self.parent = parent
        self.num_input_channels = num_input_channels
        self.context_length = context_length
        self.patch_length = patch_length
        self.patch_stride = patch_stride
        # self.d_model = d_model
        self.hidden_size = hidden_size
        self.expansion_factor = expansion_factor
        # self.num_layers = num_layers
        self.num_hidden_layers = num_hidden_layers
        self.dropout = dropout
        self.mode = mode
        self.gated_attn = gated_attn
        self.norm_mlp = norm_mlp
        self.swin_hier = swin_hier
        self.scaling = scaling
        self.head_dropout = head_dropout
        # masking related
        self.mask_type = mask_type
        self.random_mask_ratio = random_mask_ratio
        self.mask_patches = mask_patches
        self.forecast_mask_ratios = forecast_mask_ratios
        self.mask_value = mask_value
        self.channel_consistent_masking = channel_consistent_masking
        self.mask_mode = mask_mode
        self.masked_loss = masked_loss
        # patching related
        self.patch_last = True
        # forecast related
        self.prediction_length = prediction_length
        self.out_channels = out_channels
        # classification/regression related
        # self.num_labels = num_labels
        self.num_targets = num_targets
        self.output_range = output_range
        self.head_aggregation = head_aggregation
        # Trainer related
        self.batch_size = batch_size
        self.is_training = is_training
        self.seed_number = seed_number
        self.num_parallel_samples = num_parallel_samples

    def get_config(self):
        config_ = PatchTSMixerConfig(
            num_input_channels=self.num_input_channels,
            context_length=self.context_length,
            patch_length=self.patch_length,
            patch_stride=self.patch_stride,
            # d_model = self.d_model,
            d_model=self.hidden_size,
            expansion_factor=self.expansion_factor,
            # num_layers = self.num_layers,
            num_layers=self.num_hidden_layers,
            dropout=self.dropout,
            mode=self.mode,
            gated_attn=self.gated_attn,
            norm_mlp=self.norm_mlp,
            swin_hier=self.swin_hier,
            scaling=self.scaling,
            head_dropout=self.head_dropout,
            mask_type=self.mask_type,
            random_mask_ratio=self.random_mask_ratio,
            mask_patches=self.mask_patches,
            forecast_mask_ratios=self.forecast_mask_ratios,
            mask_value=self.mask_value,
            channel_consistent_masking=self.channel_consistent_masking,
            mask_mode=self.mask_mode,
            masked_loss=self.masked_loss,
            prediction_length=self.prediction_length,
            out_channels=self.out_channels,
            # num_labels=self.num_labels,
            num_targets=self.num_targets,
            output_range=self.output_range,
            head_aggregation=self.head_aggregation,
        )
        self.num_patches = config_.num_patches
        return config_

    def prepare_patchtsmixer_inputs_dict(self, config):
        _past_length = config.context_length
        # bs, n_vars, num_patch, patch_length

        # [bs x context_length x n_vars]
        past_values = floats_tensor([self.batch_size, _past_length, self.num_input_channels])

        inputs_dict = {
            "past_values": past_values,
        }
        return inputs_dict

    def prepare_config_and_inputs(self):
        config = self.get_config()
        inputs_dict = self.prepare_patchtsmixer_inputs_dict(config)
        return config, inputs_dict

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class PatchTSMixerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            PatchTSMixerModel,
            PatchTSMixerForPrediction,
            PatchTSMixerForPretraining,
            PatchTSMixerForTimeSeriesClassification,
            PatchTSMixerForRegression,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = {"feature-extraction": PatchTSMixerModel} if is_torch_available() else {}
    is_encoder_decoder = False

    test_missing_keys = False
    test_inputs_embeds = False

    test_resize_embeddings = True
    test_resize_position_embeddings = False
    test_mismatched_shapes = True
    has_attentions = False

