codingutf8 2020 the huggingface inc team the microsoft research 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 orglicenseslicense2 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 encoderdecoder outputs encoder outputs decoder outputs compare the actual values for a slice coding utf 8 2020 the huggingface inc team the microsoft research 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 encoder decoder outputs encoder outputs decoder outputs compare the actual values for a slice
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
    import torch

    from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer


@require_torch
class XLMProphetNetModelIntegrationTest(unittest.TestCase):
    @slow
    def test_pretrained_checkpoint_hidden_states(self):
        model = XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased")
        model.to(torch_device)

        
        encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
        decoder_prev_ids = torch.tensor(
            [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]
        ).to(torch_device)
        output = model(
            input_ids=encoder_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=decoder_prev_ids
        )
        output_predited_logis = output[0]
        expected_shape = torch.Size((1, 14, 250012))
        self.assertEqual(output_predited_logis.shape, expected_shape)
        expected_slice = torch.tensor(
            [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3], expected_slice, atol=1e-4))

        
        encoder_outputs = model.prophetnet.encoder(encoder_ids)[0]
        expected_encoder_outputs_slice = torch.tensor(
            [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]
        ).to(torch_device)
        expected_shape_encoder = torch.Size((1, 4, 1024))
        self.assertEqual(encoder_outputs.shape, expected_shape_encoder)
        self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4))

        
        decoder_outputs = model.prophetnet.decoder(
            decoder_prev_ids,
            encoder_hidden_states=encoder_outputs,
        )
        predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 14, -1)
        predicting_streams_logits = model.lm_head(predicting_streams)
        next_first_stream_logits = predicting_streams_logits[:, 0]
        self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4))

    @slow
    def test_ntg_hidden_states(self):
        model = XLMProphetNetForConditionalGeneration.from_pretrained(
            "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"
        )
        model.to(torch_device)

        encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
        decoder_prev_ids = torch.tensor(
            [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]
        ).to(torch_device)
        output = model(
            input_ids=encoder_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=decoder_prev_ids
        )
        output_predited_logis = output[0]
        expected_shape = torch.Size((1, 14, 250012))
        self.assertEqual(output_predited_logis.shape, expected_shape)
        
        expected_slice = torch.tensor(
            [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]
        ).to(torch_device)

        self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3], expected_slice, atol=1e-4))

    @slow
    def test_xprophetnet_ntg_inference(self):
        model = XLMProphetNetForConditionalGeneration.from_pretrained(
            "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"
        )
        model.to(torch_device)
        model.config.max_length = 512

        tokenizer = XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")

        EN_SENTENCE = (
            "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
            " January 14, 2020, according to the official portal of the organization. From that day, users of this"
            " system will not be able to receive security updates, which could make their computers vulnerable to"
            " cyber attacks."
        )
        RU_SENTENCE = (
            "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
            " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
            " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
            " к кибератакам."
        )
        ZH_SENTENCE = "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"

        input_ids = tokenizer(
            [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE], padding=True, max_length=255, return_tensors="pt"
        ).input_ids
        input_ids = input_ids.to(torch_device)

        summary_ids = model.generate(
            input_ids, num_beams=10, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
        )
        generated_titles = [tokenizer.decode(g, skip_special_tokens=True) for g in summary_ids]
        EXPECTED_TITLE_EN = "Microsoft to end Windows 7 free support after January 14, 2020"
        EXPECTED_TITLE_RU = "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
        EXPECTED_TITLE_ZH = "微软打算终止对Windows 7操作系统的免费支持"
        self.assertListEqual(
            [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH],
            generated_titles,
        )

        summary_ids_beam1 = model.generate(
            input_ids, num_beams=1, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
        )
        generated_titles_beam1_tok = [
            tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True) for g in summary_ids_beam1
        ]
        EXPECTED_TITLE_EN_BEAM1_TOK = "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" ")
        EXPECTED_TITLE_RU_BEAM1_TOK = "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
            " "
        )
        EXPECTED_TITLE_ZH_BEAM1_TOK = "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" ")
        self.assertListEqual(
            [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK],
            generated_titles_beam1_tok,
        )