# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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.


from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFSharedEmbeddings,
    )

    if _tf_gpu_memory_limit is not None:
        gpus = tf.config.list_physical_devices("GPU")
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                logical_gpus = tf.config.list_logical_devices("GPU")
                print("Logical GPUs", logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)


@require_tf
class TFCoreModelTesterMixin:
    model_tester = None
    all_model_classes = ()
    all_generative_model_classes = ()
    test_mismatched_shapes = True
    test_resize_embeddings = True
    test_head_masking = True
    is_encoder_decoder = False

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
        inputs_dict = copy.deepcopy(inputs_dict)

        if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
            inputs_dict = {
                k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                if isinstance(v, tf.Tensor) and v.ndim > 0
                else v
                for k, v in inputs_dict.items()
            }

        if return_labels:
            if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in [
                *get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
                *get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
            ]:
                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
                inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in [
                *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
                *get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING),
                *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
                *get_values(TF_MODEL_FOR_PRETRAINING_MAPPING),
                *get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
            ]:
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
                )
        return inputs_dict

    @slow
    def test_graph_mode(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
            inputs = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)

            @tf.function
            def run_in_graph_mode():
                return model(inputs)

            outputs = run_in_graph_mode()
            self.assertIsNotNone(outputs)

    @slow
    def test_xla_mode(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
            inputs = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)

            @tf.function(experimental_compile=True)
            def run_in_graph_mode():
                return model(inputs)

            outputs = run_in_graph_mode()
            self.assertIsNotNone(outputs)

    @slow
    def test_xla_fit(self):
        # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
            model = model_class(config)
            if getattr(model, "hf_compute_loss", None):
                # Test that model correctly compute the loss with kwargs
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                # Is there a better way to remove these decoder inputs?
                prepared_for_class = {
                    key: val
                    for key, val in prepared_for_class.items()
                    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                }

                possible_label_cols = {
                    "labels",
                    "label",
                    "label_ids",
                    "start_positions",
                    "start_position",
                    "end_positions",
                    "end_position",
                    "next_sentence_label",
                }
                label_names = possible_label_cols.intersection(set(prepared_for_class))
                self.assertGreater(len(label_names), 0, msg="No matching label names found!")
                labels = {key: val for key, val in prepared_for_class.items() if key in label_names}
                inputs_minus_labels = {key: val for key, val in prepared_for_class.items() if key not in label_names}
                self.assertGreater(len(inputs_minus_labels), 0)

                # Make sure it works with XLA!
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0), jit_compile=True)
                # Make sure the model fits without crashing regardless of where we pass the labels
                history = model.fit(
                    prepared_for_class,
                    validation_data=prepared_for_class,
                    steps_per_epoch=1,
                    validation_steps=1,
                    shuffle=False,
                    verbose=0,
                )
                loss = history.history["loss"][0]
                self.assertTrue(not isnan(loss))
                val_loss = history.history["val_loss"][0]
                self.assertTrue(not isnan(val_loss))

                # Now test it with separate labels, to make sure that path works in XLA too.
                model = model_class(config)
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0), jit_compile=True)
                history = model.fit(
                    inputs_minus_labels,
                    labels,
                    validation_data=(inputs_minus_labels, labels),
                    steps_per_epoch=1,
                    validation_steps=1,
                    shuffle=False,
                    verbose=0,
                )

                loss = history.history["loss"][0]
                self.assertTrue(not isnan(loss))
                val_loss = history.history["val_loss"][0]
                self.assertTrue(not isnan(val_loss))

    @slow
    def test_saved_model_creation_extended(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        if hasattr(config, "use_cache"):
            config.use_cache = True

        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)

        for model_class in self.all_model_classes[:2]:
            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)
            model.build()
            num_out = len(model(class_inputs_dict))

            for key in list(class_inputs_dict.keys()):
                # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                if key not in model.input_signature:
                    del class_inputs_dict[key]
                # Check it's a tensor, in case the inputs dict has some bools in it too
                elif isinstance(class_inputs_dict[key], tf.Tensor) and class_inputs_dict[key].dtype.is_integer:
                    class_inputs_dict[key] = tf.cast(class_inputs_dict[key], tf.int32)

            if set(class_inputs_dict.keys()) != set(model.input_signature.keys()):
                continue  # Some models have inputs that the preparation functions don't create, we skip those

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=True)
                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
                model = tf.keras.models.load_model(saved_model_dir)
                outputs = model(class_inputs_dict)

                if self.is_encoder_decoder:
                    output_hidden_states = outputs["encoder_hidden_states"]
                    output_attentions = outputs["encoder_attentions"]
                else:
                    output_hidden_states = outputs["hidden_states"]
                    output_attentions = outputs["attentions"]

                self.assertEqual(len(outputs), num_out)

                expected_num_layers = getattr(
                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
                )

                self.assertEqual(len(output_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )

                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                )

    @slow
    def test_mixed_precision(self):
        tf.keras.mixed_precision.set_global_policy("mixed_float16")

        # try/finally block to ensure subsequent tests run in float32
        try:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                model = model_class(config)
                outputs = model(class_inputs_dict)

                self.assertIsNotNone(outputs)
        finally:
            tf.keras.mixed_precision.set_global_policy("float32")

    @slow
    def test_train_pipeline_custom_model(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        # head_mask and decoder_head_mask has different shapes than other input args
        if "head_mask" in inputs_dict:
            del inputs_dict["head_mask"]
        if "decoder_head_mask" in inputs_dict:
            del inputs_dict["decoder_head_mask"]
        if "cross_attn_head_mask" in inputs_dict:
            del inputs_dict["cross_attn_head_mask"]
        tf_main_layer_classes = {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__),)
            for module_member_name in dir(module)
            if module_member_name.endswith("MainLayer")
            for module_member in (getattr(module, module_member_name),)
            if isinstance(module_member, type)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(module_member, "_keras_serializable", False)
        }

        for main_layer_class in tf_main_layer_classes:
            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
            if "T5" in main_layer_class.__name__:
                # Take the same values than in TFT5ModelTester for this shared layer
                shared = TFSharedEmbeddings(self.model_tester.vocab_size, self.model_tester.hidden_size, name="shared")
                config.use_cache = False
                main_layer = main_layer_class(config, embed_tokens=shared)
            else:
                main_layer = main_layer_class(config)

            symbolic_inputs = {
                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
            }

            if hasattr(self.model_tester, "num_labels"):
                num_labels = self.model_tester.num_labels
            else:
                num_labels = 2

            X = tf.data.Dataset.from_tensor_slices(
                (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)))
            ).batch(1)

            hidden_states = main_layer(symbolic_inputs)[0]
            outputs = tf.keras.layers.Dense(num_labels, activation="softmax", name="outputs")(hidden_states)
            model = tf.keras.models.Model(inputs=symbolic_inputs, outputs=[outputs])

            model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["binary_accuracy"])
            model.fit(X, epochs=1)

            with tempfile.TemporaryDirectory() as tmpdirname:
                filepath = os.path.join(tmpdirname, "keras_model.h5")
                model.save(filepath)
                if "T5" in main_layer_class.__name__:
                    model = tf.keras.models.load_model(
                        filepath,
                        custom_objects={
                            main_layer_class.__name__: main_layer_class,
                            "TFSharedEmbeddings": TFSharedEmbeddings,
                        },
                    )
                else:
                    model = tf.keras.models.load_model(
                        filepath, custom_objects={main_layer_class.__name__: main_layer_class}
                    )
                assert isinstance(model, tf.keras.Model)
                model(inputs_dict)

    @slow
    def test_graph_mode_with_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
            model = model_class(config)

            inputs = copy.deepcopy(inputs_dict)

            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)

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

            inputs = self._prepare_for_class(inputs, model_class)

            @tf.function
            def run_in_graph_mode():
                return model(inputs)

            outputs = run_in_graph_mode()
            self.assertIsNotNone(outputs)

    def _generate_random_bad_tokens(self, num_bad_tokens, model):
        # special tokens cannot be bad tokens
        special_tokens = []
        if model.config.bos_token_id is not None:
            special_tokens.append(model.config.bos_token_id)
        if model.config.pad_token_id is not None:
            special_tokens.append(model.config.pad_token_id)
        if model.config.eos_token_id is not None:
            special_tokens.append(model.config.eos_token_id)

        # create random bad tokens that are not special tokens
        bad_tokens = []
        while len(bad_tokens) < num_bad_tokens:
            token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0]
            if token not in special_tokens:
                bad_tokens.append(token)
        return bad_tokens

    def _check_generated_ids(self, output_ids):
        for token_id in output_ids[0].numpy().tolist():
            self.assertGreaterEqual(token_id, 0)
            self.assertLess(token_id, self.model_tester.vocab_size)

    def _check_match_tokens(self, generated_ids, bad_words_ids):
        # for all bad word tokens
        for bad_word_ids in bad_words_ids:
            # for all slices in batch
            for generated_ids_slice in generated_ids:
                # for all word idx
                for i in range(len(bad_word_ids), len(generated_ids_slice)):
                    # if tokens match
                    if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids:
                        return True
        return False

# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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.