    def setUp(self):
        self.model_tester = PatchTSMixerModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=PatchTSMixerConfig,
            has_text_modality=False,
            prediction_length=self.model_tester.prediction_length,
            common_properties=["hidden_size", "expansion_factor", "num_hidden_layers"],
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if model_class == PatchTSMixerForPrediction:
            rng = random.Random(self.model_tester.seed_number)
            labels = floats_tensor(
                [
                    self.model_tester.batch_size,
                    self.model_tester.prediction_length,
                    self.model_tester.num_input_channels,
                ],
                rng=rng,
            )
            inputs_dict["future_values"] = labels
        elif model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING):
            rng = random.Random(self.model_tester.seed_number)
            labels = ids_tensor([self.model_tester.batch_size], self.model_tester.num_targets, rng=rng)
            inputs_dict["target_values"] = labels
        elif model_class in get_values(MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING):
            rng = random.Random(self.model_tester.seed_number)
            labels = floats_tensor([self.model_tester.batch_size, self.model_tester.num_targets], rng=rng)
            inputs_dict["target_values"] = labels

        inputs_dict["output_hidden_states"] = True
        return inputs_dict

    def test_save_load_strict(self):
        config, _ = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
            self.assertEqual(info["missing_keys"], set())

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_layers = getattr(
                self.model_tester,
                "expected_num_hidden_layers",
                self.model_tester.num_hidden_layers,
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            expected_hidden_size = self.model_tester.hidden_size
            self.assertEqual(hidden_states[0].shape[-1], expected_hidden_size)

            num_patch = self.model_tester.num_patches
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [num_patch, self.model_tester.hidden_size],
            )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            check_hidden_states_output(inputs_dict, config, model_class)

    @unittest.skip(reason="No tokens embeddings")
    def test_resize_tokens_embeddings(self):
        pass

    def test_model_outputs_equivalence(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def set_nan_tensor_to_zero(t):
            t[t != t] = 0
            return t

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            with torch.no_grad():
                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
                output_ = model(**dict_inputs, return_dict=True, **additional_kwargs)
                # ``ModelOutput.to_tuple()`` drops ``None`` entries; do the same on the dict side.
                dict_output = output_.to_tuple()

                def recursive_check(tuple_object, dict_object):
                    if isinstance(tuple_object, (list, tuple)):
                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif isinstance(tuple_object, dict):
                        for tuple_iterable_value, dict_iterable_value in zip(
                            tuple_object.values(), dict_object.values()
                        ):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif tuple_object is None:
                        return
                    else:
                        self.assertTrue(
                            torch.allclose(
                                set_nan_tensor_to_zero(tuple_object),
                                set_nan_tensor_to_zero(dict_object),
                                atol=1e-5,
                            ),
                            msg=(
                                "Tuple and dict output are not equal. Difference:"
                                f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                            ),
                        )

                recursive_check(tuple_output, dict_output)

        for model_class in self.all_model_classes:
            print(model_class)
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)

            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            tuple_inputs.update({"output_hidden_states": False})
            dict_inputs.update({"output_hidden_states": False})
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            tuple_inputs.update({"output_hidden_states": False})
            dict_inputs.update({"output_hidden_states": False})
            check_equivalence(
                model,
                tuple_inputs,
                dict_inputs,
            )

    def test_model_main_input_name(self):
        model_signature = inspect.signature(getattr(PatchTSMixerModel, "forward"))
        # The main input is the name of the argument after `self`
        observed_main_input_name = list(model_signature.parameters.keys())[1]
        self.assertEqual(PatchTSMixerModel.main_input_name, observed_main_input_name)

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            if model_class == PatchTSMixerForPretraining:
                expected_arg_names = [
                    "past_values",
                    "observed_mask",
                    "output_hidden_states",
                    "return_loss",
                ]
            elif model_class == PatchTSMixerModel:
                expected_arg_names = [
                    "past_values",
                    "observed_mask",
                    "output_hidden_states",
                ]
            elif model_class in get_values(MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING) or model_class in get_values(
                MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING
            ):
                expected_arg_names = [
                    "past_values",
                    "target_values",
                    "output_hidden_states",
                    "return_loss",
                ]
            else:
                # PatchTSMixerForPrediction
                expected_arg_names = [
                    "past_values",
                    "observed_mask",
                    "future_values",
                    "output_hidden_states",
                    "return_loss",
                ]

