import unittest
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


@unittest.skip("skip TestClampMax now")
class TestClampmax(TestCase):

    def npu_op_exec(self, input1, max_val):
        output = torch.clamp_max(input1, max_val)
        output = output.cpu().numpy()
        return output

    def cpu_op_exec(self, input1, max_val):
        output = torch.clamp_max(input1, max_val)
        output = output.numpy()
        return output

    def npu_inp_op_exec(self, input1, max_val):
        torch.clamp_max_(input1, max_val)
        output = input1.cpu().numpy()
        return output

    def cpu_inp_op_exec(self, input1, max_val):
        output = torch.clamp_max_(input1, max_val)
        output = output.numpy()
        return output

    def npu_op_exec_out(self, input1, max_val, out_npu):
        torch.clamp_max(input1, max_val, out=out_npu)
        output = out_npu.cpu().numpy()
        return output

    def cpu_op_exec_out(self, input1, max_val, out_cpu):
        torch.clamp_max(input1, max_val, out=out_cpu)
        output = out_cpu.numpy()
        return output

    def npu_inp_uncon_op_exec(self, input1, max_val):
        input1 = input1.as_strided([2, 2], [1, 2], 2)
        torch.clamp_max_(input1, max_val)
        output = input1.cpu().numpy()
        return output

    def cpu_inp_uncon_op_exec(self, input1, max_val):
        input_dtype = input1.dtype
        if input_dtype == torch.float16:
            input1 = input1.to(torch.float32)
        input1 = input1.as_strided([2, 2], [1, 2], 2)
        output = torch.clamp_max(input1, max_val)
        if input_dtype == torch.float16:
            output = output.to(torch.float16)
        output = output.numpy()
        return output

    def test_clamp_max_common(self):
        shape_format = [
            [np.float32, 0, (4, 3)],
            [np.int32, 0, (4, 3)],
            [np.int64, 0, (4, 3)],
            [np.float16, 0, (4, 3)]
        ]
        for item in shape_format:
            input_cpu, input_npu = create_common_tensor(item, 1, 100)
            _, out_npu = create_common_tensor(item, 1, 100)

            cpu_output = self.cpu_op_exec(input_cpu, 50)
            npu_output = self.npu_op_exec(input_npu, 50)

            cpu_inp_output = self.cpu_inp_op_exec(input_cpu, 50)
            npu_inp_output = self.npu_inp_op_exec(input_npu, 50)

            npu_out_output = self.npu_op_exec_out(input_npu, 50, out_npu)

            cpu_inp_uncon_output = self.cpu_inp_uncon_op_exec(input_cpu, 50)
            npu_inp_uncon_output = self.npu_inp_uncon_op_exec(input_npu, 50)

            self.assertRtolEqual(cpu_output, npu_output)
            self.assertRtolEqual(cpu_inp_output, npu_inp_output)
            self.assertRtolEqual(cpu_output, npu_out_output)
            self.assertRtolEqual(cpu_inp_uncon_output, npu_inp_uncon_output)

    def test_clamp_max_tensor(self):
        shape_format = [
            [[np.float32, 0, (4, 3)], [np.float32, 0, (4, 3)]],
            [[np.int32, 0, (24, 13)], [np.int32, 0, (1, 13)]],
            [[np.int64, 0, (41, 32, 23)], [np.int32, 0, (41, 32, 23)]],
            [[np.float16, 0, (14, 3)], [np.float32, 0, (14, 3)]],
            [[np.int32, 0, (14, 3)], [np.float32, 0, (14, 3)]],
        ]
        for item in shape_format:
            input_cpu, input_npu = create_common_tensor(item[0], 1, 100)
            max_cpu, max_npu = create_common_tensor(item[1], 1, 50)
            out_cpu, out_npu = create_common_tensor(item[0], 1, 100)

            cpu_output = self.cpu_op_exec(input_cpu, max_cpu)
            npu_output = self.npu_op_exec(input_npu, max_npu)
            self.assertRtolEqual(cpu_output, npu_output)

            if torch.can_cast(max_npu.dtype, input_npu.dtype):
                cpu_inp_output = self.cpu_inp_op_exec(input_cpu, max_cpu)
                npu_inp_output = self.npu_inp_op_exec(input_npu, max_npu)
                self.assertRtolEqual(cpu_inp_output, npu_inp_output)

                npu_out_output = self.npu_op_exec_out(input_npu, max_npu, out_npu)
                cpu_out_output = self.cpu_op_exec_out(input_cpu, max_cpu, out_cpu)
                self.assertRtolEqual(cpu_out_output, npu_out_output)
            else:
                with self.assertRaises(RuntimeError) as cpu_err:
                    self.cpu_inp_op_exec(input_cpu, max_cpu)
                self.assertTrue("can't be cast to the desired output" in str(cpu_err.exception))
                with self.assertRaises(RuntimeError) as npu_err:
                    self.npu_inp_op_exec(input_npu, max_npu)
                self.assertTrue("can't be cast to the desired output" in str(npu_err.exception))
                with self.assertRaises(RuntimeError) as cpu_err:
                    self.cpu_op_exec_out(input_cpu, max_cpu, out_cpu)
                self.assertTrue("can't be cast to the desired output" in str(cpu_err.exception))
                with self.assertRaises(RuntimeError) as npu_err:
                    self.npu_op_exec_out(input_npu, max_npu, out_npu)
                self.assertTrue("can't be cast to the desired output" in str(npu_err.exception))


if __name__ == "__main__":
    run_tests()