MMAudio-main/transformers/tests/test_modeling_tf_common.py

# 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 inspect
import json
import os
import random
import tempfile
import unittest
from importlib import import_module
from math import isnan

from datasets import Dataset

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import (
    CaptureLogger,
    require_tf,
    require_tf2onnx,
    slow,
)
from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
from transformers.utils.generic import ModelOutput


logger = logging.get_logger(__name__)


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

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_IMAGE_MODELING_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_SEMANTIC_SEGMENTATION_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFAutoModel,
        TFAutoModelForSequenceClassification,
        TFSharedEmbeddings,
    )
    from transformers.modeling_tf_utils import keras

    tf.config.experimental.enable_tensor_float_32_execution(False)


def _config_zero_init(config):
    configs_no_init = copy.deepcopy(config)
    for key in configs_no_init.__dict__.keys():
        if "_range" in key or "_std" in key:
            setattr(configs_no_init, key, 0.0)
    return configs_no_init


@require_tf
class TFModelTesterMixin:
    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
    has_attentions = True

    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),
                *get_values(TF_MODEL_FOR_DOCUMENT_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),
                *get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING),
            ] and "labels" in dict(inspect.signature(model_class.call).parameters):
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
                )
            elif model_class in get_values(TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING):
                num_patches = self.model_tester.image_size // self.model_tester.patch_size
                inputs_dict["bool_masked_pos"] = tf.zeros(
                    (self.model_tester.batch_size, num_patches**2), dtype=tf.int32
                )
            elif model_class in get_values(TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING):
                batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
                inputs_dict["labels"] = tf.zeros((self.model_tester.batch_size, height, width), dtype=tf.int32)
            elif model_class.__name__.endswith("ForCTC"):
                # When we have enough CTC models for an AutoClass, we should use their mapping instead of name checks
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
                )

        return inputs_dict

    def test_initialization(self):
        pass

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=False)

                # the config file (and the generation config file, if it can generate) should be saved
                self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME)))
                self.assertEqual(
                    model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME))
                )

                model = model_class.from_pretrained(tmpdirname)
                after_outputs = model(self._prepare_for_class(inputs_dict, model_class))

                self.assert_outputs_same(after_outputs, outputs)

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            model_config = model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(model_config)
            new_model = model_class.from_config(model.get_config())
            # make sure it also accepts a normal config
            _ = model_class.from_config(model.config)
            _ = new_model(self._prepare_for_class(inputs_dict, model_class))  # Build model
            new_model.set_weights(model.get_weights())
            after_outputs = new_model(self._prepare_for_class(inputs_dict, model_class))

            self.assert_outputs_same(after_outputs, outputs)

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

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

        model_class = self.all_model_classes[0]

        class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
        model = model_class(config)

        model(class_inputs_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname, saved_model=True)
            saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
            self.assertTrue(os.path.exists(saved_model_dir))

    def test_prepare_serving_output(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = self.has_attentions

        for model_class in self.all_model_classes:
            model = model_class(config)
            inputs = self._prepare_for_class(inputs_dict, model_class)
            outputs = model(inputs)
            serving_outputs = model.serving_output(outputs)

            for k, v in serving_outputs.items():
                # Check that we have one of three possible outputs: None, tuple of tensors or a tensor
                if isinstance(v, tuple):
                    self.assertTrue(all(isinstance(elem, tf.Tensor) for elem in v))
                elif v is not None:
                    self.assertIsInstance(v, tf.Tensor)
                else:
                    self.assertIsNone(v)

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.call)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            if model.config.is_encoder_decoder:
                expected_arg_names = [
                    "input_ids",
                    "attention_mask",
                    "decoder_input_ids",
                    "decoder_attention_mask",
                ]
                expected_arg_names.extend(["decoder_position_ids"] if "decoder_position_ids" in arg_names else [])
                expected_arg_names.extend(
                    ["head_mask", "decoder_head_mask"] if "head_mask" and "decoder_head_mask" in arg_names else []
                )
                expected_arg_names.extend(
                    ["cross_attn_head_mask", "encoder_outputs"]
                    if "cross_attn_head_mask" in arg_names
                    else ["encoder_outputs"]
                )
                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