            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

    @is_flaky()
    def test_retain_grad_hidden_states_attentions(self):
        super().test_retain_grad_hidden_states_attentions()

    @unittest.skip(reason="Model does not have input embeddings")
    def test_model_get_set_embeddings(self):
        pass


def prepare_batch(repo_id="ibm/patchtsmixer-etth1-test-data", file="pretrain_batch.pt"):
    # TODO: Make repo public
    file = hf_hub_download(repo_id=repo_id, filename=file, repo_type="dataset")
    check_torch_load_is_safe()
    batch = torch.load(file, map_location=torch_device, weights_only=True)
    return batch


@require_torch
@slow
class PatchTSMixerModelIntegrationTests(unittest.TestCase):
    def test_pretrain_head(self):
        model = PatchTSMixerForPretraining.from_pretrained("ibm/patchtsmixer-etth1-pretrain").to(torch_device)
        batch = prepare_batch()

        torch.manual_seed(0)
        with torch.no_grad():
            output = model(past_values=batch["past_values"].to(torch_device)).prediction_outputs
        num_patch = (
            max(model.config.context_length, model.config.patch_length) - model.config.patch_length
        ) // model.config.patch_stride + 1
        expected_shape = torch.Size(
            [
                64,
                model.config.num_input_channels,
                num_patch,
                model.config.patch_length,
            ]
        )
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[[-0.9106]],[[1.5326]],[[-0.8245]],[[0.7439]],[[-0.7830]],[[2.6256]],[[-0.6485]],],device=torch_device)  # fmt: skip
        torch.testing.assert_close(output[0, :7, :1, :1], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)

    def test_forecasting_head(self):
        model = PatchTSMixerForPrediction.from_pretrained("ibm/patchtsmixer-etth1-forecasting").to(torch_device)
        batch = prepare_batch(file="forecast_batch.pt")

        model.eval()
        torch.manual_seed(0)
        with torch.no_grad():
            output = model(
                past_values=batch["past_values"].to(torch_device),
                future_values=batch["future_values"].to(torch_device),
            ).prediction_outputs

        expected_shape = torch.Size([64, model.config.prediction_length, model.config.num_input_channels])
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor(
            [[0.2471, 0.5036, 0.3596, 0.5401, -0.0985, 0.3423, -0.8439]],
            device=torch_device,
        )
        torch.testing.assert_close(output[0, :1, :7], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)

    def test_prediction_generation(self):
        model = PatchTSMixerForPrediction.from_pretrained("ibm/patchtsmixer-etth1-generate").to(torch_device)
        batch = prepare_batch(file="forecast_batch.pt")
        print(batch["past_values"])

        torch.manual_seed(0)
        model.eval()
        with torch.no_grad():
            outputs = model.generate(past_values=batch["past_values"].to(torch_device))
        expected_shape = torch.Size((64, 1, model.config.prediction_length, model.config.num_input_channels))

        self.assertEqual(outputs.sequences.shape, expected_shape)

        expected_slice = torch.tensor(
            [[0.4308, -0.4731, 1.3512, -0.1038, -0.4655, 1.1279, -0.7179]],
            device=torch_device,
        )

        mean_prediction = outputs.sequences.mean(dim=1)

        torch.testing.assert_close(mean_prediction[0, -1:], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)


