2021 the huggingface 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 the slow tests are often failing with oom error on gpu this makes jax allocate exactly what is needed on demand and deallocate memory that is no longer needed but will be slower as stated here https jax readthedocs ioenlatestgpumemoryallocation html timeoutdecorator timeout1 not working with the decorator so far flaxblenderbotforsequenceclassification expects eos token in inputids 2021 the huggingface 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 noqa the slow tests are often failing with oom error on gpu this makes jax allocate exactly what is needed on demand and deallocate memory that is no longer needed but will be slower as stated here https jax readthedocs io en latest gpu_memory_allocation html note padding timeout_decorator timeout 1 not working with the decorator so far flaxblenderbotforsequenceclassification expects eos token in input_ids
import unittest

import numpy as np
import timeout_decorator  

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    
    
    
    os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )


def prepare_blenderbot_inputs_dict(
    config,
    input_ids,
    decoder_input_ids=None,
    attention_mask=None,
    decoder_attention_mask=None,
    head_mask=None,
    decoder_head_mask=None,
    cross_attn_head_mask=None,
):
    if attention_mask is None:
        attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
    if decoder_attention_mask is None:
        decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
    if head_mask is None:
        head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
    if decoder_head_mask is None:
        decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
    if cross_attn_head_mask is None:
        cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }


class FlaxBlenderbotModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=50,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        initializer_range=0.02,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.initializer_range = initializer_range

    def prepare_config_and_inputs(self):
        input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
        input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)

        decoder_input_ids = shift_tokens_right(input_ids, 1, 2)

        config = BlenderbotConfig(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            encoder_layers=self.num_hidden_layers,
            decoder_layers=self.num_hidden_layers,
            encoder_attention_heads=self.num_attention_heads,
            decoder_attention_heads=self.num_attention_heads,
            encoder_ffn_dim=self.intermediate_size,
            decoder_ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            initializer_range=self.initializer_range,
            use_cache=False,
        )
        inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids)
        return config, inputs_dict

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict

    def check_use_cache_forward(self, model_class_name, config, inputs_dict):
        max_decoder_length = 20
        model = model_class_name(config)

        encoder_outputs = model.encode(inputs_dict["input_ids"])

        decoder_input_ids, decoder_attention_mask = (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
        decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")

        decoder_position_ids = jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
            (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
        )
        outputs_cache = model.decode(
            decoder_input_ids[:, :-1],
            encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            past_key_values=past_key_values,
            decoder_position_ids=decoder_position_ids,
        )

        decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
        outputs_cache_next = model.decode(
            decoder_input_ids[:, -1:],
            encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            past_key_values=outputs_cache.past_key_values,
            decoder_position_ids=decoder_position_ids,
        )

        outputs = model.decode(decoder_input_ids, encoder_outputs)

        diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")

    def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
        max_decoder_length = 20
        model = model_class_name(config)

        encoder_outputs = model.encode(inputs_dict["input_ids"])

        decoder_input_ids, decoder_attention_mask = (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        decoder_attention_mask_cache = jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
            ],
            axis=-1,
        )

        past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
        decoder_position_ids = jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
            (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
        )

        outputs_cache = model.decode(
            decoder_input_ids[:, :-1],
            encoder_outputs,
            decoder_attention_mask=decoder_attention_mask_cache,
            past_key_values=past_key_values,
            decoder_position_ids=decoder_position_ids,
        )
        decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
        outputs_cache_next = model.decode(
            decoder_input_ids[:, -1:],
            encoder_outputs,
            past_key_values=outputs_cache.past_key_values,
            decoder_attention_mask=decoder_attention_mask_cache,
            decoder_position_ids=decoder_position_ids,
        )

        outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)

        diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")


