/**
 * @license
 * Copyright 2020 Google LLC. 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.
 * =============================================================================
 */
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
import { backend } from '../index';
describeWithFlags('min', ALL_ENVS, () => {
    it('Tensor1D', async () => {
        const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
        expectArraysClose(await tf.min(a).data(), -7);
    });
    it('return NaNs', async () => {
        const a = tf.tensor1d([3, NaN, 2]);
        expectArraysClose(await tf.min(a).data(), NaN);
    });
    it('2D', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        expectArraysClose(await tf.min(a).data(), -7);
    });
    it('2D axis=[0,1]', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        expectArraysClose(await tf.min(a, [0, 1]).data(), -7);
    });
    it('2D, axis=0', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        const r = tf.min(a, 0);
        expect(r.shape).toEqual([3]);
        expectArraysClose(await r.data(), [3, -7, 0]);
    });
    it('2D, axis=0, keepDims', async () => {
        const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        const r = tf.min(a, 0, true /* keepDims */);
        expect(r.shape).toEqual([1, 3]);
        expectArraysClose(await r.data(), [3, -7, 0]);
    });
    it('2D, axis=1 provided as a number', async () => {
        const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
        const r = tf.min(a, 1);
        expectArraysClose(await r.data(), [2, -7]);
    });
    it('2D, axis = -1 provided as a number', async () => {
        const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
        const r = tf.min(a, -1);
        expectArraysClose(await r.data(), [2, -7]);
    });
    it('2D, axis=[1]', async () => {
        const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
        const r = tf.min(a, [1]);
        expectArraysClose(await r.data(), [2, -7]);
    });
    it('axis permutation does not change input', async () => {
        const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
        const inputDataBefore = await input.data();
        tf.min(input, [1, 0]);
        const inputDataAfter = await input.data();
        expectArraysClose(inputDataBefore, inputDataAfter);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.min({}))
            .toThrowError(/Argument 'x' passed to 'min' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        expectArraysClose(await tf.min([3, -1, 0, 100, -7, 2]).data(), -7);
    });
    it('accpets int32 input', async () => {
        if (backend() && backend().floatPrecision() === 32) {
            // TODO: Use skip() instead when it is implemented
            const a = tf.tensor1d([12345678, 12345679], 'int32');
            expect(a.dtype).toEqual('int32');
            expectArraysClose(await tf.min(a).data(), 12345678);
        }
    });
    it('min gradient: Scalar', async () => {
        const x = tf.scalar(42);
        const dy = tf.scalar(-1);
        const gradients = tf.grad(v => tf.min(v))(x, dy);
        expectArraysClose(await gradients.data(), -1);
    });
    it('gradient with clones', async () => {
        const x = tf.scalar(42);
        const dy = tf.scalar(-1);
        const gradients = tf.grad(v => tf.min(v.clone()).clone())(x, dy);
        expectArraysClose(await gradients.data(), -1);
    });
    it('min gradient: 1D, ties', async () => {
        const x = tf.tensor1d([-1, -3, -7, -7]);
        const dy = tf.scalar(-1);
        const gradients = tf.grad(v => tf.min(v))(x, dy);
        expectArraysClose(await gradients.data(), [0, 0, -1, -1]);
    });
    it('min gradient: 2D, axes=-1, keepDims=false', async () => {
        const x = tf.tensor2d([[-0, -20, -10], [10, 30, 20]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = -1;
        const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('min gradient: ties, 2D, axes=-1, keepDims=false', async () => {
        const x = tf.tensor2d([[0, -20, -20], [10, 30, 10]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = -1;
        const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('min gradient: 2D, axes=0, keepDims=false', async () => {
        const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
        const dy = tf.tensor1d([-1, -1, -1]);
        const axis = 0;
        const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('min gradient: 2D, axes=-1, keepDims=true', async () => {
        const x = tf.tensor2d([[0, -20, -10], [10, 30, 20]]);
        const dy = tf.tensor2d([[-1], [-1]]);
        const axis = -1;
        const keepDims = true;
        const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('min gradient: 2D, axes=0, keepDims=true', async () => {
        const x = tf.tensor2d([[0, -20, -10], [10, 30, -20]]);
        const dy = tf.tensor2d([[-1, -1, -1]]);
        const axis = 0;
        const keepDims = true;
        const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 3]);