from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFSharedEmbeddings,
    )

    if _tf_gpu_memory_limit is not None:
        gpus = tf.config.list_physical_devices("GPU")
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                logical_gpus = tf.config.list_logical_devices("GPU")
                print("Logical GPUs", logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)


@require_tf
class TFCoreModelTesterMixin:
    model_tester = None
    all_model_classes = ()
    all_generative_model_classes = ()
    test_mismatched_shapes = True
    test_resize_embeddings = True
    test_head_masking = True
    is_encoder_decoder = False

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
        inputs_dict = copy.deepcopy(inputs_dict)

        if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
            inputs_dict = {
                k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                if isinstance(v, tf.Tensor) and v.ndim > 0
                else v
                for k, v in inputs_dict.items()
            }

        if return_labels:
            if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in [
                *get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
                *get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
            ]:
                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
                inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in [
                *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
                *get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING),
                *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
                *get_values(TF_MODEL_FOR_PRETRAINING_MAPPING),
                *get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
            ]:
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
                )
        return inputs_dict

    @slow
    def test_graph_mode(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
            inputs = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)

            @tf.function
            def run_in_graph_mode():
                return model(inputs)

            outputs = run_in_graph_mode()
            self.assertIsNotNone(outputs)

    @slow
    def test_xla_mode(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
            inputs = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)

            @tf.function(experimental_compile=True)
            def run_in_graph_mode():
                return model(inputs)

            outputs = run_in_graph_mode()
            self.assertIsNotNone(outputs)

    @slow
    def test_xla_fit(self):
        # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
            model = model_class(config)
            if getattr(model, "hf_compute_loss", None):
                # Test that model correctly compute the loss with kwargs
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                # Is there a better way to remove these decoder inputs?
                prepared_for_class = {
                    key: val
                    for key, val in prepared_for_class.items()
                    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                }

                possible_label_cols = {
                    "labels",
                    "label",
                    "label_ids",
                    "start_positions",
                    "start_position",
                    "end_positions",
                    "end_position",
                    "next_sentence_label",
                }
                label_names = possible_label_cols.intersection(set(prepared_for_class))
                self.assertGreater(len(label_names), 0, msg="No matching label names found!")
                labels = {key: val for key, val in prepared_for_class.items() if key in label_names}
                inputs_minus_labels = {key: val for key, val in prepared_for_class.items() if key not in label_names}
                self.assertGreater(len(inputs_minus_labels), 0)

                # Make sure it works with XLA!
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0), jit_compile=True)
                # Make sure the model fits without crashing regardless of where we pass the labels
                history = model.fit(
                    prepared_for_class,
                    validation_data=prepared_for_class,
                    steps_per_epoch=1,
                    validation_steps=1,
                    shuffle=False,
                    verbose=0,
                )
                loss = history.history["loss"][0]
                self.assertTrue(not isnan(loss))
                val_loss = history.history["val_loss"][0]
                self.assertTrue(not isnan(val_loss))

                # Now test it with separate labels, to make sure that path works in XLA too.
                model = model_class(config)
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0), jit_compile=True)
                history = model.fit(
                    inputs_minus_labels,
                    labels,
                    validation_data=(inputs_minus_labels, labels),
                    steps_per_epoch=1,
                    validation_steps=1,
                    shuffle=False,
                    verbose=0,
                )

                loss = history.history["loss"][0]
                self.assertTrue(not isnan(loss))
                val_loss = history.history["val_loss"][0]
                self.assertTrue(not isnan(val_loss))

    @slow
    def test_saved_model_creation_extended(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        if hasattr(config, "use_cache"):
            config.use_cache = True

        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)

        for model_class in self.all_model_classes[:2]:
            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            model = model_class(config)
            model.build()
            num_out = len(model(class_inputs_dict))

            for key in list(class_inputs_dict.keys()):
                # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                if key not in model.input_signature:
                    del class_inputs_dict[key]
                # Check it's a tensor, in case the inputs dict has some bools in it too
                elif isinstance(class_inputs_dict[key], tf.Tensor) and class_inputs_dict[key].dtype.is_integer:
                    class_inputs_dict[key] = tf.cast(class_inputs_dict[key], tf.int32)

            if set(class_inputs_dict.keys()) != set(model.input_signature.keys()):
                continue  # Some models have inputs that the preparation functions don't create, we skip those

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=True)
                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
                model = tf.keras.models.load_model(saved_model_dir)
                outputs = model(class_inputs_dict)

                if self.is_encoder_decoder:
                    output_hidden_states = outputs["encoder_hidden_states"]
                    output_attentions = outputs["encoder_attentions"]
                else:
                    output_hidden_states = outputs["hidden_states"]
                    output_attentions = outputs["attentions"]

                self.assertEqual(len(outputs), num_out)

                expected_num_layers = getattr(
                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
                )

                self.assertEqual(len(output_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )

                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                )

    @slow
    def test_mixed_precision(self):
        tf.keras.mixed_precision.set_global_policy("mixed_float16")

        # try/finally block to ensure subsequent tests run in float32
        try:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                model = model_class(config)
                outputs = model(class_inputs_dict)

                self.assertIsNotNone(outputs)
        finally:
            tf.keras.mixed_precision.set_global_policy("float32")

    @slow
    def test_train_pipeline_custom_model(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        # head_mask and decoder_head_mask has different shapes than other input args
        if "head_mask" in inputs_dict:
            del inputs_dict["head_mask"]
        if "decoder_head_mask" in inputs_dict:
            del inputs_dict["decoder_head_mask"]
        if "cross_attn_head_mask" in inputs_dict:
            del inputs_dict["cross_attn_head_mask"]
        tf_main_layer_classes = {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__),)
            for module_member_name in dir(module)
            if module_member_name.endswith("MainLayer")
            for module_member in (getattr(module, module_member_name),)
            if isinstance(module_member, type)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(module_member, "_keras_serializable", False)
        }

        for main_layer_class in tf_main_layer_classes:
            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
            if "T5" in main_layer_class.__name__:
                # Take the same values than in TFT5ModelTester for this shared layer
                shared = TFSharedEmbeddings(self.model_tester.vocab_size, self.model_tester.hidden_size, name="shared")
                config.use_cache = False
                main_layer = main_layer_class(config, embed_tokens=shared)
            else:
                main_layer = main_layer_class(config)

            symbolic_inputs = {
                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
            }

            if hasattr(self.model_tester, "num_labels"):
                num_labels = self.model_tester.num_labels
            else:
                num_labels = 2

            X = tf.data.Dataset.from_tensor_slices(
                (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)))
            ).batch(1)

            hidden_states = main_layer(symbolic_inputs)[0]
            outputs = tf.keras.layers.Dense(num_labels, activation="softmax", name="outputs")(hidden_states)
            model = tf.keras.models.Model(inputs=symbolic_inputs, outputs=[outputs])

            model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["binary_accuracy"])
            model.fit(X, epochs=1)

            with tempfile.TemporaryDirectory() as tmpdirname:
                filepath = os.path.join(tmpdirname, "keras_model.h5")
                model.save(filepath)
                if "T5" in main_layer_class.__name__:
                    model = tf.keras.models.load_model(
                        filepath,
                        custom_objects={
                            main_layer_class.__name__: main_layer_class,
                            "TFSharedEmbeddings": TFSharedEmbeddings,
                        },
                    )
                else:
                    model = tf.keras.models.load_model(
                        filepath, custom_objects={main_layer_class.__name__: main_layer_class}
                    )
                assert isinstance(model, tf.keras.Model)
                model(inputs_dict)

    @slow
    def test_graph_mode_with_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
            model = model_class(config)

            inputs = copy.deepcopy(inputs_dict)

            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)

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

            inputs = self._prepare_for_class(inputs, model_class)

            @tf.function
            def run_in_graph_mode():
                return model(inputs)

            outputs = run_in_graph_mode()
            self.assertIsNotNone(outputs)

    def _generate_random_bad_tokens(self, num_bad_tokens, model):
        # special tokens cannot be bad tokens
        special_tokens = []
        if model.config.bos_token_id is not None:
            special_tokens.append(model.config.bos_token_id)
        if model.config.pad_token_id is not None:
            special_tokens.append(model.config.pad_token_id)
        if model.config.eos_token_id is not None:
            special_tokens.append(model.config.eos_token_id)

        # create random bad tokens that are not special tokens
        bad_tokens = []
        while len(bad_tokens) < num_bad_tokens:
            token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0]
            if token not in special_tokens:
                bad_tokens.append(token)
        return bad_tokens

    def _check_generated_ids(self, output_ids):
        for token_id in output_ids[0].numpy().tolist():
            self.assertGreaterEqual(token_id, 0)
            self.assertLess(token_id, self.model_tester.vocab_size)

    def _check_match_tokens(self, generated_ids, bad_words_ids):
        # for all bad word tokens
        for bad_word_ids in bad_words_ids:
            # for all slices in batch
            for generated_ids_slice in generated_ids:
                # for all word idx
                for i in range(len(bad_word_ids), len(generated_ids_slice)):
                    # if tokens match
                    if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids:
                        return True
        return False