            else:
                expected_arg_names = ["input_ids"]
                self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_onnx_compliancy(self):
        if not self.test_onnx:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        INTERNAL_OPS = [
            "Assert",
            "AssignVariableOp",
            "EmptyTensorList",
            "ReadVariableOp",
            "ResourceGather",
            "TruncatedNormal",
            "VarHandleOp",
            "VarIsInitializedOp",
        ]
        onnx_ops = []

        with open(os.path.join(".", "utils", "tf_ops", "onnx.json")) as f:
            onnx_opsets = json.load(f)["opsets"]

        for i in range(1, self.onnx_min_opset + 1):
            onnx_ops.extend(onnx_opsets[str(i)])

        for model_class in self.all_model_classes:
            model_op_names = set()

            with tf.Graph().as_default() as g:
                model = model_class(config)
                model.build_in_name_scope()

                for op in g.get_operations():
                    model_op_names.add(op.node_def.op)

            model_op_names = sorted(model_op_names)
            incompatible_ops = []

            for op in model_op_names:
                if op not in onnx_ops and op not in INTERNAL_OPS:
                    incompatible_ops.append(op)

            self.assertEqual(len(incompatible_ops), 0, incompatible_ops)

    # `tf2onnx` issue page: https://github.com/onnx/tensorflow-onnx/issues/2172
    # TODO: undo skip once a fix is done in `tf2onnx`
    @unittest.skip("`tf2onnx` broke with TF 2.13")
    @require_tf2onnx
    @slow
    def test_onnx_runtime_optimize(self):
        if not self.test_onnx:
            return

        import onnxruntime
        import tf2onnx

        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)
            model.build_in_name_scope()

            onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)

            onnxruntime.InferenceSession(onnx_model_proto.SerializeToString())

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

        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")
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
            for module_member in (getattr(module, module_member_name),)
            if isinstance(module_member, type)
            and 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(99, 32, name="shared")
                config.use_cache = inputs_dict.pop("use_cache", None)
                main_layer = main_layer_class(config, embed_tokens=shared)
            else:
                main_layer = main_layer_class(config)

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

            model = keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
            outputs = model(inputs_dict)

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

    def assert_outputs_same(self, after_outputs, outputs):
        # Make sure we don't have nans
        if isinstance(after_outputs, tf.Tensor):
            out_1 = after_outputs.numpy()
        elif isinstance(after_outputs, dict):
            out_1 = after_outputs[list(after_outputs.keys())[0]].numpy()
        else:
            out_1 = after_outputs[0].numpy()
        out_2 = outputs[0].numpy()
        self.assertEqual(out_1.shape, out_2.shape)
        out_1 = out_1[~np.isnan(out_1)]
        out_2 = out_2[~np.isnan(out_2)]
        max_diff = np.amax(np.abs(out_1 - out_2))
        self.assertLessEqual(max_diff, 1e-5)

    # Don't copy this method to model specific test file!
    # TODO: remove this method once the issues are all fixed!
    def _make_attention_mask_non_null(self, inputs_dict):
        """Make sure no sequence has all zeros as attention mask"""

        for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]:
            if k in inputs_dict:
                attention_mask = inputs_dict[k]

                # Make sure no all 0s attention masks - to avoid failure at this moment.
                # Put `1` at the beginning of sequences to make it still work when combining causal attention masks.
                # TODO: remove this line once a fix regarding large negative values for attention mask is done.
                attention_mask = tf.concat(
                    [tf.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], axis=-1
                )

                # Here we make the first sequence with all 0s as attention mask.
                # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative
                # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks.
                # TODO: enable this block once the large negative values thing is cleaned up.
                # (see https://github.com/huggingface/transformers/issues/14859)
                # attention_mask = tf.concat(
                #     [
                #         tf.zeros_like(attention_mask[:1], dtype=tf.int32),
                #         tf.cast(attention_mask[1:], dtype=tf.int32)
                #     ],
                #     axis=0
                # )

                inputs_dict[k] = attention_mask

    # Don't copy this method to model specific test file!
    # TODO: remove this method once the issues are all fixed!
    def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class):
        """For temporarily ignoring some failed test cases (issues to be fixed)"""

        tf_keys = {k for k, v in tf_outputs.items() if v is not None}
        pt_keys = {k for k, v in pt_outputs.items() if v is not None}

        key_differences = tf_keys.symmetric_difference(pt_keys)

        if model_class.__name__ in [
            "TFFlaubertWithLMHeadModel",
            "TFFunnelForPreTraining",
            "TFElectraForPreTraining",
            "TFXLMWithLMHeadModel",
        ]:
            for k in key_differences:
                if k in ["loss", "losses"]:
                    tf_keys.discard(k)
                    pt_keys.discard(k)
        elif model_class.__name__.startswith("TFGPT2"):
            # `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple.
            tf_keys.discard("past_key_values")
            pt_keys.discard("past_key_values")

