codingutf8 2021 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 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 make the decoder inputs a different shape from the encoder inputs to harden the test make sure eos token is set to none to prevent early stopping of generation bert does not have a bos token id so use padtokenid instead make sure that cross attention layers are added make sure that cross attention layers are added can t save full model for now because speech2textmodel speech2textencoder can t save full model for now because speech2textmodel speech2textencoder all published pretrained models are speech2textmodel speech2textencoder make sure that cross attention layers are added disable cache for now there are no published pretrained speech2text2forcausallm for now coding utf 8 2021 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 make the decoder inputs a different shape from the encoder inputs to harden the test make sure eos token is set to none to prevent early stopping of generation bert does not have a bos token id so use pad_token_id instead make sure that cross attention layers are added make sure that cross attention layers are added can t save full model for now because speech2textmodel speech2textencoder can t save full model for now because speech2textmodel speech2textencoder all published pretrained models are speech2textmodel speech2textencoder make sure that cross attention layers are added disable cache for now there are no published pretrained speech2text2forcausallm for now
import tempfile
import unittest

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

from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_bert import BertModelTester
from ..speech_to_text.test_modeling_speech_to_text import Speech2TextModelTester
from ..speech_to_text_2.test_modeling_speech_to_text_2 import Speech2Text2StandaloneDecoderModelTester
from ..wav2vec2.test_modeling_wav2vec2 import Wav2Vec2ModelTester


if is_torch_available():
    import numpy as np
    import torch

    from transformers import (
        BertLMHeadModel,
        Speech2Text2ForCausalLM,
        SpeechEncoderDecoderConfig,
        SpeechEncoderDecoderModel,
        Wav2Vec2Model,
    )
    from transformers.modeling_outputs import BaseModelOutput
    from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextEncoder


@require_torch
class EncoderDecoderMixin:
    def get_encoder_decoder_model(self, config, decoder_config):
        pass

    def prepare_config_and_inputs(self):
        pass

    def get_pretrained_model_and_inputs(self):
        pass

    def check_encoder_decoder_model_from_pretrained_configs(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
        self.assertTrue(encoder_decoder_config.decoder.is_decoder)

        enc_dec_model = SpeechEncoderDecoderModel(encoder_decoder_config)
        enc_dec_model.to(torch_device)
        enc_dec_model.eval()

        self.assertTrue(enc_dec_model.config.is_encoder_decoder)
        self.assertFalse(enc_dec_model.config.tie_word_embeddings)

        outputs_encoder_decoder = enc_dec_model(
            input_values=input_values,
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )

    def check_encoder_decoder_model(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        self.assertTrue(enc_dec_model.config.decoder.is_decoder)
        self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
        self.assertTrue(enc_dec_model.config.is_encoder_decoder)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_values=input_values,
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            output_hidden_states=True,
        )
        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        encoder_outputs = BaseModelOutput(last_hidden_state=outputs_encoder_decoder.encoder_hidden_states[-1])
        outputs_encoder_decoder = enc_dec_model(
            encoder_outputs=encoder_outputs,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )

    def check_encoder_decoder_model_with_inputs(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        inputs = input_values if input_features is None else input_features
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)

        outputs_encoder_decoder = enc_dec_model(
            inputs,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            output_hidden_states=True,
        )
        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        outputs_encoder_decoder_kwarg = enc_dec_model(
            inputs=inputs,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            output_hidden_states=True,
        )
        self.assertEqual(
            outputs_encoder_decoder_kwarg["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )

    def check_encoder_decoder_model_from_pretrained(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        return_dict,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
        enc_dec_model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_values=input_values,
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            output_hidden_states=True,
            return_dict=True,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )

    def check_save_and_load(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        enc_dec_model.eval()
        with torch.no_grad():
            outputs = enc_dec_model(
                input_values=input_values,
                input_features=input_features,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )
            out_2 = outputs[0].cpu().numpy()
            out_2[np.isnan(out_2)] = 0

            with tempfile.TemporaryDirectory() as tmpdirname:
                enc_dec_model.save_pretrained(tmpdirname)
                enc_dec_model = SpeechEncoderDecoderModel.from_pretrained(tmpdirname)
                enc_dec_model.to(torch_device)

                after_outputs = enc_dec_model(
                    input_values=input_values,
                    input_features=input_features,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                    decoder_attention_mask=decoder_attention_mask,
                )
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)