@require_torch
class PatchTSMixerFunctionalTests(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        """Setup method: Called once before test-cases execution"""
        cls.params = {}
        cls.params.update(
            context_length=32,
            patch_length=8,
            num_input_channels=3,
            patch_stride=8,
            d_model=4,
            expansion_factor=2,
            num_layers=3,
            dropout=0.2,
            mode="common_channel",  # common_channel,  mix_channel
            gated_attn=True,
            norm_mlp="LayerNorm",
            mask_type="random",
            random_mask_ratio=0.5,
            mask_patches=[2, 3],
            forecast_mask_ratios=[1, 1],
            mask_value=0,
            masked_loss=True,
            channel_consistent_masking=True,
            head_dropout=0.2,
            prediction_length=64,
            out_channels=None,
            # num_labels=3,
            num_targets=3,
            output_range=None,
            head_aggregation=None,
            scaling="std",
            use_positional_encoding=False,
            positional_encoding="sincos",
            self_attn=False,
            self_attn_heads=1,
            num_parallel_samples=4,
        )

        cls.num_patches = (
            max(cls.params["context_length"], cls.params["patch_length"]) - cls.params["patch_length"]
        ) // cls.params["patch_stride"] + 1

        # batch_size = 32
        batch_size = 2

        int(cls.params["prediction_length"] / cls.params["patch_length"])

        cls.data = torch.rand(
            batch_size,
            cls.params["context_length"],
            cls.params["num_input_channels"],
        )

        cls.enc_data = torch.rand(
            batch_size,
            cls.params["num_input_channels"],
            cls.num_patches,
            cls.params["patch_length"],
        )

        cls.enc_output = torch.rand(
            batch_size,
            cls.params["num_input_channels"],
            cls.num_patches,
            cls.params["d_model"],
        )

        cls.flat_enc_output = torch.rand(
            batch_size,
            cls.num_patches,
            cls.params["d_model"],
        )

        cls.correct_pred_output = torch.rand(
            batch_size,
            cls.params["prediction_length"],
            cls.params["num_input_channels"],
        )
        cls.correct_regression_output = torch.rand(batch_size, cls.params["num_targets"])

        cls.correct_pretrain_output = torch.rand(
            batch_size,
            cls.params["num_input_channels"],
            cls.num_patches,
            cls.params["patch_length"],
        )

        cls.correct_forecast_output = torch.rand(
            batch_size,
            cls.params["prediction_length"],
            cls.params["num_input_channels"],
        )

        cls.correct_sel_forecast_output = torch.rand(batch_size, cls.params["prediction_length"], 2)

        cls.correct_classification_output = torch.rand(
            batch_size,
            cls.params["num_targets"],
        )

        cls.correct_classification_classes = torch.randint(0, cls.params["num_targets"], (batch_size,))

    def test_patchtsmixer_encoder(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        enc = PatchTSMixerEncoder(config)
        output = enc(self.__class__.enc_data)
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)

    def test_patchmodel(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerModel(config)
        output = mdl(self.__class__.data)
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.patch_input.shape, self.__class__.enc_data.shape)

    def test_pretrainhead(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        head = PatchTSMixerPretrainHead(
            config=config,
        )
        output = head(self.__class__.enc_output)

        self.assertEqual(output.shape, self.__class__.correct_pretrain_output.shape)

    def test_pretrain_full(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerForPretraining(config)
        output = mdl(self.__class__.data)
        self.assertEqual(
            output.prediction_outputs.shape,
            self.__class__.correct_pretrain_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

    def test_pretrain_full_with_return_dict(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerForPretraining(config)
        output = mdl(self.__class__.data, return_dict=False)
        self.assertEqual(output[1].shape, self.__class__.correct_pretrain_output.shape)
        self.assertEqual(output[2].shape, self.__class__.enc_output.shape)
        self.assertEqual(output[0].item() < np.inf, True)

    def test_forecast_head(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        head = PatchTSMixerForPredictionHead(
            config=config,
        )
        # output = head(self.__class__.enc_output, raw_data = self.__class__.correct_pretrain_output)
        output = head(self.__class__.enc_output)

        self.assertEqual(output.shape, self.__class__.correct_forecast_output.shape)