        # create new outputs from the remaining fields
        new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys})
        new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys})

        return new_tf_outputs, new_pt_outputs

    @slow
    def test_compile_tf_model(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
            # Prepare our model
            model = model_class(config)
            # These are maximally general inputs for the model, with multiple None dimensions
            # Hopefully this will catch any conditionals that fail for flexible shapes
            functional_inputs = {
                key: keras.Input(shape=val.shape[1:], dtype=val.dtype, name=key)
                for key, val in model.input_signature.items()
                if key in model.dummy_inputs
            }
            outputs_dict = model(functional_inputs)

            hidden_states = outputs_dict[0]

            # Compile extended model
            functional_model = keras.Model(inputs=functional_inputs, outputs=hidden_states)
            model_out = functional_model.predict(model.dummy_inputs)  # Check we can pass inputs with the Keras API
            self.assertTrue(model_out is not None)
            with tempfile.TemporaryDirectory() as tmpdirname:
                functional_model.save(tmpdirname)  # Ensure we can save/export the whole functional model

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            inputs = self._prepare_for_class(inputs_dict, model_class)

            outputs_dict = model(inputs)

            inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
            outputs_keywords = model(**inputs_keywords)
            output_dict = outputs_dict[0].numpy()
            output_keywords = outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)

    def test_attention_outputs(self):
        if not self.has_attentions:
            self.skipTest(reason="Model does not output attentions")

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True
        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length)
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
        decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)

        def check_decoder_attentions_output(outputs):
            out_len = len(outputs)
            self.assertEqual(min(out_len % 2, out_len % 5), 0)  # differentiation due to newly added cross_attentions
            decoder_attentions = outputs.decoder_attentions
            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
            )

        def check_encoder_attentions_output(outputs):
            attentions = [
                t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
            ]
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
            )

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            config.output_hidden_states = False
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            out_len = len(outputs)
            self.assertEqual(config.output_hidden_states, False)
            check_encoder_attentions_output(outputs)

            if self.is_encoder_decoder:
                model = model_class(config)
                outputs = model(self._prepare_for_class(inputs_dict, model_class))
                self.assertEqual(config.output_hidden_states, False)
                check_decoder_attentions_output(outputs)

            # Check that output attentions can also be changed via the config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            self.assertEqual(config.output_hidden_states, False)
            check_encoder_attentions_output(outputs)

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            config.output_hidden_states = True
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))

            self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
            self.assertEqual(model.config.output_hidden_states, True)
            check_encoder_attentions_output(outputs)

    def test_headmasking(self):
        if not self.test_head_masking:
            return

        random.Random().seed(42)
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        random.Random().seed()

        inputs_dict["output_attentions"] = True
        config.output_hidden_states = True
        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)

            # Prepare head_mask
            def prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
                if i == 0:
                    return tf.concat(
                        (tf.zeros(1, dtype=tf.float32), tf.ones(attention_heads - 1, dtype=tf.float32)), 0
                    )
                elif i == num_hidden_layers - 1:
                    return tf.concat(
                        (tf.zeros(attention_heads - 1, dtype=tf.float32), tf.ones(1, dtype=tf.float32)), 0
                    )
                else:
                    return tf.ones(attention_heads, dtype=tf.float32)

            head_mask = tf.stack(
                [
                    prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
                    for i in range(config.num_hidden_layers)
                ],
                0,
            )

            inputs = self._prepare_for_class(inputs_dict, model_class).copy()
            inputs["head_mask"] = head_mask
            if model.config.is_encoder_decoder:
                signature = inspect.signature(model.call)
                arg_names = [*signature.parameters.keys()]
                if "decoder_head_mask" in arg_names:  # necessary differentiation because of T5 model
                    inputs["decoder_head_mask"] = head_mask
                if "cross_attn_head_mask" in arg_names:
                    inputs["cross_attn_head_mask"] = head_mask

