test_modeling_t5.py

# coding=utf-8
# Copyright 2018 Google T5 Authors and 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 copy
import os
import pickle
import tempfile
import unittest

from transformers import T5Config, is_torch_available
from transformers.models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
from transformers.testing_utils import (
    require_accelerate,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_fx_available

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_fx_available():
    from transformers.utils.fx import symbolic_trace


if is_torch_available():
    import torch

    from transformers import (
        AutoTokenizer,
        ByT5Tokenizer,
        T5EncoderModel,
        T5ForConditionalGeneration,
        T5ForQuestionAnswering,
        T5ForSequenceClassification,
        T5ForTokenClassification,
        T5Model,
        T5Tokenizer,
    )


class T5ModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=13,
        encoder_seq_length=7,
        decoder_seq_length=7,
        # For common tests
        is_training=True,
        use_attention_mask=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        d_ff=37,
        relative_attention_num_buckets=8,
        dropout_rate=0.1,
        initializer_factor=0.002,
        eos_token_id=1,
        pad_token_id=0,
        decoder_start_token_id=0,
        scope=None,
        decoder_layers=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        self.decoder_seq_length = decoder_seq_length
        # For common tests
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        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.d_ff = d_ff
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.dropout_rate = dropout_rate
        self.initializer_factor = initializer_factor
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.scope = None
        self.decoder_layers = decoder_layers

    def get_large_model_config(self):
        return T5Config.from_pretrained("google-t5/t5-base")

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size).clamp(2)
        input_ids[:, -1] = self.eos_token_id  # Eos Token
        decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        attention_mask = None
        decoder_attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
            decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        )

    def get_pipeline_config(self):
        return T5Config(
            vocab_size=166,  # t5 forces 100 extra tokens
            d_model=self.hidden_size,
            d_ff=self.d_ff,
            d_kv=self.hidden_size // self.num_attention_heads,
            num_layers=self.num_hidden_layers,
            num_decoder_layers=self.decoder_layers,
            num_heads=self.num_attention_heads,
            relative_attention_num_buckets=self.relative_attention_num_buckets,
            dropout_rate=self.dropout_rate,
            initializer_factor=self.initializer_factor,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.pad_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
        )

    def get_config(self):
        return T5Config(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            d_ff=self.d_ff,
            d_kv=self.hidden_size // self.num_attention_heads,
            num_layers=self.num_hidden_layers,
            num_decoder_layers=self.decoder_layers,
            num_heads=self.num_attention_heads,
            relative_attention_num_buckets=self.relative_attention_num_buckets,
            dropout_rate=self.dropout_rate,
            initializer_factor=self.initializer_factor,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.pad_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
        )

    def check_prepare_lm_labels_via_shift_left(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5Model(config=config)
        model.to(torch_device)
        model.eval()

        # make sure that lm_labels are correctly padded from the right
        lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)

        # add casaul pad token mask
        triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
        lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
        decoder_input_ids = model._shift_right(lm_labels)

        for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
            # first item
            self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
            if i < decoder_input_ids_slice.shape[-1]:
                if i < decoder_input_ids.shape[-1] - 1:
                    # items before diagonal
                    self.parent.assertListEqual(
                        decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
                    )
                # pad items after diagonal
                if i < decoder_input_ids.shape[-1] - 2:
                    self.parent.assertListEqual(
                        decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
                    )
            else:
                # all items after square
                self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())

    def create_and_check_model(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5Model(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )
        result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
        decoder_output = result.last_hidden_state
        decoder_past = result.past_key_values
        encoder_output = result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
        self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(decoder_past), config.num_layers)
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]), 4)

    def create_and_check_with_lm_head(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
        outputs = model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            labels=lm_labels,
        )
        self.parent.assertEqual(len(outputs), 4)
        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
        self.parent.assertEqual(outputs["loss"].size(), ())

    def create_and_check_with_sequence_classification_head(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device)
        model = T5ForSequenceClassification(config=config).to(torch_device).eval()
        outputs = model(
            input_ids=input_ids,
            decoder_input_ids=input_ids,
            labels=labels,
        )
        # self.parent.assertEqual(len(outputs), 4)
        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels))
        self.parent.assertEqual(outputs["loss"].size(), ())

    def create_and_check_decoder_model_past(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5Model(config=config).get_decoder().to(torch_device).eval()
        # first forward pass
        outputs = model(input_ids, use_cache=True)
        outputs_use_cache_conf = model(input_ids)
        outputs_no_past = model(input_ids, use_cache=False)