    });
    it('max gradient: 3D, axis=1 keepDims=false', async () => {
        const x = tf.ones([2, 1, 250]);
        const axis = 1;
        const gradients = tf.grad(v => tf.min(v, axis))(x);
        expect(gradients.shape).toEqual(x.shape);
    });
    it('min gradient: 3D, axes=[1, 2], keepDims=false', async () => {
        const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = [1, 2];
        const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('min gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {
        const x = tf.tensor3d([[[0, -20], [-20, -20]], [[10, 30], [10, 15]]]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = [1, 2];
        const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('min gradient: 3D, axes=2, keepDims=false', async () => {
        const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
        const dy = tf.tensor2d([[-1, -1], [-1, -1]]);
        const axis = 2;
        const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('min gradient: 3D, axes=2, keepDims=true', async () => {
        const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
        const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
        const axis = 2;
        const keepDims = true;
        const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
        expect(gradients.shape).toEqual([2, 2, 2]);
    });
    it('min gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {
        const x = tf.tensor4d([
            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],
            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]
        ]);
        const dy = tf.tensor1d([-1, -1]);
        const axis = [1, 2, 3];
        const gradients = tf.grad(v => tf.min(v, axis))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);
        expect(gradients.shape).toEqual([2, 2, 2, 2]);
    });
    it('min gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {
        const x = tf.tensor4d([
            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],
            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]
        ]);
        const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
        const axis = [2, 3];
        const keepDims = true;
        const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);
        expectArraysClose(await gradients.data(), [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);
        expect(gradients.shape).toEqual([2, 2, 2, 2]);
    });
    it('throws error for string tensor', () => {
        expect(() => tf.min(['a']))
            .toThrowError(/Argument 'x' passed to 'min' must be numeric tensor/);
    });
});
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\nimport {backend} from '../index';\n\ndescribeWithFlags('min', ALL_ENVS, () => {\n  it('Tensor1D', async () => {\n    const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);\n    expectArraysClose(await tf.min(a).data(), -7);\n  });\n\n  it('return NaNs', async () => {\n    const a = tf.tensor1d([3, NaN, 2]);\n    expectArraysClose(await tf.min(a).data(), NaN);\n  });\n\n  it('2D', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    expectArraysClose(await tf.min(a).data(), -7);\n  });\n\n  it('2D axis=[0,1]', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    expectArraysClose(await tf.min(a, [0, 1]).data(), -7);\n  });\n\n  it('2D, axis=0', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    const r = tf.min(a, 0);\n\n    expect(r.shape).toEqual([3]);\n    expectArraysClose(await r.data(), [3, -7, 0]);\n  });\n\n  it('2D, axis=0, keepDims', async () => {\n    const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    const r = tf.min(a, 0, true /* keepDims */);\n\n    expect(r.shape).toEqual([1, 3]);\n    expectArraysClose(await r.data(), [3, -7, 0]);\n  });\n\n  it('2D, axis=1 provided as a number', async () => {\n    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n    const r = tf.min(a, 1);\n    expectArraysClose(await r.data(), [2, -7]);\n  });\n\n  it('2D, axis = -1 provided as a number', async () => {\n    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n    const r = tf.min(a, -1);\n    expectArraysClose(await r.data(), [2, -7]);\n  });\n\n  it('2D, axis=[1]', async () => {\n    const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);\n    const r = tf.min(a, [1]);\n    expectArraysClose(await r.data(), [2, -7]);\n  });\n\n  it('axis permutation does not change input', async () => {\n    const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);\n    const inputDataBefore = await input.data();\n\n    tf.min(input, [1, 0]);\n\n    const inputDataAfter = await input.data();\n    expectArraysClose(inputDataBefore, inputDataAfter);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.min({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'min' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    expectArraysClose(await tf.min([3, -1, 0, 100, -7, 2]).data(), -7);\n  });\n\n  