            outputs = model(**inputs, return_dict=True)

            def check_attentions_validity(attentions):
                # Remove Nan
                for t in attentions:
                    self.assertLess(
                        (tf.math.reduce_sum(tf.cast(tf.math.is_nan(t), tf.float32))).numpy(), (tf.size(t) / 4).numpy()
                    )  # Check we don't have more than 25% nans (arbitrary)

                attentions = [
                    tf.where(tf.math.is_nan(t), 0.0, t) for t in attentions
                ]  # remove them (the test is less complete)

                self.assertAlmostEqual(tf.math.reduce_sum(attentions[0][..., 0, :, :]).numpy(), 0.0)
                self.assertNotEqual(tf.math.reduce_sum(attentions[0][..., -1, :, :]).numpy(), 0.0)
                if len(attentions) > 2:  # encoder-decodere models have only 2 layers in each modules
                    self.assertNotEqual(tf.math.reduce_sum(attentions[1][..., 0, :, :]).numpy(), 0.0)
                self.assertAlmostEqual(tf.math.reduce_sum(attentions[-1][..., -2, :, :]).numpy(), 0.0)
                self.assertNotEqual(tf.math.reduce_sum(attentions[-1][..., -1, :, :]).numpy(), 0.0)

            if model.config.is_encoder_decoder:
                check_attentions_validity(outputs.encoder_attentions)
                check_attentions_validity(outputs.decoder_attentions)
                if "cross_attn_head_mask" in arg_names:
                    check_attentions_validity(outputs.cross_attentions)
            else:
                check_attentions_validity(outputs.attentions)

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

        def check_hidden_states_output(config, inputs_dict, model_class):
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )

            if model.config.is_encoder_decoder:
                encoder_hidden_states = outputs.encoder_hidden_states
                decoder_hidden_states = outputs.decoder_hidden_states

                self.assertEqual(config.output_attentions, False)
                self.assertEqual(len(encoder_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(encoder_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )
                self.assertEqual(len(decoder_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(decoder_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )
            else:
                hidden_states = outputs.hidden_states
                self.assertEqual(config.output_attentions, False)
                self.assertEqual(len(hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(config, inputs_dict, model_class)

            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True
            check_hidden_states_output(config, inputs_dict, model_class)

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        text_in_text_out_models = (
            get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING)
            + get_values(TF_MODEL_FOR_MASKED_LM_MAPPING)
            + get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
        )
        speech_in_text_out_models = get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING)

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), keras.layers.Layer)

            legacy_text_in_text_out = model.get_lm_head() is not None
            if model_class in text_in_text_out_models or legacy_text_in_text_out:
                out_embeddings = model.get_output_embeddings()
                self.assertIsInstance(out_embeddings, keras.layers.Layer)
                bias = model.get_bias()
                if bias is not None:
                    self.assertIsInstance(bias, dict)
                    for _, v in bias.items():
                        self.assertIsInstance(v, tf.Variable)
            elif model_class in speech_in_text_out_models:
                out_embeddings = model.get_output_embeddings()
                self.assertIsInstance(out_embeddings, keras.layers.Layer)
                bias = model.get_bias()
                self.assertIsNone(bias)
            else:
                out_embeddings = model.get_output_embeddings()
                assert out_embeddings is None
                bias = model.get_bias()
                self.assertIsNone(bias)

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            first, second = (
                model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
                model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
            )
            out_1 = first.numpy()
            out_2 = second.numpy()
            out_1 = out_1[~np.isnan(out_1)]
            out_2 = out_2[~np.isnan(out_2)]
            max_diff = np.amax(np.abs(out_1 - out_2))
            self.assertLessEqual(max_diff, 1e-5)

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

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
            dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

            def recursive_check(tuple_object, dict_object):
                if isinstance(tuple_object, (list, tuple)):
                    for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                        recursive_check(tuple_iterable_value, dict_iterable_value)
                elif tuple_object is None:
                    return
                else:
                    self.assertTrue(
                        all(tf.equal(tuple_object, dict_object)),
                        msg=(
                            "Tuple and dict output are not equal. Difference:"
                            f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
                        ),
                    )

                recursive_check(tuple_output, dict_output)

