import math
from collections import OrderedDict

import mlflow
import torch
from mlflow.entities import RunData, RunInfo, Run
from mlflow.entities.model_registry import ModelVersion
from mlflow.models.model import ModelInfo
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from torch import tensor


def create_model():
    x = torch.linspace(-math.pi, math.pi, 2000)
    y = torch.sin(x)
    p = torch.tensor([1, 2, 3])
    xx = x.unsqueeze(-1).pow(p)
    model = torch.nn.Sequential(torch.nn.Linear(3, 1), torch.nn.Flatten(0, 1))

    loss_fn = torch.nn.MSELoss(reduction="sum")

    learning_rate = 1e-6
    for t in range(1000):
        y_pred = model(xx)
        loss = loss_fn(y_pred, y)
        model.zero_grad()
        loss.backward()
        with torch.no_grad():
            for param in model.parameters():
                param -= learning_rate * param.grad

    return model


def model_sklearn():
    params = {"n_estimators": 5, "random_state": 42}
    sk_learn_rfr = RandomForestRegressor(**params)
    return sk_learn_rfr


def mock_log_state_dict(*_, **__):
    return OrderedDict(
        [
            (
                "encoder.0.weight",
                tensor(
                    [
                        [
                            0.2635,
                            0.5033,
                            -0.2808,
                            -0.4609,
                            0.2749,
                            -0.5048,
                            -0.0960,
                            0.6310,
                            -0.4750,
                            0.1700,
                        ],
                        [
                            -0.1626,
                            0.1635,
                            -0.2873,
                            0.5045,
                            -0.3312,
                            0.0791,
                            -0.4530,
                            -0.5068,
                            0.1734,
                            0.0485,
                        ],
                        [
                            -0.5209,
                            -0.1975,
                            -0.3471,
                            -0.6511,
                            0.5214,
                            0.4137,
                            -0.2795,
                            0.2267,
                            0.2497,
                            0.3451,
                        ],
                    ]
                ),
            )
        ]
    )


def mock_log_model_pytorch(*_, **__):
    return ModelInfo(
        artifact_path="model",
        flavors={
            "pytorch": {"model_data": "data", "pytorch_version": "1.11.0", "code": None},
            "python_function": {
                "pickle_module_name": "mlflow.pytorch.pickle_module",
                "loader_module": "mlflow.pytorch",
                "python_version": "3.8.5",
                "data": "data",
                "env": "conda.yaml",
            },
        },
        model_uri="runs:/f2dad48d86c748358b47bdaa24b2619c/model",
        model_uuid="adisajdasjdoasd",
        run_id="f2dad48d86c748358b47bdaa24b2619c",
        saved_input_example_info=None,
        signature_dict=None,
        utc_time_created="2022-05-23 22:35:59.557372",
        mlflow_version="1.26.0",
    )


def mock_log_model_sklearn(*_, **__):
    return ModelInfo(
        artifact_path="model",
        flavors={
            "sklearn": {"model_data": "data", "sklearn_version": "1.11.0", "code": None},
            "python_function": {
                "pickle_module_name": "mlflow.sklearn.pickle_module",
                "loader_module": "mlflow.sklearn",
                "python_version": "3.8.5",
                "data": "data",
                "env": "conda.yaml",
            },
        },
        model_uri="runs:/f2dad48d86c748358b47bdaa24b2619c/model",
        model_uuid="adisajdasjdoasd",
        run_id="f2dad48d86c748358b47bdaa24b2619c",
        saved_input_example_info=None,
        signature_dict=None,
        utc_time_created="2022-05-23 22:35:59.557372",
        mlflow_version="1.26.0",
    )


def mock_transition_stage(*_, **__):
    return ModelVersion(
        creation_timestamp=1653402941169,
        current_stage="Production",
        description="",
        last_updated_timestamp=1653402941191,
        name="testtest:error",
        run_id="6e85c26e6e8b49fdb493807d5a527a2c",
        run_link="",
        source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model",
        status="READY",
        status_message="",
        tags={},
        user_id="",
        version="5",
    )


def mock_get_model_version(*_, **__):
    return [
        ModelVersion(
            creation_timestamp=1653402941169,
            current_stage="Production",
            description="",
            last_updated_timestamp=1653402941191,
            name="testtest:error",
            run_id="6e85c26e6e8b49fdb493807d5a527a2c",
            run_link="",
            source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model",
            status="READY",
            status_message="",
            tags={},
            user_id="",
            version="5",
        )
    ]


def return_scaler():
    scaler = StandardScaler()
    data = [[0, 0], [0, 0], [1, 1], [1, 1]]
    scaler.fit_transform(data)
    return scaler