    def check_save_and_load_encoder_decoder_model(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        enc_dec_model.eval()
        with torch.no_grad():
            outputs = enc_dec_model(
                input_values=input_values,
                input_features=input_features,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )
            out_2 = outputs[0].cpu().numpy()
            out_2[np.isnan(out_2)] = 0

            with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
                enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
                enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
                SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
                    encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
                    decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
                )

                after_outputs = enc_dec_model(
                    input_values=input_values,
                    input_features=input_features,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                    decoder_attention_mask=decoder_attention_mask,
                )
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)

    def check_encoder_decoder_model_output_attentions(
        self,
        config,
        attention_mask,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        labels=None,
        input_values=None,
        input_features=None,
        **kwargs,
    ):
        
        decoder_input_ids = decoder_input_ids[:, :-1]
        decoder_attention_mask = decoder_attention_mask[:, :-1]
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_values=input_values,
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            output_attentions=True,
        )

        inputs = input_values if input_features is None else input_features

        encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
        self.assertEqual(len(encoder_attentions), config.num_hidden_layers)

        seq_len = enc_dec_model.encoder._get_feat_extract_output_lengths(inputs.shape[1])
        self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len))

        decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
        num_decoder_layers = (
            decoder_config.num_decoder_layers
            if hasattr(decoder_config, "num_decoder_layers")
            else decoder_config.num_hidden_layers
        )
        self.assertEqual(len(decoder_attentions), num_decoder_layers)

        self.assertEqual(
            decoder_attentions[0].shape[-3:],
            (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
        )

        cross_attentions = outputs_encoder_decoder["cross_attentions"]
        self.assertEqual(len(cross_attentions), num_decoder_layers)

        cross_attention_input_seq_len = decoder_input_ids.shape[-1]
        self.assertEqual(
            cross_attentions[0].shape[-3:],
            (decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len),
        )

    def check_encoder_decoder_model_generate(
        self, config, decoder_config, input_values=None, input_features=None, **kwargs
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)

        
        if hasattr(enc_dec_model.config, "eos_token_id"):
            enc_dec_model.config.eos_token_id = None
        if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"):
            enc_dec_model.config.decoder.eos_token_id = None

        inputs = input_values if input_features is None else input_features

        
        generated_output = enc_dec_model.generate(
            inputs, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
        )
        self.assertEqual(generated_output.shape, (inputs.shape[0],) + (decoder_config.max_length,))

    def test_encoder_decoder_model(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model(**input_ids_dict)

    def test_encoder_decoder_model_with_inputs(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_with_inputs(**input_ids_dict)

    def test_encoder_decoder_model_from_pretrained_configs(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)

    def test_encoder_decoder_model_from_pretrained(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=False)

    def test_encoder_decoder_model_from_pretrained_return_dict(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=True)

    def test_save_and_load_from_pretrained(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_save_and_load(**input_ids_dict)

    def test_save_and_load_from_encoder_decoder_pretrained(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_save_and_load_encoder_decoder_model(**input_ids_dict)

    def test_encoder_decoder_model_output_attentions(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_output_attentions(**input_ids_dict)

    def test_encoder_decoder_model_generate(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_generate(**input_ids_dict)

    def test_training_gradient_checkpointing(self):
        inputs_dict = self.prepare_config_and_inputs()
        encoder_model, decoder_model = self.get_encoder_decoder_model(
            inputs_dict["config"], inputs_dict["decoder_config"]
        )

        model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        model.to(torch_device)
        model.train()
        model.gradient_checkpointing_enable()
        model.config.decoder_start_token_id = 0
        model.config.pad_token_id = 0

        model_inputs = {
            "attention_mask": inputs_dict["attention_mask"],
            "labels": inputs_dict["labels"],
            "decoder_input_ids": inputs_dict["decoder_input_ids"],
        }
        inputs = inputs_dict["input_features"] if "input_features" in inputs_dict else inputs_dict["input_values"]

        loss = model(inputs, **model_inputs).loss
        loss.backward()

    @slow
    def test_real_model_save_load_from_pretrained(self):
        model_2, inputs = self.get_pretrained_model_and_inputs()
        model_2.to(torch_device)

        with torch.no_grad():
            outputs = model_2(**inputs)
            out_2 = outputs[0].cpu().numpy()
            out_2[np.isnan(out_2)] = 0

            with tempfile.TemporaryDirectory() as tmp_dirname:
                model_2.save_pretrained(tmp_dirname)
                model_1 = SpeechEncoderDecoderModel.from_pretrained(tmp_dirname)
                model_1.to(torch_device)

                after_outputs = model_1(**inputs)
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)