        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)

        output, past_key_values = outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)

        output_from_no_past = model(next_input_ids)["last_hidden_state"]
        output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_decoder_model_attention_mask_past(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5Model(config=config).get_decoder()
        model.to(torch_device)
        model.eval()

        # create attention mask
        attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)

        half_seq_length = input_ids.shape[-1] // 2
        attn_mask[:, half_seq_length:] = 0

        # first forward pass
        output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple()

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # change a random masked slice from input_ids
        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
        random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
        input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens

        # append to next input_ids and attn_mask
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        attn_mask = torch.cat(
            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
            dim=1,
        )

        # get two different outputs
        output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
            "last_hidden_state"
        ]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5Model(config=config).get_decoder().to(torch_device).eval()
        # first forward pass
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)

        output, past_key_values = outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([attention_mask, next_mask], dim=-1)

        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
            "last_hidden_state"
        ]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_generate_with_past_key_values(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
        torch.manual_seed(0)
        output_without_past_cache = model.generate(
            input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
        )
        torch.manual_seed(0)
        output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
        self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))

    def create_and_check_model_fp16_forward(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        model = T5Model(config=config).to(torch_device).half().eval()
        output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
        self.parent.assertFalse(torch.isnan(output).any().item())

    def create_and_check_encoder_decoder_shared_weights(
        self,
        config,
        input_ids,
        decoder_input_ids,
        attention_mask,
        decoder_attention_mask,
        lm_labels,
    ):
        for model_class in [T5Model, T5ForConditionalGeneration]:
            torch.manual_seed(0)
            model = model_class(config=config).to(torch_device).eval()
            # load state dict copies weights but does not tie them
            model.encoder.load_state_dict(model.decoder.state_dict(), strict=False)

            torch.manual_seed(0)
            tied_config = copy.deepcopy(config)
            tied_config.tie_encoder_decoder = True
            tied_model = model_class(config=tied_config).to(torch_device).eval()

            model_result = model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )

            tied_model_result = tied_model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )

            # check that models has less parameters
            self.parent.assertLess(
                sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
            )
            random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()

            # check that outputs are equal
            self.parent.assertTrue(
                torch.allclose(
                    model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4
                )
            )

            # check that outputs after saving and loading are equal
            with tempfile.TemporaryDirectory() as tmpdirname:
                tied_model.save_pretrained(tmpdirname)
                tied_model = model_class.from_pretrained(tmpdirname)
                tied_model.to(torch_device)
                tied_model.eval()

                # check that models has less parameters
                self.parent.assertLess(
                    sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
                )
                random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()

                tied_model_result = tied_model(
                    input_ids=input_ids,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                    decoder_attention_mask=decoder_attention_mask,
                )

                # check that outputs are equal
                self.parent.assertTrue(
                    torch.allclose(
                        model_result[0][0, :, random_slice_idx],
                        tied_model_result[0][0, :, random_slice_idx],
                        atol=1e-4,
                    )
                )

    def check_resize_embeddings_t5_v1_1(
        self,
        config,
    ):
        prev_vocab_size = config.vocab_size

        config.tie_word_embeddings = False
        model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
        model.resize_token_embeddings(prev_vocab_size - 10)

        self.parent.assertEqual(model.get_input_embeddings().weight.shape[0], prev_vocab_size - 10)
        self.parent.assertEqual(model.get_output_embeddings().weight.shape[0], prev_vocab_size - 10)
        self.parent.assertEqual(model.config.vocab_size, prev_vocab_size - 10)

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = config_and_inputs

        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
            "use_cache": False,
        }
        return config, inputs_dict


@require_torch
class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (T5Model, T5ForConditionalGeneration, T5ForSequenceClassification, T5ForQuestionAnswering)
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "conversational": T5ForConditionalGeneration,
            "feature-extraction": T5Model,
            "question-answering": T5ForQuestionAnswering,
            "summarization": T5ForConditionalGeneration,
            "text-classification": T5ForSequenceClassification,
            "text2text-generation": T5ForConditionalGeneration,
            "translation": T5ForConditionalGeneration,
            "zero-shot": T5ForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    all_parallelizable_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
    fx_compatible = True
    test_pruning = False
    test_resize_embeddings = True
    test_model_parallel = True
    is_encoder_decoder = True
    # The small T5 model needs higher percentages for CPU/MP tests
    model_split_percents = [0.8, 0.9]

    def setUp(self):
        self.model_tester = T5ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)

