# Copyright (C) 2018-2026 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import pytest

from pytorch_layer_test_class import PytorchLayerTest, skip_if_export


class TestMinMax(PytorchLayerTest):
    def _prepare_input(self, input_dtype="float32", second_input=False, second_input_dtype="float32"):
        import numpy as np
        if not second_input:
            return (self.random.randn(1, 3, 10, 10, dtype=input_dtype),)
        return (self.random.randn(1, 3, 10, 10, dtype=input_dtype), self.random.randn(1, 3, 10, 10, dtype=second_input_dtype))

    def create_model(self, op_type, axes, keep_dims, single_input=True, dtypes=("float32", "float32")):
        import torch
        op_types = {
            'max': torch.max,
            'min': torch.min
        }

        dtypes_map = {
            "float32": torch.float32,
            "float64": torch.float64,
            "int32": torch.int32,
            "int64": torch.int64,
            "uint8": torch.uint8
        }

        op = op_types[op_type]

        class aten_min_max(torch.nn.Module):
            def __init__(self, op):
                super().__init__()
                self.op = op

            def forward(self, x):
                return self.op(x)

        class aten_min_max_3args(torch.nn.Module):
            def __init__(self, op, axes=None, keep_dims=None):
                super().__init__()
                self.op = op
                self.axes = axes
                self.keep_dims = keep_dims

            def forward(self, x):
                return self.op(x, self.axes, self.keep_dims)

        class aten_min_max_2args(torch.nn.Module):
            def __init__(self, op, l_dtype, r_dtype):
                super().__init__()
                self.op = op
                self.l_dtype = l_dtype
                self.r_dtype = r_dtype

            def forward(self, x, y):
                return self.op(x.to(self.l_dtype), y.to(self.r_dtype))

        if axes is None and keep_dims is None:
            if single_input:
                model_cls = aten_min_max(op)
            else:
                l_dtype = dtypes_map[dtypes[0]]
                r_dtype = dtypes_map[dtypes[1]]
                model_cls = aten_min_max_2args(op, l_dtype, r_dtype)
        else:
            model_cls = aten_min_max_3args(op, axes, keep_dims)

        return model_cls, f"aten::{op_type}"

    @pytest.mark.parametrize("axes,keep_dims", [(None, None), (1, False), (1, True), (-1, False), (-1, True)])
    @pytest.mark.parametrize("op_type", ['min', 'max'])
    @pytest.mark.nightly
    @pytest.mark.precommit
    @pytest.mark.precommit_torch_export
    @pytest.mark.precommit_fx_backend
    def test_reduce_min_max(self, axes, keep_dims, op_type, ie_device, precision, ir_version):
        self._test(*self.create_model(op_type, axes, keep_dims,
                                      single_input=True), ie_device, precision, ir_version, fx_kind=f"aten.{op_type}")

    @pytest.mark.parametrize("op_type", ['min', 'max'])
    @pytest.mark.parametrize("second_input_dtype", ["float32", "int32", "float64", "int64", "uint8"])
    @pytest.mark.parametrize("first_input_dtype", ["float32", "int32", "float64", "int64", "uint8"])
    @pytest.mark.nightly
    @pytest.mark.precommit
    @pytest.mark.precommit_torch_export
    @pytest.mark.precommit_fx_backend
    def test_min_max(self, op_type, first_input_dtype, second_input_dtype, ie_device, precision, ir_version):
        if ie_device == "GPU" and first_input_dtype == "uint8" and second_input_dtype == "uint8":
            pytest.xfail(reason="Cumsum for i8 is unsupported on GPU")
        self._test(*self.create_model(op_type, None, None, single_input=False, dtypes=(first_input_dtype, second_input_dtype)),
                   ie_device, precision, ir_version, kwargs_to_prepare_input=
                   {"second_input": True, "input_dtype": first_input_dtype, "second_input_dtype": second_input_dtype},
                   fx_kind=f"aten.{op_type}")


class TestPrimMax(PytorchLayerTest):
    def _prepare_input(self, first_input, second_input, dtype="float"):
        import numpy as np
        first_array = np.array(first_input).astype(dtype)
        if not second_input:
            return (first_array,)
        second_array = np.array(second_input).astype(dtype)
        return (first_array, second_array)

    def create_model(self, case):
        import torch

        class prim_max_2_values(torch.nn.Module):

            def forward(self, x: float, y: float):
                return max(x, y)

        class prim_max_2_list_values(torch.nn.Module):
            def forward(self, x: float, y: float):
                return torch.tensor(max([x, x + y], [y, y - x]))

        class prim_max_1list_several_values(torch.nn.Module):

            def forward(self, x: float, y: float):
                return max([x, y, x + y])

        class prim_max_one_value(torch.nn.Module):
            def forward(self, x: float, y: float):
                return max(x)

        cases = {
            "2_values": prim_max_2_values,
            "2_list_values": prim_max_2_list_values,
            "list_several_values": prim_max_1list_several_values,
            "one_value": prim_max_one_value
        }
        model_cls = cases[case]()


        return model_cls, "prim::max"