def return_empty_rundata():
    return Run(
        run_info=RunInfo(
            artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts",
            end_time=None,
            experiment_id="0",
            lifecycle_stage="active",
            run_id="a7c0b376530b40d7b23e6ce2081c899c",
            run_uuid="a7c0b376530b40d7b23e6ce2081c899c",
            start_time=1658788772612,
            status="RUNNING",
            user_id="lol",
        ),
        run_data=RunData(metrics={}, tags={}, params={}),
    )


def return_sklearn_rundata():
    return Run(
        run_info=RunInfo(
            artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts",
            end_time=None,
            experiment_id="0",
            lifecycle_stage="active",
            run_id="a7c0b376530b40d7b23e6ce2081c899c",
            run_uuid="a7c0b376530b40d7b23e6ce2081c899c",
            start_time=1658788772612,
            status="RUNNING",
            user_id="lol",
        ),
        run_data=RunData(
            metrics={},
            tags={},
            params=[
                mlflow.entities.Param(
                    "secondary_artifacts",
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                    "cnmUTowHdmVyYm9zZZSJaD5oP3ViLg==\n",
                )
            ],
        ),
    )


def return_pytorch_rundata_dict():
    return Run(
        run_info=RunInfo(
            artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts",
            end_time=None,
            experiment_id="0",
            lifecycle_stage="active",
            run_id="a7c0b376530b40d7b23e6ce2081c899c",
            run_uuid="a7c0b376530b40d7b23e6ce2081c899c",
            start_time=1658788772612,
            status="RUNNING",
            user_id="lol",
        ),
        run_data=RunData(
            metrics={},
            tags={},
            params=[
                mlflow.entities.Param(
                    "metadata",
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                    "cm91cHOUXZR9lCiMAmxylEc/UGJN0vGp/IwFYmV0YXOURz/szMzMzMzNRz/v987ZFocrhpSMA2Vw\n"
                    "c5RHPkV5juIwjDqMDHdlaWdodF9kZWNheZRLAIwHYW1zZ3JhZJSJjAhtYXhpbWl6ZZSJjAZwYXJh\n"
                    "bXOUXZQoSwBLAUsCSwNLBEsFSwZLB0sISwlLCksLSwxLDWV1YXWMCnRocmVzaG9sZHOUjBVudW1w\n"
                    "eS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5SMBW51bXB5lIwHbmRhcnJheZSTlEsA\n"
                    "hZRDAWKUh5RSlChLAUsBhZRq/AEAAIwFZHR5cGWUk5SMAmY4lImIh5RSlChLA4wBPJROTk5K////\n"
                    "/0r/////SwB0lGKJQwjp5y3oX8ncP5R0lGKMCWVycl9zdGF0c5R9lCiMBG1lYW6UavsBAABq/gEA\n"
                    "AEsAhZRqAAIAAIeUUpQoSwFLAYWUaggCAACJQwiTKv0JGrWMP5R0lGKMA3N0ZJRq+wEAAGr+AQAA\n"
                    "SwCFlGoAAgAAh5RSlChLAUsBhZRqCAIAAIlDCLipPmXPl8I/lHSUYnV1Lg==\n",
                ),
                mlflow.entities.Param("model_key", "sandbox:lolsadsd::sad:lolsadasdsda"),
                mlflow.entities.Param(
                    "secondary_artifacts",
                    "gASVogEAAAAAAAB9lCiMB3ByZXByb2OUjBBza2xlYXJuLnBpcGVsaW5llIwIUGlwZWxpbmWUk5Qp\n"
                    "gZR9lCiMBXN0ZXBzlF2UKIwOc3RhbmRhcmRzY2FsZXKUjBtza2xlYXJuLnByZXByb2Nlc3Npbmcu\n"
                    "X2RhdGGUjA5TdGFuZGFyZFNjYWxlcpSTlCmBlH2UKIwJd2l0aF9tZWFulIiMCHdpdGhfc3RklIiM\n"
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                    "bnVtYWxvZ2ljLnByZXByb2Nlc3MudHJhbnNmb3JtZXKUjA5Mb2dUcmFuc2Zvcm1lcpSTlCmBlH2U\n"
                    "jAphZGRfZmFjdG9ylEsCc2KGlGWMBm1lbW9yeZROjAd2ZXJib3NllIloEmgTdWKMCHBvc3Rwcm9j\n"
                    "lGgEKYGUfZQoaAddlIwKbm9ybWFsaXplcpRoCowKTm9ybWFsaXplcpSTlCmBlH2UKIwEbm9ybZSM\n"
                    "AmwylGgRiGgSaBN1YoaUYWgdTmgeiWgSaBN1YnUu\n",
                ),
            ],
        ),
    )