    def check_module(
        self,
        task,
        params=None,
        output_hidden_states=True,
    ):
        config = PatchTSMixerConfig(**params)
        if task == "forecast":
            mdl = PatchTSMixerForPrediction(config)
            target_input = self.__class__.correct_forecast_output
            if config.prediction_channel_indices is not None:
                target_output = self.__class__.correct_sel_forecast_output
            else:
                target_output = target_input
            ref_samples = target_output.unsqueeze(1).expand(-1, config.num_parallel_samples, -1, -1)
            ground_truth_arg = "future_values"
            output_predictions_arg = "prediction_outputs"
        elif task == "classification":
            mdl = PatchTSMixerForTimeSeriesClassification(config)
            target_input = self.__class__.correct_classification_classes
            target_output = self.__class__.correct_classification_output
            ground_truth_arg = "target_values"
            output_predictions_arg = "prediction_outputs"
        elif task == "regression":
            mdl = PatchTSMixerForRegression(config)
            target_input = self.__class__.correct_regression_output
            target_output = self.__class__.correct_regression_output
            ref_samples = target_output.unsqueeze(1).expand(-1, config.num_parallel_samples, -1)
            ground_truth_arg = "target_values"
            output_predictions_arg = "regression_outputs"
        elif task == "pretrain":
            mdl = PatchTSMixerForPretraining(config)
            target_input = None
            target_output = self.__class__.correct_pretrain_output
            ground_truth_arg = None
            output_predictions_arg = "prediction_outputs"
        else:
            print("invalid task")

        enc_output = self.__class__.enc_output

        if target_input is None:
            output = mdl(self.__class__.data, output_hidden_states=output_hidden_states)
        else:
            output = mdl(
                self.__class__.data,
                **{
                    ground_truth_arg: target_input,
                    "output_hidden_states": output_hidden_states,
                },
            )

        prediction_outputs = getattr(output, output_predictions_arg)
        if isinstance(prediction_outputs, tuple):
            for t in prediction_outputs:
                self.assertEqual(t.shape, target_output.shape)
        else:
            self.assertEqual(prediction_outputs.shape, target_output.shape)

        self.assertEqual(output.last_hidden_state.shape, enc_output.shape)

        if output_hidden_states is True:
            self.assertEqual(len(output.hidden_states), params["num_layers"])

        else:
            self.assertEqual(output.hidden_states, None)

        self.assertEqual(output.loss.item() < np.inf, True)

        if config.loss == "nll" and task in ["forecast", "regression"]:
            samples = mdl.generate(self.__class__.data)
            self.assertEqual(samples.sequences.shape, ref_samples.shape)

    @parameterized.expand(
        list(
            itertools.product(
                ["common_channel", "mix_channel"],
                [True, False],
                [True, False, "mean", "std"],
                [True, False],
                [None, [0, 2]],
                ["mse", "nll"],
            )
        )
    )
    def test_forecast(self, mode, self_attn, scaling, gated_attn, prediction_channel_indices, loss):
        params = self.__class__.params.copy()
        params.update(
            mode=mode,
            self_attn=self_attn,
            scaling=scaling,
            prediction_channel_indices=prediction_channel_indices,
            gated_attn=gated_attn,
            loss=loss,
        )

        self.check_module(task="forecast", params=params)

    @parameterized.expand(
        list(
            itertools.product(
                ["common_channel", "mix_channel"],
                [True, False],
                [True, False, "mean", "std"],
                [True, False],
                ["max_pool", "avg_pool"],
            )
        )
    )
    def test_classification(self, mode, self_attn, scaling, gated_attn, head_aggregation):
        params = self.__class__.params.copy()
        params.update(
            mode=mode,
            self_attn=self_attn,
            scaling=scaling,
            head_aggregation=head_aggregation,
            gated_attn=gated_attn,
        )

        self.check_module(task="classification", params=params)