    @pytest.mark.parametrize("case", ["2_values", "2_list_values", "list_several_values", "one_value"])
    @pytest.mark.parametrize("kwargs_to_prepare_input", [
        {"first_input": 0, "second_input": 1, "dtype": "float"},
        {"first_input": 1, "second_input": 1, "dtype": "float"},
        {"first_input": 2, "second_input": 1, "dtype": "float"},
        {"first_input": 0, "second_input": 1, "dtype": "int"},
        {"first_input": 1, "second_input": 1, "dtype": "int"},
        {"first_input": 2, "second_input": 1, "dtype": "int"},
        {"first_input": 0, "second_input": 1, "dtype": "bool"},
        {"first_input": 1, "second_input": 1, "dtype": "bool"},
        {"first_input": 2, "second_input": 1, "dtype": "bool"},
    ])
    @pytest.mark.nightly
    @pytest.mark.precommit
    def test_max(self, case, kwargs_to_prepare_input, ie_device, precision, ir_version):
        self._test(*self.create_model(case),
                   ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input, use_mo_convert=False)

class TestPrimMin(PytorchLayerTest):
    def _prepare_input(self, first_input, second_input, dtype="float"):
        import numpy as np
        first_array = np.array(first_input).astype(dtype)
        if not second_input:
            return (first_array,)
        second_array = np.array(second_input).astype(dtype)
        return (first_array, second_array)

    def create_model(self, case):
        import torch

        class prim_min_2_values(torch.nn.Module):

            def forward(self, x: float, y: float):
                return min(x, y)

        class prim_min_2_list_values(torch.nn.Module):
            def forward(self, x: float, y: float):
                return torch.tensor(min([x, x + y], [y, y - x]))

        class prim_min_1list_several_values(torch.nn.Module):

            def forward(self, x: float, y: float):
                return min([x, y, x + y])

        class prim_min_one_value(torch.nn.Module):
            def forward(self, x: float, y: float):
                return min(x)

        cases = {
            "2_values": prim_min_2_values,
            "2_list_values": prim_min_2_list_values,
            "list_several_values": prim_min_1list_several_values,
            "one_value": prim_min_one_value
        }
        model_cls = cases[case]()


        return model_cls, "prim::min"

    @pytest.mark.parametrize("case", ["2_values", "2_list_values", "list_several_values", "one_value"])
    @pytest.mark.parametrize("kwargs_to_prepare_input", [
        {"first_input": 0, "second_input": 1, "dtype": "float"},
        {"first_input": 1, "second_input": 1, "dtype": "float"},
        {"first_input": 2, "second_input": 1, "dtype": "float"},
        {"first_input": 0, "second_input": 1, "dtype": "int"},
        {"first_input": 1, "second_input": 1, "dtype": "int"},
        {"first_input": 2, "second_input": 1, "dtype": "int"},
        {"first_input": 0, "second_input": 1, "dtype": "bool"},
        {"first_input": 1, "second_input": 1, "dtype": "bool"},
        {"first_input": 2, "second_input": 1, "dtype": "bool"},
    ])
    @pytest.mark.nightly
    @pytest.mark.precommit
    def test_min(self, case, kwargs_to_prepare_input, ie_device, precision, ir_version):
        self._test(*self.create_model(case),
                   ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input, use_mo_convert=False)


class TestMinimumMaximum(PytorchLayerTest):
    def _prepare_input(self, input_dtype="float32", second_input_dtype="float32", out=False):
        import numpy as np
        x = self.random.randn(1, 3, 10, 10, dtype=input_dtype)
        y = self.random.randn(1, 3, 10, 10, dtype=second_input_dtype)
        if not out:
            return x, y
        return (x, y, np.zeros_like(x).astype(input_dtype))

    def create_model(self, op_type, dtypes=("float32", "float32"), out=False):
        import torch
        op_types = {
            "maximum": torch.maximum,
            "minimum": torch.minimum
        }

        dtypes_map = {
            "float32": torch.float32,
            "int32": torch.int32,
            "int64": torch.int64,
            "float64": torch.float64
        }

        op = op_types[op_type]