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

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

            if self.has_attentions:
                tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
                dict_inputs = self._prepare_for_class(inputs_dict, model_class)
                check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

            # Not all models accept "labels" in the forward pass (yet :) )
            if "labels" in inspect.signature(model.call).parameters.keys():
                tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                check_equivalence(model, tuple_inputs, dict_inputs)

                tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

                if self.has_attentions:
                    tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                    dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                    check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

                    tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                    dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                    check_equivalence(
                        model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
                    )

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

        for model_class in self.all_model_classes:
            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)

            model(inputs)

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

        def prepare_numpy_arrays(inputs_dict):
            inputs_np_dict = {}
            for k, v in inputs_dict.items():
                if tf.is_tensor(v):
                    inputs_np_dict[k] = v.numpy()
                else:
                    inputs_np_dict[k] = np.array(k)

            return inputs_np_dict

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

            inputs = self._prepare_for_class(inputs_dict, model_class)
            inputs_np = prepare_numpy_arrays(inputs)

            output_for_dict_input = model(inputs_np)
            output_for_kw_input = model(**inputs_np)
            self.assert_outputs_same(output_for_dict_input, output_for_kw_input)

    def test_valid_input_signature_and_dummies(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            call_args = inspect.signature(model.call).parameters
            for key in model.input_signature:
                self.assertIn(key, call_args)
            for key in model.dummy_inputs:
                self.assertIn(key, call_args)

    def test_resize_token_embeddings(self):
        # TODO (joao): after the embeddings refactor is complete, rework this test so as to rely exclusively on
        # keras.layers.Embedding

        if not self.test_resize_embeddings:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def _get_word_embedding_weight(model, embedding_layer):
            if isinstance(embedding_layer, keras.layers.Embedding):
                # builds the embeddings layer
                model.build_in_name_scope()
                return embedding_layer.embeddings
            else:
                return model._get_word_embedding_weight(embedding_layer)

        for model_class in self.all_model_classes:
            for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
                # build the embeddings
                model = model_class(config=copy.deepcopy(config))  # `resize_token_embeddings` mutates `config`
                old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                old_bias = model.get_bias()
                old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
                # reshape the embeddings
                model.resize_token_embeddings(size)
                new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                new_bias = model.get_bias()
                new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())

                # check that the resized embeddings size matches the desired size.
                assert_size = size if size is not None else config.vocab_size
                self.assertEqual(new_input_embeddings.shape[0], assert_size)

                # check that weights remain the same after resizing
                models_equal = True
                for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
                    if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                        models_equal = False
                self.assertTrue(models_equal)

                if old_bias is not None and new_bias is not None:
                    for old_weight, new_weight in zip(old_bias.values(), new_bias.values()):
                        self.assertEqual(new_weight.shape[-1], assert_size)

                        models_equal = True
                        for p1, p2 in zip(tf.squeeze(old_weight), tf.squeeze(new_weight)):
                            if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                                models_equal = False
                        self.assertTrue(models_equal)

                if old_output_embeddings is not None and new_output_embeddings is not None:
                    self.assertEqual(new_output_embeddings.shape[0], assert_size)
                    self.assertEqual(new_output_embeddings.shape[1], old_output_embeddings.shape[1])

                    models_equal = True
                    for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
                        if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                            models_equal = False
                    self.assertTrue(models_equal)

    # TODO (Joao): this test is not slow, but it's tagged as such to keep track of failures on the scheduled CI runs,
    # while passing push CI. Fix the underlying issues and remove the tag.
    @slow
    def test_save_load_after_resize_token_embeddings(self):
        if not self.test_resize_embeddings:
            return
        config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            # create a model with resized (expended) embeddings
            new_tokens_size = 10
            old_total_size = config.vocab_size
            new_total_size = old_total_size + new_tokens_size
            model = model_class(config=copy.deepcopy(config))  # `resize_token_embeddings` mutates `config`
            model.build_in_name_scope()
            model.resize_token_embeddings(new_total_size)