    # `QAPipelineTests` is not working well with slow tokenizers (for some models) and we don't want to touch the file
    # `src/transformers/data/processors/squad.py` (where this test fails for this model)
    def is_pipeline_test_to_skip(
        self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if tokenizer_name is None:
            return True
        if pipeline_test_case_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
            return True

        return False

    def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
        if not is_torch_fx_available() or not self.fx_compatible:
            return

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.return_dict = False

        for model_class in self.all_model_classes:
            if model_class.__name__ == "T5ForSequenceClassification":
                continue
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)

            try:
                if model.config.is_encoder_decoder:
                    model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
                    labels = inputs.get("labels", None)
                    input_names = [
                        "attention_mask",
                        "decoder_attention_mask",
                        "decoder_input_ids",
                        "input_features",
                        "input_ids",
                        "input_values",
                    ]
                    if labels is not None:
                        input_names.append("labels")

                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = list(filtered_inputs.keys())

                    model_output = model(**filtered_inputs)

                    traced_model = symbolic_trace(model, input_names)
                    traced_output = traced_model(**filtered_inputs)
                else:
                    input_names = [
                        "attention_mask",
                        "bbox",
                        "input_features",
                        "input_ids",
                        "input_values",
                        "pixel_values",
                        "token_type_ids",
                        "visual_feats",
                        "visual_pos",
                    ]

                    labels = inputs.get("labels", None)
                    start_positions = inputs.get("start_positions", None)
                    end_positions = inputs.get("end_positions", None)
                    if labels is not None:
                        input_names.append("labels")
                    if start_positions is not None:
                        input_names.append("start_positions")
                    if end_positions is not None:
                        input_names.append("end_positions")

                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = list(filtered_inputs.keys())

                    if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and (
                        not hasattr(model.config, "problem_type") or model.config.problem_type is None
                    ):
                        model.config.problem_type = "single_label_classification"

                    traced_model = symbolic_trace(model, input_names)
                    traced_output = traced_model(**filtered_inputs)
                    model_output = model(**filtered_inputs)

            except Exception as e:
                self.fail(f"Couldn't trace module: {e}")

            def flatten_output(output):
                flatten = []
                for x in output:
                    if isinstance(x, (tuple, list)):
                        flatten += flatten_output(x)
                    elif not isinstance(x, torch.Tensor):
                        continue
                    else:
                        flatten.append(x)
                return flatten

            model_output = flatten_output(model_output)
            traced_output = flatten_output(traced_output)
            num_outputs = len(model_output)

            for i in range(num_outputs):
                self.assertTrue(
                    torch.allclose(model_output[i], traced_output[i]),
                    f"traced {i}th output doesn't match model {i}th output for {model_class}",
                )

            # Test that the model can be serialized and restored properly
            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
                try:
                    with open(pkl_file_name, "wb") as f:
                        pickle.dump(traced_model, f)
                    with open(pkl_file_name, "rb") as f:
                        loaded = pickle.load(f)
                except Exception as e:
                    self.fail(f"Couldn't serialize / deserialize the traced model: {e}")

                loaded_output = loaded(**filtered_inputs)
                loaded_output = flatten_output(loaded_output)

                for i in range(num_outputs):
                    self.assertTrue(
                        torch.allclose(model_output[i], loaded_output[i]),
                        f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
                    )

            # Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
            # (Even with this call, there are still memory leak by ~0.04MB)
            self.clear_torch_jit_class_registry()

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_shift_right(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_model_v1_1(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        # check that gated gelu feed forward and different word embeddings work
        config = config_and_inputs[0]
        config.tie_word_embeddings = False
        config.feed_forward_proj = "gated-gelu"
        self.model_tester.create_and_check_model(config, *config_and_inputs[1:])