        class aten_minimum_maximum(torch.nn.Module):
            def __init__(self, op, l_dtype, r_dtype, out):
                super().__init__()
                self.op = op
                self.l_dtype = l_dtype
                self.r_dtype = r_dtype
                if out:
                    self.forward = self.forward_out

            def forward_out(self, x, y, z):
                return self.op(x.to(self.l_dtype), y.to(self.r_dtype), out=z), z

            def forward(self, x, y):
                return self.op(x.to(self.l_dtype), y.to(self.r_dtype))

        l_dtype = dtypes_map[dtypes[0]]
        r_dtype = dtypes_map[dtypes[1]]
        model_cls = aten_minimum_maximum(op, l_dtype, r_dtype, out)

        return model_cls, f"aten::{op_type}"

    @pytest.mark.parametrize("op_type", ["minimum", "maximum"])
    @pytest.mark.parametrize("second_input_dtype", ["float32", "int32", "int64", "float64"])
    @pytest.mark.parametrize("first_input_dtype", ["float32", "int32", "int64", "float64"])
    @pytest.mark.nightly
    @pytest.mark.precommit
    @pytest.mark.precommit_torch_export
    @pytest.mark.precommit_fx_backend
    def test_minimum_maximum(
        self, op_type, first_input_dtype, second_input_dtype, ie_device, precision, ir_version
        ):
        self._test(*self.create_model(op_type, dtypes=(first_input_dtype, second_input_dtype), out=False),
                   ie_device, precision, ir_version, kwargs_to_prepare_input=
                   {"input_dtype": first_input_dtype, "second_input_dtype": second_input_dtype, "out": False},
                   fx_kind=f"aten.{op_type}")


    @pytest.mark.parametrize("op_type", ['minimum', 'maximum'])
    @pytest.mark.parametrize("input_dtype", ["float32", "int32", "int64", "float64"])
    @pytest.mark.nightly
    @pytest.mark.precommit
    def test_minimum_maximum_out(
        self, op_type, input_dtype, ie_device, precision, ir_version
        ):
        self._test(*self.create_model(op_type, dtypes=(input_dtype, input_dtype), out=True),
                   ie_device, precision, ir_version, kwargs_to_prepare_input=
                   {"input_dtype": input_dtype, "second_input_dtype": input_dtype,
                    "out": True}
                   )


class TestAminAmax(PytorchLayerTest):
    def _prepare_input(self, input_dtype="float32", out=False, axes=None, keep_dims=False):
        import numpy as np
        x = self.random.randn(1, 3, 10, 10, dtype=input_dtype)
        if not out:
            return (x,)
        if isinstance(axes, list):
            axes = tuple(axes)
        out = np.zeros_like(np.max(x, axis=axes, keepdims=keep_dims), dtype=input_dtype)
        return (x, out)

    def create_model(self, op_type, axis, keep_dims, out=False):
        import torch
        op_types = {
            "amax": torch.amax,
            "amin": torch.amin
        }


        op = op_types[op_type]

        class aten_amin_amax(torch.nn.Module):
            def __init__(self, op, axis, keep_dims, out):
                super().__init__()
                self.op = op
                self.axis = axis
                self.keep_dims = keep_dims
                if out:
                    self.forward = self.forward_out

            def forward_out(self, x, y):
                return self.op(x, self.axis, self.keep_dims, out=y), y

            def forward(self, x):
                return self.op(x, self.axis, self.keep_dims)


        model_cls = aten_amin_amax(op, axis, keep_dims, out)

        return model_cls, f"aten::{op_type}"

    @pytest.mark.parametrize("op_type", ["amin", "amax"])
    @pytest.mark.parametrize("axis", [0, -1, 1, [1, 2], [-1, -2], [2, 0, -1], [0, 1, 2, 3]])
    @pytest.mark.parametrize("keep_dims", [True, False])
    @pytest.mark.parametrize("out", [skip_if_export(True), False])
    @pytest.mark.parametrize("input_dtype", ['float32', 'int32', 'int64', 'float64'])
    @pytest.mark.precommit
    @pytest.mark.precommit_torch_export
    @pytest.mark.precommit_fx_backend
    def test_amin_amax(self, op_type, input_dtype, axis, keep_dims, out, ie_device, precision, ir_version):
        self._test(*self.create_model(op_type, axis, keep_dims, out),
                   ie_device, precision, ir_version, kwargs_to_prepare_input=
                   {"input_dtype": input_dtype, "out": out, "axes": axis, "keep_dims": keep_dims},
                   fx_kind=f"aten.{op_type}")