it('accpets int32 input', async () => {\n    if (backend() && backend().floatPrecision() === 32) {\n      // TODO: Use skip() instead when it is implemented\n      const a = tf.tensor1d([12345678, 12345679], 'int32');\n      expect(a.dtype).toEqual('int32');\n      expectArraysClose(await tf.min(a).data(), 12345678);\n    }\n  });\n\n  it('min gradient: Scalar', async () => {\n    const x = tf.scalar(42);\n    const dy = tf.scalar(-1);\n    const gradients = tf.grad(v => tf.min(v))(x, dy);\n    expectArraysClose(await gradients.data(), -1);\n  });\n\n  it('gradient with clones', async () => {\n    const x = tf.scalar(42);\n    const dy = tf.scalar(-1);\n    const gradients = tf.grad(v => tf.min(v.clone()).clone())(x, dy);\n    expectArraysClose(await gradients.data(), -1);\n  });\n\n  it('min gradient: 1D, ties', async () => {\n    const x = tf.tensor1d([-1, -3, -7, -7]);\n    const dy = tf.scalar(-1);\n    const gradients = tf.grad(v => tf.min(v))(x, dy);\n    expectArraysClose(await gradients.data(), [0, 0, -1, -1]);\n  });\n\n  it('min gradient: 2D, axes=-1, keepDims=false', async () => {\n    const x = tf.tensor2d([[-0, -20, -10], [10, 30, 20]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = -1;\n    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('min gradient: ties, 2D, axes=-1, keepDims=false', async () => {\n    const x = tf.tensor2d([[0, -20, -20], [10, 30, 10]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = -1;\n    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('min gradient: 2D, axes=0, keepDims=false', async () => {\n    const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);\n    const dy = tf.tensor1d([-1, -1, -1]);\n    const axis = 0;\n    const gradients = tf.grad(v => tf.max(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('min gradient: 2D, axes=-1, keepDims=true', async () => {\n    const x = tf.tensor2d([[0, -20, -10], [10, 30, 20]]);\n    const dy = tf.tensor2d([[-1], [-1]]);\n    const axis = -1;\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('min gradient: 2D, axes=0, keepDims=true', async () => {\n    const x = tf.tensor2d([[0, -20, -10], [10, 30, -20]]);\n    const dy = tf.tensor2d([[-1, -1, -1]]);\n    const axis = 0;\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);\n    expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 3]);\n  });\n\n  it('max gradient: 3D, axis=1 keepDims=false', async () => {\n    const x = tf.ones([2, 1, 250]);\n    const axis = 1;\n    const gradients = tf.grad(v => tf.min(v, axis))(x);\n    expect(gradients.shape).toEqual(x.shape);\n  });\n\n  it('min gradient: 3D, axes=[1, 2], keepDims=false', async () => {\n    const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = [1, 2];\n    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('min gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {\n    const x = tf.tensor3d([[[0, -20], [-20, -20]], [[10, 30], [10, 15]]]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = [1, 2];\n    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('min gradient: 3D, axes=2, keepDims=false', async () => {\n    const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);\n    const dy = tf.tensor2d([[-1, -1], [-1, -1]]);\n    const axis = 2;\n    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('min gradient: 3D, axes=2, keepDims=true', async () => {\n    const x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);\n    const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);\n    const axis = 2;\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);\n    expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);\n    expect(gradients.shape).toEqual([2, 2, 2]);\n  });\n\n  it('min gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {\n    const x = tf.tensor4d([\n      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],\n      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]\n    ]);\n    const dy = tf.tensor1d([-1, -1]);\n    const axis = [1, 2, 3];\n    const gradients = tf.grad(v => tf.min(v, axis))(x, dy);\n    expectArraysClose(\n        await gradients.data(),\n        [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);\n    expect(gradients.shape).toEqual([2, 2, 2, 2]);\n  });\n\n  it('min gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {\n    const x = tf.tensor4d([\n      [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],\n      [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]\n    ]);\n    const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);\n    const axis = [2, 3];\n    const keepDims = true;\n    const gradients = tf.grad(v => tf.min(v, axis, keepDims))(x, dy);\n    expectArraysClose(\n        await gradients.data(),\n        [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);\n    expect(gradients.shape).toEqual([2, 2, 2, 2]);\n  });\n\n  it('throws error for string tensor', () => {\n    expect(() => tf.min(['a']))\n        .toThrowError(/Argument 'x' passed to 'min' must be numeric tensor/);\n  });\n});\n"]}