    # T5ForSequenceClassification does not support inputs_embeds
    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (T5Model, T5ForConditionalGeneration, T5ForQuestionAnswering):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))

            if not self.is_encoder_decoder:
                input_ids = inputs["input_ids"]
                del inputs["input_ids"]
            else:
                encoder_input_ids = inputs["input_ids"]
                decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids", None)

            wte = model.get_input_embeddings()
            if not self.is_encoder_decoder:
                inputs["inputs_embeds"] = wte(input_ids)
            else:
                inputs["inputs_embeds"] = wte(encoder_input_ids)
                inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)

            with torch.no_grad():
                model(**inputs)[0]

    def test_config_and_model_silu_gated(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        config = config_and_inputs[0]
        config.feed_forward_proj = "gated-silu"
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_with_lm_head(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_with_lm_head(*config_and_inputs)

    def test_with_sequence_classification_head(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)

    def test_decoder_model_past(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)

    def test_decoder_model_past_with_attn_mask(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)

    def test_decoder_model_past_with_3d_attn_mask(self):
        (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = self.model_tester.prepare_config_and_inputs()

        attention_mask = ids_tensor(
            [self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
            vocab_size=2,
        )
        decoder_attention_mask = ids_tensor(
            [self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
            vocab_size=2,
        )

        self.model_tester.create_and_check_decoder_model_attention_mask_past(
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        )

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_generate_with_past_key_values(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs)

    def test_encoder_decoder_shared_weights(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs)

    @unittest.skipIf(torch_device == "cpu", "Cant do half precision")
    def test_model_fp16_forward(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)

    def test_v1_1_resize_embeddings(self):
        config = self.model_tester.prepare_config_and_inputs()[0]
        self.model_tester.check_resize_embeddings_t5_v1_1(config)

    @slow
    def test_model_from_pretrained(self):
        model_name = "google-t5/t5-small"
        model = T5Model.from_pretrained(model_name)
        self.assertIsNotNone(model)

    @unittest.skip("Test has a segmentation fault on torch 1.8.0")
    def test_export_to_onnx(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        model = T5Model(config_and_inputs[0]).to(torch_device)
        with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                model,
                (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
                f"{tmpdirname}/t5_test.onnx",
                export_params=True,
                opset_version=9,
                input_names=["input_ids", "decoder_input_ids"],
            )

    def test_generate_with_head_masking(self):
        attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        config = config_and_inputs[0]
        max_length = config_and_inputs[1].shape[-1] + 3
        model = T5ForConditionalGeneration(config).eval()
        model.to(torch_device)

        head_masking = {
            "head_mask": torch.zeros(config.num_layers, config.num_heads, device=torch_device),
            "decoder_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device),
            "cross_attn_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device),
        }

        for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
            head_masks = {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                head_masks["decoder_head_mask"] = torch.ones(
                    config.num_decoder_layers, config.num_heads, device=torch_device
                )

            out = model.generate(
                config_and_inputs[1],
                num_beams=1,
                max_length=max_length,
                output_attentions=True,
                return_dict_in_generate=True,
                **head_masks,
            )
            # We check the state of decoder_attentions and cross_attentions just from the last step
            attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)

    @unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
    def test_disk_offload(self):
        pass

    @unittest.skip("Does not support conversations.")
    def test_pipeline_conversational(self):
        pass


class T5EncoderOnlyModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=13,
        encoder_seq_length=7,
        # For common tests
        use_attention_mask=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        d_ff=37,
        relative_attention_num_buckets=8,
        is_training=False,
        dropout_rate=0.1,
        initializer_factor=0.002,
        is_encoder_decoder=False,
        eos_token_id=1,
        pad_token_id=0,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        # For common tests
        self.seq_length = self.encoder_seq_length
        self.use_attention_mask = use_attention_mask
        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.d_ff = d_ff
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.dropout_rate = dropout_rate
        self.initializer_factor = initializer_factor
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.is_encoder_decoder = is_encoder_decoder
        self.scope = None
        self.is_training = is_training

    def get_large_model_config(self):
        return T5Config.from_pretrained("google-t5/t5-base")