            # fetch the output for an input exclusively made of new members of the vocabulary
            inputs_dict = copy.deepcopy(original_inputs_dict)
            ids_feat_name = None
            if "input_ids" in inputs_dict:
                ids_feat_name = "input_ids"
            elif "decoder_input_ids" in inputs_dict:
                ids_feat_name = "decoder_input_ids"
            else:
                assert False, "No input ids feature found in the inputs dict"

            new_vocab_input_ids = ids_tensor(inputs_dict[ids_feat_name].shape, new_tokens_size)
            new_vocab_input_ids += old_total_size
            inputs_dict[ids_feat_name] = new_vocab_input_ids
            if "input_ids" in inputs_dict:
                inputs_dict["input_ids"] = new_vocab_input_ids
            if "decoder_input_ids" in inputs_dict:
                inputs_dict["decoder_input_ids"] = new_vocab_input_ids
            prepared_inputs = self._prepare_for_class(inputs_dict, model_class)
            outputs = model(**prepared_inputs)

            # save and load the model
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=False)
                model = model_class.from_pretrained(tmpdirname)
                restored_model_outputs = model(**prepared_inputs)

                # check that the output for the restored model is the same
                self.assert_outputs_same(restored_model_outputs, outputs)

    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
        reason="This test always passes on CPU.",
    )
    def test_embeddings_out_of_bounds_raise_exception(self):
        # TF embeddings layers don't raise an exception when an index is out of bounds on GPU, so we manually raise it.
        # This test should only fail on GPU for models where we haven't added the safety check.
        if not self.test_resize_embeddings:
            return
        config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config=config)
            inputs_dict = copy.deepcopy(original_inputs_dict)
            if "input_ids" in inputs_dict:
                inputs_dict["input_ids"] = inputs_dict["input_ids"] * int(1e9)
            if "decoder_input_ids" in inputs_dict:
                inputs_dict["decoder_input_ids"] = inputs_dict["decoder_input_ids"] * int(1e9)
            prepared_inputs = self._prepare_for_class(inputs_dict, model_class)
            with self.assertRaises(tf.errors.InvalidArgumentError):
                model(**prepared_inputs)

    def test_loss_computation(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            # The number of elements in the loss should be the same as the number of elements in the label
            prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
            added_label_names = sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)
            if not added_label_names:
                continue  # This test is only for models with easily-separable labels
            added_label = prepared_for_class[added_label_names[0]]
            expected_loss_size = added_label.shape.as_list()[:1]

            # Test that model correctly compute the loss with kwargs
            prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
            possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"}
            input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
            model_input = prepared_for_class.pop(input_name)

            outputs = model(model_input, **prepared_for_class)
            if not isinstance(outputs, ModelOutput) or not hasattr(outputs, "loss"):
                continue

            loss = outputs.loss
            self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

            # Test that model correctly compute the loss when we mask some positions
            prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
            possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"}
            input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
            model_input = prepared_for_class.pop(input_name)
            if "labels" in prepared_for_class:
                labels = prepared_for_class["labels"].numpy()
                if len(labels.shape) > 1 and labels.shape[1] != 1:
                    labels[0] = -100
                    prepared_for_class["labels"] = tf.convert_to_tensor(labels)
                    loss = model(model_input, **prepared_for_class)[0]
                    self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
                    self.assertTrue(not np.any(np.isnan(loss.numpy())))

            # Test that model correctly compute the loss with a dict
            prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
            loss = model(prepared_for_class)[0]
            self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

            # Test that model correctly compute the loss with a tuple
            prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)

            # Get keys that were added with the _prepare_for_class function
            label_keys = prepared_for_class.keys() - inputs_dict.keys()
            signature = inspect.signature(model.call).parameters
            signature_names = list(signature.keys())

            # Create a dictionary holding the location of the tensors in the tuple
            tuple_index_mapping = {0: input_name}
            for label_key in label_keys:
                label_key_index = signature_names.index(label_key)
                tuple_index_mapping[label_key_index] = label_key
            sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
            # Initialize a list with their default values, update the values and convert to a tuple
            list_input = []

            for name in signature_names:
                if name != "kwargs":
                    list_input.append(signature[name].default)

            for index, value in sorted_tuple_index_mapping:
                list_input[index] = prepared_for_class[value]

            tuple_input = tuple(list_input)