@require_torch
class Wav2Vec2BertModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_pretrained_model_and_inputs(self):
        model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
            "facebook/wav2vec2-base-960h", "bert-base-cased"
        )
        batch_size = 13
        input_values = floats_tensor([batch_size, 512], scale=1.0)
        attention_mask = random_attention_mask([batch_size, 512])
        decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
        decoder_attention_mask = random_attention_mask([batch_size, 4])
        inputs = {
            "input_values": input_values,
            "attention_mask": attention_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
        }

        return model, inputs

    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = Wav2Vec2Model(config).eval()
        decoder_model = BertLMHeadModel(decoder_config).eval()
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        bert_model_tester = BertModelTester(self)
        wav2vec2_model_tester = Wav2Vec2ModelTester(self)
        encoder_config_and_inputs = wav2vec2_model_tester.prepare_config_and_inputs()
        decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_values,
            input_mask,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_token_type_ids,
            decoder_input_mask,
            decoder_sequence_labels,
            decoder_token_labels,
            decoder_choice_labels,
            encoder_attention_mask,
            _,
        ) = decoder_config_and_inputs

        
        decoder_config.add_cross_attention = True
        return {
            "config": config,
            "input_values": input_values,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_token_type_ids": decoder_token_type_ids,
            "decoder_attention_mask": decoder_input_mask,
            "decoder_sequence_labels": decoder_sequence_labels,
            "decoder_token_labels": decoder_token_labels,
            "decoder_choice_labels": decoder_choice_labels,
            "labels": decoder_token_labels,
        }


@require_torch
class Speech2TextBertModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_pretrained_model_and_inputs(self):
        model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
            "facebook/s2t-small-librispeech-asr", "bert-base-cased"
        )
        batch_size = 13
        input_features = floats_tensor([batch_size, 7, 80], scale=1.0)
        attention_mask = random_attention_mask([batch_size, 7])
        decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
        decoder_attention_mask = random_attention_mask([batch_size, 4])
        inputs = {
            "input_features": input_features,
            "attention_mask": attention_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
        }

        return model, inputs

    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = Speech2TextEncoder(config).eval()
        decoder_model = BertLMHeadModel(decoder_config).eval()
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        bert_model_tester = BertModelTester(self)
        speech2text_model_tester = Speech2TextModelTester(self)
        encoder_config_and_inputs = speech2text_model_tester.prepare_config_and_inputs()
        decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()

        config, inputs = encoder_config_and_inputs
        input_features = inputs["input_features"]
        input_mask = inputs["attention_mask"]

        (
            decoder_config,
            decoder_input_ids,
            decoder_token_type_ids,
            decoder_input_mask,
            decoder_sequence_labels,
            decoder_token_labels,
            decoder_choice_labels,
            encoder_attention_mask,
            _,
        ) = decoder_config_and_inputs

        
        decoder_config.add_cross_attention = True
        return {
            "config": config,
            "input_features": input_features,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_token_type_ids": decoder_token_type_ids,
            "decoder_attention_mask": decoder_input_mask,
            "decoder_sequence_labels": decoder_sequence_labels,
            "decoder_token_labels": decoder_token_labels,
            "decoder_choice_labels": decoder_choice_labels,
            "labels": decoder_token_labels,
        }

    
    def test_encoder_decoder_model_from_pretrained_configs(self):
        pass

    
    def test_save_and_load_from_pretrained(self):
        pass

    
    def test_real_model_save_load_from_pretrained(self):
        pass


@require_torch
class Wav2Vec2Speech2Text2(EncoderDecoderMixin, unittest.TestCase):
    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = Wav2Vec2Model(config).eval()
        decoder_model = Speech2Text2ForCausalLM(decoder_config).eval()
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester_encoder = Wav2Vec2ModelTester(self, batch_size=13)
        model_tester_decoder = Speech2Text2StandaloneDecoderModelTester(
            self, batch_size=13, d_model=32, max_position_embeddings=512
        )
        encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs()
        (
            config,
            input_values,
            input_mask,
        ) = encoder_config_and_inputs
        (decoder_config, decoder_input_ids, decoder_attention_mask, _) = decoder_config_and_inputs

        
        decoder_config.add_cross_attention = True
        
        decoder_config.use_cache = False
        return {
            "config": config,
            "input_values": input_values,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
            "labels": decoder_input_ids,
        }

    
    def test_real_model_save_load_from_pretrained(self):
        pass