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)

        attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)

        config = T5Config(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            d_ff=self.d_ff,
            d_kv=self.hidden_size // self.num_attention_heads,
            num_layers=self.num_hidden_layers,
            num_heads=self.num_attention_heads,
            relative_attention_num_buckets=self.relative_attention_num_buckets,
            dropout_rate=self.dropout_rate,
            initializer_factor=self.initializer_factor,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.pad_token_id,
            pad_token_id=self.pad_token_id,
            is_encoder_decoder=self.is_encoder_decoder,
        )

        return (
            config,
            input_ids,
            attention_mask,
        )

    def create_and_check_model(
        self,
        config,
        input_ids,
        attention_mask,
    ):
        model = T5EncoderModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids=input_ids,
            attention_mask=attention_mask,
        )
        result = model(input_ids=input_ids)
        encoder_output = result.last_hidden_state

        self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))

    def create_and_check_model_fp16_forward(
        self,
        config,
        input_ids,
        attention_mask,
    ):
        model = T5EncoderModel(config=config).to(torch_device).half().eval()
        output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
        self.parent.assertFalse(torch.isnan(output).any().item())

    def create_and_check_with_token_classification_head(
        self,
        config,
        input_ids,
        attention_mask,
    ):
        labels = torch.tensor([1] * self.seq_length * self.batch_size, dtype=torch.long, device=torch_device)
        model = T5ForTokenClassification(config=config).to(torch_device).eval()
        outputs = model(
            input_ids=input_ids,
            labels=labels,
            attention_mask=attention_mask,
        )
        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
        self.parent.assertEqual(outputs["loss"].size(), ())

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            attention_mask,
        ) = config_and_inputs

        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict


class T5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (T5EncoderModel, T5ForTokenClassification) if is_torch_available() else ()
    test_pruning = False
    test_resize_embeddings = False
    test_model_parallel = True
    pipeline_model_mapping = (
        {
            "token-classification": T5ForTokenClassification,
        }
        if is_torch_available()
        else {}
    )
    all_parallelizable_model_classes = (T5EncoderModel,) if is_torch_available() else ()

    def setUp(self):
        self.model_tester = T5EncoderOnlyModelTester(self)
        self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skipIf(torch_device == "cpu", "Cant do half precision")
    def test_model_fp16_forward(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)

    def test_with_token_classification_head(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)


def use_task_specific_params(model, task):
    model.config.update(model.config.task_specific_params[task])


@require_torch
@require_accelerate
@require_tokenizers
@slow
class T5ModelFp16Tests(unittest.TestCase):
    def test_fp16_fp32_conversion(self):
        r"""
        A test to check whether the argument `keep_in_fp32_modules` correctly does its job
        """
        orig_import = __import__
        accelerate_mock = unittest.mock.Mock()

        # mock import of accelerate
        def import_accelerate_mock(name, *args, **kwargs):
            if name == "accelerate":
                if accelerate_available:
                    return accelerate_mock
                else:
                    raise ImportError
            return orig_import(name, *args, **kwargs)

        # Load without using `accelerate`
        with unittest.mock.patch("builtins.__import__", side_effect=import_accelerate_mock):
            accelerate_available = False

            model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small", torch_dtype=torch.float16)
            self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
            self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16)

            # Load without in bf16
            model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small", torch_dtype=torch.bfloat16)
            self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16)
            self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16)

        # Load using `accelerate` in bf16
        model = T5ForConditionalGeneration.from_pretrained(
            "google-t5/t5-small", torch_dtype=torch.bfloat16, device_map="auto"
        )
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16)
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16)

        # Load using `accelerate` in bf16
        model = T5ForConditionalGeneration.from_pretrained(
            "google-t5/t5-small", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True
        )
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16)
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16)

        # Load without using `accelerate`
        model = T5ForConditionalGeneration.from_pretrained(
            "google-t5/t5-small", torch_dtype=torch.float16, low_cpu_mem_usage=True
        )
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16)

        # Load using `accelerate`
        model = T5ForConditionalGeneration.from_pretrained(
            "google-t5/t5-small", torch_dtype=torch.float16, device_map="auto"
        )
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16)


@require_torch
@require_sentencepiece
@require_tokenizers
class T5ModelIntegrationTests(unittest.TestCase):
    @cached_property
    def model(self):
        return T5ForConditionalGeneration.from_pretrained("google-t5/t5-base").to(torch_device)

    @cached_property
    def tokenizer(self):
        return T5Tokenizer.from_pretrained("google-t5/t5-base")