            # Send to model
            loss = model(tuple_input[:-1])[0]

            self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

    def check_keras_fit_results(self, val_loss1, val_loss2, atol=1e-2, rtol=1e-3):
        self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))

    @slow
    def test_keras_fit(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            # Test that model correctly compute the loss with kwargs
            prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
            # We also remove "return_loss" as this is covered by the train_step when using fit()
            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", "return_loss")
            }
            if "labels" in prepared_for_class and "decoder_input_ids" in prepared_for_class:
                del prepared_for_class["decoder_input_ids"]

            accuracy_classes = [
                "ForPreTraining",
                "ForCausalLM",
                "ForMaskedLM",
                "ForQuestionAnswering",
                "ForMultipleChoice",
                "ForSequenceClassification",
                "ForTokenClassification",
                "ForNextSentencePrediction",
                "LMHeadModel",
            ]
            for accuracy_class in accuracy_classes:
                if model.__class__.__name__.endswith(accuracy_class):
                    metrics = [keras.metrics.SparseCategoricalAccuracy()]
                    break
            else:
                metrics = []

            if hasattr(self.model_tester, "batch_size"):
                sample_weight = tf.convert_to_tensor([0.5] * self.model_tester.batch_size, dtype=tf.float32)
            else:
                sample_weight = None
            # Build the model so we can get some constant weights and check outputs
            outputs = model(prepared_for_class)
            if getattr(outputs, "loss", None) is None:
                continue
            model_weights = model.get_weights()

            # Run eagerly to save some expensive compilation times
            model.compile(optimizer=keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics)
            # Make sure the model fits without crashing regardless of where we pass the labels
            history1 = model.fit(
                prepared_for_class,
                validation_data=prepared_for_class,
                sample_weight=sample_weight,
                steps_per_epoch=1,
                validation_steps=1,
                shuffle=False,
            )
            val_loss1 = history1.history["val_loss"][0]
            self.assertTrue(not isnan(val_loss1))
            accuracy1 = {key: val[0] for key, val in history1.history.items() if key.endswith("accuracy")}

            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))
            if len(label_names) == 0:
                # The next tests only make sense for models with separate inputs and labels, and do not make
                # sense for models that don't clearly distinguish between the two (e.g. CLIP)
                return
            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)

            # We reinitialize the model here even though our learning rate was zero
            # because BatchNorm updates weights by means other than gradient descent.
            model.set_weights(model_weights)

            history2 = model.fit(
                inputs_minus_labels,
                labels,
                validation_data=(inputs_minus_labels, labels),
                sample_weight=sample_weight,
                steps_per_epoch=1,
                validation_steps=1,
                shuffle=False,
            )
            val_loss2 = history2.history["val_loss"][0]
            self.assertTrue(not isnan(val_loss2))
            accuracy2 = {key: val[0] for key, val in history2.history.items() if key.endswith("accuracy")}
            self.check_keras_fit_results(val_loss1, val_loss2)
            self.assertEqual(history1.history.keys(), history2.history.keys())
            for key in history1.history.keys():
                if not key.startswith("val_"):
                    self.assertTrue("val_" + key in history1.history.keys(), "Outputs differ in train/test step!")
            if metrics:
                self.assertTrue(len(accuracy1) == len(accuracy2) > 0, "Missing metrics!")

    def test_int_support(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            prepared_for_class = self._prepare_for_class(
                inputs_dict.copy(),
                model_class,
                return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False,
            )
            if not any(
                tensor.dtype.is_integer for tensor in prepared_for_class.values() if isinstance(tensor, tf.Tensor)
            ):
                return  # No integer inputs means no need for this test

            prepared_for_class = {
                key: tf.cast(tensor, tf.int64) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor
                for key, tensor in prepared_for_class.items()
            }
            model = model_class(config)
            model(**prepared_for_class)  # No assertion, we're just checking this doesn't throw an error
            int32_prepared_for_class = {
                key: tf.cast(tensor, tf.int32) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor
                for key, tensor in prepared_for_class.items()
            }
            model(**int32_prepared_for_class)  # No assertion, we're just checking this doesn't throw an error

            # After testing that the model accepts all int inputs, confirm that its dummies are int32
            for key, tensor in model.dummy_inputs.items():
                self.assertTrue(
                    isinstance(tensor, tf.Tensor) or keras.backend.is_keras_tensor(tensor),
                    "Dummy inputs should be tf.Tensor!",
                )
                if tensor.dtype.is_integer:
                    self.assertTrue(tensor.dtype == tf.int32, "Integer dummy inputs should be tf.int32!")