def return_pytorch_rundata_list():
    return Run(
        run_info=RunInfo(
            artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts",
            end_time=None,
            experiment_id="0",
            lifecycle_stage="active",
            run_id="a7c0b376530b40d7b23e6ce2081c899c",
            run_uuid="a7c0b376530b40d7b23e6ce2081c899c",
            start_time=1658788772612,
            status="RUNNING",
            user_id="lol",
        ),
        run_data=RunData(
            metrics={},
            tags={},
            params=[
                mlflow.entities.Param(
                    "metadata",
                    "gASV+2AAAAAAAAB9lCiMEG1vZGVsX3N0YXRlX2RpY3SUjAtjb2xsZWN0aW9uc5SMC09yZGVyZWRE\n"
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                    "bnNvcl92MpSTlCiMDXRvcmNoLnN0b3JhZ2WUjBBfbG9hZF9mcm9tX2J5dGVzlJOUQv0DAACAAooK\n"
                    "bPycRvkgaqhQGS6AAk3pAy6AAn1xAChYEAAAAHByb3RvY29sX3ZlcnNpb25xAU3pA1gNAAAAbGl0\n"
                    "dGxlX2VuZGlhbnECiFgKAAAAdHlwZV9zaXplc3EDfXEEKFgFAAAAc2hvcnRxBUsCWAMAAABpbnRx\n"
                    "BksEWAQAAABsb25ncQdLBHV1LoACKFgHAAAAc3RvcmFnZXEAY3RvcmNoCkZsb2F0U3RvcmFnZQpx\n"
                    "AVgPAAAAMTA1NTUzMTMzMzEwNzM2cQJYAwAAAGNwdXEDS8BOdHEEUS6AAl1xAFgPAAAAMTA1NTUz\n"
                    "MTMzMzEwNzM2cQFhLsAAAAAAAAAAs883PebKjj36NOy+zy7IvmXtM78Ve68+XkxDvFQluL6PW7O+\n"
                    "FKigvnXGAr41EGk+Un2CPmy2Kb2uRzE+885KP7kH/L4EcXg+YivkPhKaLD892AI/pzDgvsoFQz+J\n"
                    "iwA/7qsDPwCjEr+GuaC+jQOHPl9EDr72bIc+kNZRvklaAr8RBfU+JZGMvgWfRbtq49M+W8XWvQOq\n"
                    "572EFb4++f8YPwombT78w8U+PLjMvp8Iz742eBI/TU6PvouyBb+zU+O9i2HDvJjpVD/2MM69j1RY\n"
                    "P4kzXD8wjko/O/1RP8NOS7xUaRY+Y5o+P6GSWD85cQU/IwcAvzzJX76+mYK+tIwzvd9cLD9PPaW9\n"
                    "xc8kPqmDDT8W0qU+bQYHvsOspTxiz9o9e+AIvyRxPLvwEIM+x7XLPfCbtL7KCEC/7HvovvQBlD3E\n"
                    "Gxu/V8JNPlNNwb7Xkv++Co2rPQMhVL4xbtk+nZ9qPpBs2j5gn/g+91UgPnC6Jj7/oLe8mBAKP4vU\n"
                    "vr5/QDK/mwEYPwxhAb4BgQi/S50xvtBudz0sFXS+cYiFvjJioz7NtfI8UL06PzJEnb6JOhY9EVO5\n"
                    "vdw7mT4NZEq+QRKFPK7U/r4oniS/bK8bP6Umvj5PDxw/Q/j9va5FbT4QwwE/6RHGPrxERL59cey+\n"
                    "+PMqP6R6oj4wLA8/QMI3vkIb9r7oFcI+K8BXPfgT075j5vw+ma0FP/Dr8r4YqZU80+txvnuXrT7B\n"
                    "vuO+cDxLPelYKz4zfFi9uB+zPL4mn70H4u0+VOywPiyVNb/Tc9A+mGG6PtMhAb+9MXS+7ayWvbzu\n"
                    "jD7nnp0+xy63vUJJ6rvsioq+LlTgPQWmLj9XR48+QVOtPpXwLD/DwP++8rgUPT6V6rw1916+1e0D\n"
                    "v//v5b5kvRC+DmBAPwUD0z47P/W+VCOhvh70pD6R8iU89cnQPk58fr6o0W+9OVhDvAeHx74pT4s+\n"
                    "oIIRv+7ytD1fhAY/aBnAvmb61j6bE+2+rJe1Pqhz5z6BHw6/wSb9PjeRAr8S7dM9lIWUUpRLAEsQ\n"
