import torch
import numpy as np
import torch_npu

from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor


class TestFloor(TestCase):
    def cpu_op_exec(self, input1):
        output = torch.floor(input1)
        output = output.numpy()
        return output

    def npu_op_exec(self, input1):
        output = torch.floor(input1)
        output = output.to("cpu")
        output = output.numpy()
        return output

    def cpu_op_inter_exec(self, input1):
        torch.floor_(input1)
        output = input1.numpy()
        return output

    def npu_op_inter_exec(self, input1):
        torch.floor_(input1)
        output = input1.to("cpu")
        output = output.numpy()
        return output

    def cpu_op_out_exec(self, input1, output):
        torch.floor(input1, out=output)
        output = output.numpy()
        return output

    def npu_op_out_exec(self, input1, output):
        torch.floor(input1, out=output)
        output = output.to("cpu")
        output = output.numpy()
        return output

    def test_floor_float32_shape_format(self):
        format_list = [0, 3]
        shape_list = [[256, 1, 1, 1], [1024, 32, 7, 7], [1024, 32, 7], [1024, 32], [1024]]
        shape_format = [
            [np.float32, i, j] for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, 1, 100)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_exec(npu_input)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_floor_inter_float32_shape_format(self):
        format_list = [0, 3]
        shape_list = [[256, 1, 1, 1], [1024, 32, 7, 7], [1024, 32, 7], [1024, 32], [1024]]
        shape_format = [
            [np.float32, i, j] for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, 1, 100)
            cpu_output = self.cpu_op_inter_exec(cpu_input)
            npu_output = self.npu_op_inter_exec(npu_input)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_floor_out_float32_shape_format(self):
        shape_format = [
            [[np.float32, 0, [1024, 32, 7, 7]], [np.float32, 0, [1024, 32, 7, 7]]],
            [[np.float32, 0, [1024, 32, 7]], [np.float32, 0, [1024, 32]]],
            [[np.float32, 0, [1024, 32]], [np.float32, 0, [1024, 32]]],
            [[np.float32, 0, [1024]], [np.float32, 0, [1024, 1]]],
            [[np.float32, 3, [1024, 32, 7, 7]], [np.float32, 3, [1024, 32, 7, 7]]],
            [[np.float32, 3, [1024, 32, 7]], [np.float32, 3, [1024, 32]]],
            [[np.float32, 3, [1024, 32]], [np.float32, 3, [1024, 20]]],
            [[np.float32, 3, [1024]], [np.float32, 3, [1024]]],
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item[0], 1, 100)
            cpu_output, npu_output = create_common_tensor(item[1], 1, 100)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_out_exec(npu_input, npu_output)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_floor_float16_shape_format(self):
        format_list = [0, 3]
        shape_list = [[256, 1, 1, 1], [1024, 32, 7, 7], [1024, 32, 7], [1024, 32], [1024]]
        shape_format = [
            [np.float16, i, j] for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, 1, 100)
            if item[0] == np.float16:
                cpu_input = cpu_input.to(torch.float32)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_exec(npu_input)
            if item[0] == np.float16:
                cpu_output = cpu_output.astype(np.float16)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_floor_inter_float16_shape_format(self):
        format_list = [0, 3]
        shape_list = [[256, 1, 1, 1], [1024, 32, 7, 7], [1024, 32, 7], [1024, 32], [1024]]
        shape_format = [
            [np.float16, i, j] for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, 1, 100)
            if item[0] == np.float16:
                cpu_input = cpu_input.to(torch.float32)
            cpu_output = self.cpu_op_inter_exec(cpu_input)
            npu_output = self.npu_op_inter_exec(npu_input)
            if item[0] == np.float16:
                cpu_output = cpu_output.astype(np.float16)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_floor_out_float16_shape_format(self):
        shape_format = [
            [[np.float16, 0, [1024, 32, 7, 7]], [np.float16, 0, [1024, 32, 7, 7]]],
            [[np.float16, 0, [1024, 32, 7]], [np.float16, 0, [1024, 32]]],
            [[np.float16, 0, [1024, 32]], [np.float16, 0, [1024, 32]]],
            [[np.float16, 0, [1024]], [np.float16, 0, [1024, 1]]],
            [[np.float16, 3, [1024, 32, 7, 7]], [np.float16, 3, [1024, 32, 7, 7]]],
            [[np.float16, 3, [1024, 32, 7]], [np.float16, 3, [1024, 32]]],
            [[np.float16, 3, [1024, 32]], [np.float16, 3, [1024, 20]]],
            [[np.float16, 3, [1024]], [np.float16, 3, [1024]]],
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item[0], 1, 100)
            cpu_output, npu_output = create_common_tensor(item[1], 1, 100)
            if item[0][0] == np.float16:
                cpu_input = cpu_input.to(torch.float32)
                cpu_output = cpu_output.to(torch.float32)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_out_exec(npu_input, npu_output)
            if item[0][0] == np.float16:
                cpu_output = cpu_output.astype(np.float16)
            self.assertRtolEqual(cpu_output, npu_output)


if __name__ == "__main__":
    run_tests()