transformers/tests/utils/test_generic.py

# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
    import jax.numpy as jnp

if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


class GenericTester(unittest.TestCase):
    def test_flatten_dict(self):
        input_dict = {
            "task_specific_params": {
                "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
                "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
                "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
            }
        }
        expected_dict = {
            "task_specific_params.summarization.length_penalty": 1.0,
            "task_specific_params.summarization.max_length": 128,
            "task_specific_params.summarization.min_length": 12,
            "task_specific_params.summarization.num_beams": 4,
            "task_specific_params.summarization_cnn.length_penalty": 2.0,
            "task_specific_params.summarization_cnn.max_length": 142,
            "task_specific_params.summarization_cnn.min_length": 56,
            "task_specific_params.summarization_cnn.num_beams": 4,
            "task_specific_params.summarization_xsum.length_penalty": 1.0,
            "task_specific_params.summarization_xsum.max_length": 62,
            "task_specific_params.summarization_xsum.min_length": 11,
            "task_specific_params.summarization_xsum.num_beams": 6,
        }

        self.assertEqual(flatten_dict(input_dict), expected_dict)

    def test_transpose_numpy(self):
        x = np.random.randn(3, 4)
        self.assertTrue(np.allclose(transpose(x), x.transpose()))

        x = np.random.randn(3, 4, 5)
        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), x.transpose((1, 2, 0))))

    @require_torch
    def test_transpose_torch(self):
        x = np.random.randn(3, 4)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(transpose(x), transpose(t).numpy()))

        x = np.random.randn(3, 4, 5)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), transpose(t, axes=(1, 2, 0)).numpy()))

    @require_tf
    def test_transpose_tf(self):
        x = np.random.randn(3, 4)
        t = tf.constant(x)
        self.assertTrue(np.allclose(transpose(x), transpose(t).numpy()))

        x = np.random.randn(3, 4, 5)
        t = tf.constant(x)
        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), transpose(t, axes=(1, 2, 0)).numpy()))

    @require_flax
    def test_transpose_flax(self):
        x = np.random.randn(3, 4)
        t = jnp.array(x)
        self.assertTrue(np.allclose(transpose(x), np.asarray(transpose(t))))

        x = np.random.randn(3, 4, 5)
        t = jnp.array(x)
        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), np.asarray(transpose(t, axes=(1, 2, 0)))))

    def test_reshape_numpy(self):
        x = np.random.randn(3, 4)
        self.assertTrue(np.allclose(reshape(x, (4, 3)), np.reshape(x, (4, 3))))

        x = np.random.randn(3, 4, 5)
        self.assertTrue(np.allclose(reshape(x, (12, 5)), np.reshape(x, (12, 5))))

    @require_torch
    def test_reshape_torch(self):
        x = np.random.randn(3, 4)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(reshape(x, (4, 3)), reshape(t, (4, 3)).numpy()))

        x = np.random.randn(3, 4, 5)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(reshape(x, (12, 5)), reshape(t, (12, 5)).numpy()))

    @require_tf
    def test_reshape_tf(self):
        x = np.random.randn(3, 4)
        t = tf.constant(x)
        self.assertTrue(np.allclose(reshape(x, (4, 3)), reshape(t, (4, 3)).numpy()))

        x = np.random.randn(3, 4, 5)
        t = tf.constant(x)
        self.assertTrue(np.allclose(reshape(x, (12, 5)), reshape(t, (12, 5)).numpy()))

    @require_flax
    def test_reshape_flax(self):
        x = np.random.randn(3, 4)
        t = jnp.array(x)
        self.assertTrue(np.allclose(reshape(x, (4, 3)), np.asarray(reshape(t, (4, 3)))))

        x = np.random.randn(3, 4, 5)
        t = jnp.array(x)
        self.assertTrue(np.allclose(reshape(x, (12, 5)), np.asarray(reshape(t, (12, 5)))))

    def test_squeeze_numpy(self):
        x = np.random.randn(1, 3, 4)
        self.assertTrue(np.allclose(squeeze(x), np.squeeze(x)))

        x = np.random.randn(1, 4, 1, 5)
        self.assertTrue(np.allclose(squeeze(x, axis=2), np.squeeze(x, axis=2)))

    @require_torch
    def test_squeeze_torch(self):
        x = np.random.randn(1, 3, 4)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(squeeze(x), squeeze(t).numpy()))

        x = np.random.randn(1, 4, 1, 5)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(squeeze(x, axis=2), squeeze(t, axis=2).numpy()))

    @require_tf
    def test_squeeze_tf(self):
        x = np.random.randn(1, 3, 4)
        t = tf.constant(x)
        self.assertTrue(np.allclose(squeeze(x), squeeze(t).numpy()))

        x = np.random.randn(1, 4, 1, 5)
        t = tf.constant(x)
        self.assertTrue(np.allclose(squeeze(x, axis=2), squeeze(t, axis=2).numpy()))

    @require_flax
    def test_squeeze_flax(self):
        x = np.random.randn(1, 3, 4)
        t = jnp.array(x)
        self.assertTrue(np.allclose(squeeze(x), np.asarray(squeeze(t))))

        x = np.random.randn(1, 4, 1, 5)
        t = jnp.array(x)
        self.assertTrue(np.allclose(squeeze(x, axis=2), np.asarray(squeeze(t, axis=2))))

    def test_expand_dims_numpy(self):
        x = np.random.randn(3, 4)
        self.assertTrue(np.allclose(expand_dims(x, axis=1), np.expand_dims(x, axis=1)))

    @require_torch
    def test_expand_dims_torch(self):
        x = np.random.randn(3, 4)
        t = torch.tensor(x)
        self.assertTrue(np.allclose(expand_dims(x, axis=1), expand_dims(t, axis=1).numpy()))

    @require_tf
    def test_expand_dims_tf(self):
        x = np.random.randn(3, 4)
        t = tf.constant(x)
        self.assertTrue(np.allclose(expand_dims(x, axis=1), expand_dims(t, axis=1).numpy()))

    @require_flax
    def test_expand_dims_flax(self):
        x = np.random.randn(3, 4)
        t = jnp.array(x)
        self.assertTrue(np.allclose(expand_dims(x, axis=1), np.asarray(expand_dims(t, axis=1))))