@require_flax
class BlenderbotHeadTests(unittest.TestCase):
    vocab_size = 99

    def _get_config_and_data(self):
        input_ids = np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ],
            dtype=np.int64,
        )

        batch_size = input_ids.shape[0]
        config = BlenderbotConfig(
            vocab_size=self.vocab_size,
            d_model=24,
            encoder_layers=2,
            decoder_layers=2,
            encoder_attention_heads=2,
            decoder_attention_heads=2,
            encoder_ffn_dim=32,
            decoder_ffn_dim=32,
            max_position_embeddings=48,
            eos_token_id=2,
            pad_token_id=1,
            bos_token_id=0,
        )
        return config, input_ids, batch_size

    
    def test_lm_forward(self):
        config, input_ids, batch_size = self._get_config_and_data()
        lm_model = FlaxBlenderbotForConditionalGeneration(config)
        outputs = lm_model(input_ids=input_ids)
        expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape, expected_shape)

    def test_lm_uneven_forward(self):
        config = BlenderbotConfig(
            vocab_size=self.vocab_size,
            d_model=14,
            encoder_layers=2,
            decoder_layers=2,
            encoder_attention_heads=2,
            decoder_attention_heads=2,
            encoder_ffn_dim=8,
            decoder_ffn_dim=8,
            max_position_embeddings=48,
        )
        lm_model = FlaxBlenderbotForConditionalGeneration(config)
        context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
        summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
        outputs = lm_model(input_ids=context, decoder_input_ids=summary)
        expected_shape = (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape, expected_shape)

    def test_shift_tokens_right(self):
        input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
        shifted = shift_tokens_right(input_ids, 1, 2)
        n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
        n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
        self.assertEqual(shifted.shape, input_ids.shape)
        self.assertEqual(n_pad_after, n_pad_before - 1)
        self.assertTrue(np.equal(shifted[:, 0], 2).all())


@require_flax
class FlaxBlenderbotModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
    is_encoder_decoder = True
    all_model_classes = (
        (
            FlaxBlenderbotModel,
            FlaxBlenderbotForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    all_generative_model_classes = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

    def setUp(self):
        self.model_tester = FlaxBlenderbotModelTester(self)

    def test_use_cache_forward(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)

    def test_use_cache_forward_with_attn_mask(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)

    def test_encode(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                model = model_class(config)

                @jax.jit
                def encode_jitted(input_ids, attention_mask=None, **kwargs):
                    return model.encode(input_ids=input_ids, attention_mask=attention_mask)

                with self.subTest("JIT Enabled"):
                    jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = encode_jitted(**prepared_inputs_dict).to_tuple()

                self.assertEqual(len(outputs), len(jitted_outputs))
                for jitted_output, output in zip(jitted_outputs, outputs):
                    self.assertEqual(jitted_output.shape, output.shape)

    def test_decode(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                model = model_class(config)
                encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])

                prepared_inputs_dict = {
                    "decoder_input_ids": inputs_dict["decoder_input_ids"],
                    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                    "encoder_outputs": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
                    return model.decode(
                        decoder_input_ids=decoder_input_ids,
                        decoder_attention_mask=decoder_attention_mask,
                        encoder_outputs=encoder_outputs,
                    )

                with self.subTest("JIT Enabled"):
                    jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = decode_jitted(**prepared_inputs_dict).to_tuple()

                self.assertEqual(len(outputs), len(jitted_outputs))
                for jitted_output, output in zip(jitted_outputs, outputs):
                    self.assertEqual(jitted_output.shape, output.shape)

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            model = model_class_name.from_pretrained("facebook/blenderbot-400M-distill")
            
            input_ids = np.ones((1, 1)) * model.config.eos_token_id
            outputs = model(input_ids)
            self.assertIsNotNone(outputs)

    @unittest.skipUnless(jax_device != "cpu", "3B test too slow on CPU.")
    @slow
    def test_generation_from_short_input_same_as_parlai_3B(self):
        FASTER_GEN_KWARGS = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
        TOK_DECODE_KW = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

        model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B", from_pt=True)
        tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")

        src_text = ["Sam"]
        model_inputs = tokenizer(src_text, return_tensors="jax")

        generated_utterances = model.generate(**model_inputs, **FASTER_GEN_KWARGS)
        tgt_text = 'Sam is a great name. It means "sun" in Gaelic.'

        generated_txt = tokenizer.batch_decode(generated_utterances, **TOK_DECODE_KW)
        assert generated_txt[0].strip() == tgt_text