    @parameterized.expand(
        list(
            itertools.product(
                ["common_channel", "mix_channel"],
                [True, False],
                [True, False, "mean", "std"],
                [True, False],
                ["max_pool", "avg_pool"],
                ["mse", "nll"],
            )
        )
    )
    def test_regression(self, mode, self_attn, scaling, gated_attn, head_aggregation, loss):
        params = self.__class__.params.copy()
        params.update(
            mode=mode,
            self_attn=self_attn,
            scaling=scaling,
            head_aggregation=head_aggregation,
            gated_attn=gated_attn,
            loss=loss,
        )

        self.check_module(task="regression", params=params)

    @parameterized.expand(
        list(
            itertools.product(
                ["common_channel", "mix_channel"],
                [True, False],
                [True, False, "mean", "std"],
                [True, False],
                ["random", "forecast"],
                [True, False],
                [True, False],
            )
        )
    )
    def test_pretrain(
        self,
        mode,
        self_attn,
        scaling,
        gated_attn,
        mask_type,
        masked_loss,
        channel_consistent_masking,
    ):
        params = self.__class__.params.copy()
        params.update(
            mode=mode,
            self_attn=self_attn,
            scaling=scaling,
            gated_attn=gated_attn,
            mask_type=mask_type,
            masked_loss=masked_loss,
            channel_consistent_masking=channel_consistent_masking,
        )

        self.check_module(task="pretrain", params=params)

    def forecast_full_module(self, params=None, output_hidden_states=False, return_dict=None):
        config = PatchTSMixerConfig(**params)
        mdl = PatchTSMixerForPrediction(config)

        target_val = self.__class__.correct_forecast_output

        if config.prediction_channel_indices is not None:
            target_val = self.__class__.correct_sel_forecast_output

        enc_output = self.__class__.enc_output

        output = mdl(
            self.__class__.data,
            future_values=self.__class__.correct_forecast_output,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if isinstance(output, tuple):
            output = PatchTSMixerForPredictionOutput(*output)

        if config.loss == "mse":
            self.assertEqual(output.prediction_outputs.shape, target_val.shape)

        self.assertEqual(output.last_hidden_state.shape, enc_output.shape)

        if output_hidden_states is True:
            self.assertEqual(len(output.hidden_states), params["num_layers"])

        else:
            self.assertEqual(output.hidden_states, None)

        self.assertEqual(output.loss.item() < np.inf, True)

        if config.loss == "nll":
            samples = mdl.generate(self.__class__.data)
            ref_samples = target_val.unsqueeze(1).expand(-1, params["num_parallel_samples"], -1, -1)
            self.assertEqual(samples.sequences.shape, ref_samples.shape)

    def test_forecast_full(self):
        self.check_module(task="forecast", params=self.__class__.params, output_hidden_states=True)
        # self.forecast_full_module(self.__class__.params, output_hidden_states = True)

    def test_forecast_full_2(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
        )
        self.forecast_full_module(params, output_hidden_states=True)

    def test_forecast_full_2_with_return_dict(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
        )
        self.forecast_full_module(params, output_hidden_states=True, return_dict=False)

    def test_forecast_full_3(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
        )
        self.forecast_full_module(params, output_hidden_states=True)

    def test_forecast_full_5(self):
        params = self.__class__.params.copy()
        params.update(
            self_attn=True,
            use_positional_encoding=True,
            positional_encoding="sincos",
        )
        self.forecast_full_module(params, output_hidden_states=True)

    def test_forecast_full_4(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
            prediction_channel_indices=[0, 2],
        )
        self.forecast_full_module(params)

    def test_forecast_full_distributional(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
            prediction_channel_indices=[0, 2],
            loss="nll",
            distribution_output="normal",
        )

        self.forecast_full_module(params)

    def test_forecast_full_distributional_2(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
            prediction_channel_indices=[0, 2],
            loss="nll",
            # distribution_output = "normal",
        )
        self.forecast_full_module(params)

    def test_forecast_full_distributional_3(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
            # prediction_channel_indices=[0, 2],
            loss="nll",
            distribution_output="normal",
        )
        self.forecast_full_module(params)