                    "SwyGlEsMSwGGlIloBClSlHSUUpSMDmVuY29kZXIuMC5iaWFzlGgJKGgMQj0BAACAAooKbPycRvkg\n"
                    "aqhQGS6AAk3pAy6AAn1xAChYEAAAAHByb3RvY29sX3ZlcnNpb25xAU3pA1gNAAAAbGl0dGxlX2Vu\n"
                    "ZGlhbnECiFgKAAAAdHlwZV9zaXplc3EDfXEEKFgFAAAAc2hvcnRxBUsCWAMAAABpbnRxBksEWAQA\n"
                    "AABsb25ncQdLBHV1LoACKFgHAAAAc3RvcmFnZXEAY3RvcmNoCkZsb2F0U3RvcmFnZQpxAVgPAAAA\n"
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                    "A1gNAAAAbGl0dGxlX2VuZGlhbnECiFgKAAAAdHlwZV9zaXplc3EDfXEEKFgFAAAAc2hvcnRxBUsC\n"
                    "WAMAAABpbnRxBksEWAQAAABsb25ncQdLBHV1LoACKFgHAAAAc3RvcmFnZXEAY3RvcmNoCkZsb2F0\n"
                    "U3RvcmFnZQpxAVgPAAAAMTA1NTUzMTMzMzE0NzM2cQJYAwAAAGNwdXEDSwxOdHEEUS6AAl1xAFgP\n"
                    "AAAAMTA1NTUzMTMzMzE0NzM2cQFhLgwAAAAAAAAA9GhFOMPQqDcw/lA3G1yxN5uVlzeUOZA3vwfK\n"
                    "N3dPQjck9pE3jGGqN6mnMjgfwY43lIWUUpRLAEsMhZRLAYWUiWgEKVKUdJRSlHV1jAxwYXJhbV9n\n"
                    "cm91cHOUXZR9lCiMAmxylEc/UGJN0vGp/IwFYmV0YXOURz/szMzMzMzNRz/v987ZFocrhpSMA2Vw\n"
                    "c5RHPkV5juIwjDqMDHdlaWdodF9kZWNheZRLAIwHYW1zZ3JhZJSJjAhtYXhpbWl6ZZSJjAZwYXJh\n"
                    "bXOUXZQoSwBLAUsCSwNLBEsFSwZLB0sISwlLCksLSwxLDWV1YXWMCnRocmVzaG9sZHOUjBVudW1w\n"
                    "eS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5SMBW51bXB5lIwHbmRhcnJheZSTlEsA\n"
                    "hZRDAWKUh5RSlChLAUsBhZRq/AEAAIwFZHR5cGWUk5SMAmY4lImIh5RSlChLA4wBPJROTk5K////\n"
                    "/0r/////SwB0lGKJQwjp5y3oX8ncP5R0lGKMCWVycl9zdGF0c5R9lCiMBG1lYW6UavsBAABq/gEA\n"
                    "AEsAhZRqAAIAAIeUUpQoSwFLAYWUaggCAACJQwiTKv0JGrWMP5R0lGKMA3N0ZJRq+wEAAGr+AQAA\n"
                    "SwCFlGoAAgAAh5RSlChLAUsBhZRqCAIAAIlDCLipPmXPl8I/lHSUYnV1Lg==\n",
                ),
                mlflow.entities.Param("model_key", "sandbox:lolsadsd::sad:lolsadasdsda"),
                mlflow.entities.Param(
                    "secondary_artifacts",
                    "gASVjQEAAAAAAABdlCiMEHNrbGVhcm4ucGlwZWxpbmWUjAhQaXBlbGluZZSTlCmBlH2UKIwFc3Rl\n"
                    "cHOUXZQojA5zdGFuZGFyZHNjYWxlcpSMG3NrbGVhcm4ucHJlcHJvY2Vzc2luZy5fZGF0YZSMDlN0\n"
                    "YW5kYXJkU2NhbGVylJOUKYGUfZQojAl3aXRoX21lYW6UiIwId2l0aF9zdGSUiIwEY29weZSIjBBf\n"
                    "c2tsZWFybl92ZXJzaW9ulIwFMS4xLjGUdWKGlIwObG9ndHJhbnNmb3JtZXKUjCBudW1hbG9naWMu\n"
                    "cHJlcHJvY2Vzcy50cmFuc2Zvcm1lcpSMDkxvZ1RyYW5zZm9ybWVylJOUKYGUfZSMCmFkZF9mYWN0\n"
                    "b3KUSwJzYoaUZYwGbWVtb3J5lE6MB3ZlcmJvc2WUiWgRaBJ1YmgDKYGUfZQoaAZdlIwKbm9ybWFs\n"
                    "aXplcpRoCYwKTm9ybWFsaXplcpSTlCmBlH2UKIwEbm9ybZSMAmwylGgQiGgRaBJ1YoaUYWgcTmgd\n"
                    "iWgRaBJ1YmUu\n",
                ),
            ],
        ),
    )