    @slow
    def test_torch_quant(self):
        r"""
        Test that a simple `torch.quantization.quantize_dynamic` call works on a T5 model.
        """
        model_name = "google/flan-t5-small"
        tokenizer = T5Tokenizer.from_pretrained(model_name)
        model = T5ForConditionalGeneration.from_pretrained(model_name)
        model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
        input_text = "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
        input_ids = tokenizer(input_text, return_tensors="pt").input_ids
        _ = model.generate(input_ids)

    @slow
    def test_small_generation(self):
        model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small").to(torch_device)
        model.config.max_length = 8
        model.config.num_beams = 1
        model.config.do_sample = False
        tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")

        input_ids = tokenizer("summarize: Hello there", return_tensors="pt").input_ids.to(torch_device)

        sequences = model.generate(input_ids)

        output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
        self.assertTrue(output_str == "Hello there!")

    @slow
    def test_small_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_t5_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small").to(torch_device)
        tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -19.0845
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

    @slow
    def test_small_v1_1_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.7.1
        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary

        >>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
        >>> path_to_mtf_small_spm_model_path = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_v1_1_checkpoint, batch_size=1, tpu=None)
        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = T5ForConditionalGeneration.from_pretrained("google/t5-v1_1-small").to(torch_device)
        tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -59.0293
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

    @slow
    def test_small_byt5_integration_test(self):
        """
        For comparision run:
        >>> import t5  # pip install t5==0.9.1

        >>> path_to_byt5_small_checkpoint = '<fill_in>'
        >>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
        >>> vocab = t5.data.ByteVocabulary()
        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
        """

        model = T5ForConditionalGeneration.from_pretrained("google/byt5-small").to(torch_device)
        tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")

        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
        labels = tokenizer("Hi I am", return_tensors="pt").input_ids

        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
        mtf_score = -(labels.shape[-1] * loss.item())

        EXPECTED_SCORE = -60.7397
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

    @slow
    def test_summarization(self):
        model = self.model
        tok = self.tokenizer

        FRANCE_ARTICLE = (  # @noqa
            "Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings"
            " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane."
            ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."'
            ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s'
            " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video"
            " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French"
            " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a"
            " phone at the wreckage site. The two publications described the supposed video, but did not post it on"
            " their websites. The publications said that they watched the video, which was found by a source close to"
            " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported."
            ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the'
            " cockpit door with a heavy object.  Towards the end, after a heavy shake, stronger than the others, the"
            ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,'
            " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said"
            " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman"
            " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the"
            ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,'
            ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be'
            " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by"
            " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so"
            " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could"
            ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin'
            ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match'
            ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered'
            ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something'
            " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the"
            ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline'
            " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the"
            " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the"
            ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of'
            ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school'
            " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in"
            " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent"
            " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and"
            " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%"
            ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was'
            " sharing the information and documents -- including training and medical records -- with public"
            " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the"
            " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the"
            " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash"
            " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late"
            " Tuesday that no visible human remains were left at the site but recovery teams would keep searching."
            " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all"
            " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested."
            " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said."
            " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew"
            " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with"
            " the flight school during his training were among several developments as investigators continued to"
            " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa"
            " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his"
            ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in'
            " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at"
            " some point before his aviation career and underwent psychotherapy before he got his pilot's license."
            " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the"
            " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to"
            " lose his pilot's license, a European government official briefed on the investigation told CNN on"
            ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being'
            " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that"
            " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would"
            " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had"
            " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded"
            " he had psychological issues, the European government official said. But no matter what details emerge"
            " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic"
            ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact'
            " that maybe they weren't going to keep doing their job and they're upset about that and so they're"
            ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to'
            " also take that rage and turn it outward on 149 other people who had nothing to do with the person's"
            ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight'
            " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura"
            " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine"
            " Amiel and Anna-Maja Rappard contributed to this report."
        )
        SHORTER_ARTICLE = (
            "(CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
            " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
            " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
            " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
            ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
            ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
            " situation in Palestinian territories, paving the way for possible war crimes investigations against"
            " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
            " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
            " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
            ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
            ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
            ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
            " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
            ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
            " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
            ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
            ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
            " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
            ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
            " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
            ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
            " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
            ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
            " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
            ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
            ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
            ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
            " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
            ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
            " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
            ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
            " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
            " will include alleged war crimes committed since June. The International Criminal Court was set up in"
            " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
            " and Faith Karimi contributed to this report."
        )
        IRAN_ARTICLE = (
            "(CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran"
            " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively"
            " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger."
            " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli"
            " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a"
            " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since"
            " the announcement of the new framework will likely result in more heat than light. It will not be helped"
            " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ."
            " The most misleading assertion, despite universal rejection by experts, is that the negotiations'"
            " objective at the outset was the total elimination of any nuclear program in Iran. That is the position"
            " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it"
            " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has"
            " always been to structure an agreement or series of agreements so that Iran could not covertly develop a"
            " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded"
            " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by"
            " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another"
            " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite"
            " sharp accusations by some in the United States and its allies, Iran denies having such a program, and"
            " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's"
            " continued cooperation with International Atomic Energy Agency inspections is further evidence on this"
            " point, and we'll know even more about Iran's program in the coming months and years because of the deal."
            " In fact, the inspections provisions that are part of this agreement are designed to protect against any"
            " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that"
            " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter"
            " warning that a deal might be killed by Congress or a future president). This of course is not the case."
            " The talks were between Iran and the five permanent members of the U.N. Security Council (United States,"
            " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has"
            " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement"
            " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran"
            " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement"
            " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the"
            " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased"
            " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes"
            " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear"
            " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going"
            " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such"
            " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the"
            ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not'
            " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New"
            " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement"
            " with Iran will not be so balanced.  The restrictions and obligations in the final framework agreement"
            " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove"
            " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally"
            " some insist that any agreement must address Iranian missile programs, human rights violations or support"
            " for Hamas or Hezbollah.  As important as these issues are, and they must indeed be addressed, they are"
            " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran.  To include them in"
            " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it"
            " affects the security of our negotiating partners and allies, including Israel. Those judgments should be"
            " fact-based, not based on questionable assertions or dubious assumptions."
        )
        ARTICLE_SUBWAY = (
            "New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
            " year later, she got married again in Westchester County, but to a different man and without divorcing"
            " her first husband.  Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
            ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
            " once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
            ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
            ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
            " license application, according to court documents. Prosecutors said the marriages were part of an"
            " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
            " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
            " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
            " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
            " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.  All"
            " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
            " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
            " said the immigration scam involved some of her husbands, who filed for permanent residence status"
            " shortly after the marriages.  Any divorces happened only after such filings were approved. It was"
            " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
            " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
            ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
            " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
            " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
            " up to four years in prison.  Her next court appearance is scheduled for May 18."
        )