    def test_forecast_full_distributional_4(self):
        params = self.__class__.params.copy()
        params.update(
            mode="mix_channel",
            # prediction_channel_indices=[0, 2],
            loss="nll",
            distribution_output="normal",
        )
        self.forecast_full_module(params)

    def test_classification_head(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        head = PatchTSMixerLinearHead(
            config=config,
        )
        # output = head(self.__class__.enc_output, raw_data = self.__class__.correct_pretrain_output)
        output = head(self.__class__.enc_output)

        self.assertEqual(output.shape, self.__class__.correct_classification_output.shape)

    def test_classification_full(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerForTimeSeriesClassification(config)
        output = mdl(
            self.__class__.data,
            target_values=self.__class__.correct_classification_classes,
        )
        self.assertEqual(
            output.prediction_outputs.shape,
            self.__class__.correct_classification_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

    def test_classification_full_with_return_dict(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerForTimeSeriesClassification(config)
        output = mdl(
            self.__class__.data,
            target_values=self.__class__.correct_classification_classes,
            return_dict=False,
        )
        if isinstance(output, tuple):
            output = PatchTSMixerForTimeSeriesClassificationOutput(*output)
        self.assertEqual(
            output.prediction_outputs.shape,
            self.__class__.correct_classification_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

    def test_regression_head(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        head = PatchTSMixerLinearHead(
            config=config,
        )
        output = head(self.__class__.enc_output)
        self.assertEqual(output.shape, self.__class__.correct_regression_output.shape)

    def test_regression_full(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerForRegression(config)
        output = mdl(self.__class__.data, target_values=self.__class__.correct_regression_output)
        self.assertEqual(
            output.regression_outputs.shape,
            self.__class__.correct_regression_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

    def test_regression_full_with_return_dict(self):
        config = PatchTSMixerConfig(**self.__class__.params)
        mdl = PatchTSMixerForRegression(config)
        output = mdl(
            self.__class__.data,
            target_values=self.__class__.correct_regression_output,
            return_dict=False,
        )
        if isinstance(output, tuple):
            output = PatchTSMixerForRegressionOutput(*output)
        self.assertEqual(
            output.regression_outputs.shape,
            self.__class__.correct_regression_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

    def test_regression_full_distribute(self):
        params = self.__class__.params.copy()
        params.update(loss="nll", distribution_output="normal")

        config = PatchTSMixerConfig(**params)

        mdl = PatchTSMixerForRegression(config)
        output = mdl(self.__class__.data, target_values=self.__class__.correct_regression_output)
        self.assertEqual(
            output.regression_outputs[0].shape,
            self.__class__.correct_regression_output.shape,
        )
        self.assertEqual(
            output.regression_outputs[1].shape,
            self.__class__.correct_regression_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

        if config.loss == "nll":
            samples = mdl.generate(self.__class__.data)
            ref_samples = self.__class__.correct_regression_output.unsqueeze(1).expand(
                -1, params["num_parallel_samples"], -1
            )
            self.assertEqual(samples.sequences.shape, ref_samples.shape)

    def test_regression_full_distribute_2(self):
        params = self.__class__.params.copy()
        params.update(loss="nll", distribution_output="student_t")

        config = PatchTSMixerConfig(**params)

        mdl = PatchTSMixerForRegression(config)
        output = mdl(self.__class__.data, target_values=self.__class__.correct_regression_output)
        self.assertEqual(
            output.regression_outputs[0].shape,
            self.__class__.correct_regression_output.shape,
        )
        self.assertEqual(
            output.regression_outputs[1].shape,
            self.__class__.correct_regression_output.shape,
        )
        self.assertEqual(output.last_hidden_state.shape, self.__class__.enc_output.shape)
        self.assertEqual(output.loss.item() < np.inf, True)

        if config.loss == "nll":
            samples = mdl.generate(self.__class__.data)
            ref_samples = self.__class__.correct_regression_output.unsqueeze(1).expand(
                -1, params["num_parallel_samples"], -1
            )
            self.assertEqual(samples.sequences.shape, ref_samples.shape)