        expected_summaries = [
            'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a'
            " cell phone video of the final seconds . \"one can hear cries of 'My God' in several languages,\" one"
            " magazine says .",
            "the formal accession was marked by a ceremony at The Hague, in the Netherlands . the ICC opened a"
            " preliminary examination into the situation in the occupied Palestinian territory . as members of the"
            " court, Palestinians may be subject to counter-charges as well .",
            "the u.s. and its negotiating partners reached a very strong framework agreement with Iran . aaron miller:"
            " the debate that has already begun since the announcement of the new framework will likely result in more"
            " heat than light . the deal would reduce Iran's low-enriched uranium stockpile, cut centrifuges and"
            " implement a rigorous inspection regime .",
            "prosecutors say the marriages were part of an immigration scam . if convicted, barrientos faces two"
            ' criminal counts of "offering a false instrument for filing in the first degree" she has been married 10'
            " times, with nine of her marriages occurring between 1999 and 2002 .",
        ]

        use_task_specific_params(model, "summarization")

        dct = tok(
            [model.config.prefix + x for x in [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY]],
            padding="max_length",
            truncation=True,
            return_tensors="pt",
        ).to(torch_device)
        self.assertEqual(512, dct["input_ids"].shape[1])

        hypotheses_batch = model.generate(
            **dct,
            num_beams=4,
            length_penalty=2.0,
            max_length=142,
            min_length=56,
            no_repeat_ngram_size=3,
            do_sample=False,
            early_stopping=True,
        )

        decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False)
        self.assertListEqual(
            expected_summaries,
            decoded,
        )

    @slow
    def test_translation_en_to_de(self):
        model = self.model
        tok = self.tokenizer
        use_task_specific_params(model, "translation_en_to_de")

        en_text = '"Luigi often said to me that he never wanted the brothers to end up in court", she wrote.'
        expected_translation = (
            '"Luigi sagte mir oft, dass er nie wollte, dass die Brüder am Gericht sitzen", schrieb sie.'
        )

        input_ids = tok.encode(model.config.prefix + en_text, return_tensors="pt")
        input_ids = input_ids.to(torch_device)
        output = model.generate(input_ids)
        translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
        self.assertEqual(translation, expected_translation)

    @slow
    def test_translation_en_to_fr(self):
        model = self.model  # google-t5/t5-base
        tok = self.tokenizer
        use_task_specific_params(model, "translation_en_to_fr")

        en_text = (
            ' This image section from an infrared recording by the Spitzer telescope shows a "family portrait" of'
            " countless generations of stars: the oldest stars are seen as blue dots. "
        )

        input_ids = tok.encode(model.config.prefix + en_text, return_tensors="pt")
        input_ids = input_ids.to(torch_device)

        output = model.generate(
            input_ids=input_ids,
            num_beams=4,
            length_penalty=2.0,
            max_length=100,
            no_repeat_ngram_size=3,
            do_sample=False,
            early_stopping=True,
        )
        translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
        new_truncated_translation = (
            "Cette section d'images provenant de l'enregistrement infrarouge effectué par le télescope Spitzer montre "
            "un "
            "« portrait familial » de générations innombrables d’étoiles : les plus anciennes sont observées "
            "sous forme "
            "de points bleus."
        )

        self.assertEqual(translation, new_truncated_translation)

    @slow
    def test_translation_en_to_ro(self):
        model = self.model
        tok = self.tokenizer
        use_task_specific_params(model, "translation_en_to_ro")
        en_text = "Taco Bell said it plans to add 2,000 locations in the US by 2022."
        expected_translation = "Taco Bell a declarat că intenţionează să adauge 2 000 de locaţii în SUA până în 2022."

        inputs = tok(model.config.prefix + en_text, return_tensors="pt").to(torch_device)
        output = model.generate(**inputs)
        translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
        self.assertEqual(translation, expected_translation)

    @slow
    def test_contrastive_search_t5(self):
        article = (
            " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
            " year later, she got married again in Westchester County, but to a different man and without divorcing"
            " her first husband.  Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
            ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
            " once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
            ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
            ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
            " license application, according to court documents. Prosecutors said the marriages were part of an"
            " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
            " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
            " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
            " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
            " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.  All"
            " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
            " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
            " said the immigration scam involved some of her husbands, who filed for permanent residence status"
            " shortly after the marriages.  Any divorces happened only after such filings were approved. It was"
            " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
            " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
            ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
            " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
            " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
            " up to four years in prison.  Her next court appearance is scheduled for May 18."
        )
        article = "summarize: " + article.strip()
        t5_tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-base-cnn-dm")
        t5_model = T5ForConditionalGeneration.from_pretrained("flax-community/t5-base-cnn-dm").to(torch_device)
        input_ids = t5_tokenizer(
            article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="pt"
        ).input_ids.to(torch_device)

        outputs = t5_model.generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64)
        generated_text = t5_tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(
            generated_text,
            [
                "Liana Barrientos has been married 10 times, nine of them in the Bronx. Her husbands filed for "
                "permanent residence after the marriages, prosecutors say."
            ],
        )


@require_torch
class TestAsymmetricT5(unittest.TestCase):
    def build_model_and_check_forward_pass(self, **kwargs):
        tester = T5ModelTester(self, **kwargs)
        config, *inputs = tester.prepare_config_and_inputs()
        (
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = inputs
        model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
        outputs = model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            labels=lm_labels,
        )
        # outputs = model(*inputs)
        assert len(outputs) == 4
        assert outputs["logits"].size() == (tester.batch_size, tester.decoder_seq_length, tester.vocab_size)
        assert outputs["loss"].size() == ()
        return model

    def test_small_decoder(self):
        # num_hidden_layers is passed to T5Config as num_layers
        model = self.build_model_and_check_forward_pass(decoder_layers=1, num_hidden_layers=2)
        assert len(model.encoder.block) == 2
        assert len(model.decoder.block) == 1

    def test_defaulting_to_symmetry(self):
        # num_hidden_layers is passed to T5Config as num_layers
        model = self.build_model_and_check_forward_pass(num_hidden_layers=2)
        assert len(model.decoder.block) == len(model.encoder.block) == 2