            # Also confirm that the input_signature uses int32
            for key, tensor_spec in model.input_signature.items():
                if tensor_spec.dtype.is_integer:
                    self.assertTrue(tensor_spec.dtype == tf.int32, "Input signatures should use tf.int32 for ints!")

    def test_load_with_mismatched_shapes(self):
        if not self.test_mismatched_shapes:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            if model_class not in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
                continue

            with self.subTest(msg=f"Testing {model_class}"):
                with tempfile.TemporaryDirectory() as tmp_dir:
                    model = model_class(config)
                    inputs = self._prepare_for_class(inputs_dict, model_class)
                    _ = model(**inputs)
                    model.save_pretrained(tmp_dir)

                    # Fails when we don't set ignore_mismatched_sizes=True
                    with self.assertRaises(ValueError):
                        new_model = TFAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
                    with self.assertRaises(ValueError):
                        new_model_without_prefix = TFAutoModel.from_pretrained(tmp_dir, vocab_size=10)

                    logger = logging.get_logger("transformers.modeling_tf_utils")
                    with CaptureLogger(logger) as cl:
                        new_model = TFAutoModelForSequenceClassification.from_pretrained(
                            tmp_dir, num_labels=42, ignore_mismatched_sizes=True
                        )
                    self.assertIn("the shapes did not match", cl.out)

                    logits = new_model(**inputs).logits
                    self.assertEqual(logits.shape[1], 42)

                    with CaptureLogger(logger) as cl:
                        new_model_without_prefix = TFAutoModel.from_pretrained(
                            tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
                        )
                    self.assertIn("the shapes did not match", cl.out)

                    # Although Tf models always have a prefix pointing to `MainLayer`,
                    # we still add this "without prefix" test to keep a consistency between tf and pt tests.
                    input_ids = ids_tensor((2, 8), 10)
                    if self.is_encoder_decoder:
                        new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
                    else:
                        new_model_without_prefix(input_ids)

    def test_model_main_input_name(self):
        for model_class in self.all_model_classes:
            model_signature = inspect.signature(getattr(model_class, "call"))
            # The main input is the name of the argument after `self`
            observed_main_input_name = list(model_signature.parameters.keys())[1]
            self.assertEqual(model_class.main_input_name, observed_main_input_name)

    def test_dataset_conversion(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=False)
            if "labels" in tf_inputs_dict:
                return  # This is some kinda funky decoder model that needs labels in its forward pass
            tf_inputs_dict = {
                key: val
                for key, val in tf_inputs_dict.items()
                if "head_mask" not in key and isinstance(val, tf.Tensor)
            }
            tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0]  # Use a random other tensor
            input_dataset = Dataset.from_dict(tf_inputs_dict)
            tf_dataset = model.prepare_tf_dataset(
                input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False
            )
            test_batch = next(iter(tf_dataset))
            if isinstance(test_batch, tf.Tensor):
                self.assertEqual(len(test_batch), len(input_dataset))  # Assert we didn't lose any data
            elif isinstance(test_batch, dict):
                # Assert we discarded the unwanted extra column but kept everything else
                self.assertEqual(len(test_batch), len(input_dataset.features) - 1)
                self.assertNotIn("extra_unwanted_column", test_batch)
                for tensor in test_batch.values():
                    self.assertTrue(isinstance(tensor, tf.Tensor))
                    self.assertEqual(len(tensor), len(input_dataset))  # Assert we didn't lose any data
            model(test_batch, training=False)

            if "labels" in inspect.signature(model_class.call).parameters.keys():
                tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                if "labels" not in tf_inputs_dict:
                    return  # This model isn't giving us labels after all, don't try training with it
                tf_inputs_dict = {
                    key: val
                    for key, val in tf_inputs_dict.items()
                    if "head_mask" not in key and isinstance(val, tf.Tensor)
                }
                tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0]  # Use a random other tensor
                input_dataset = Dataset.from_dict(tf_inputs_dict)
                tf_dataset = model.prepare_tf_dataset(
                    input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False
                )
                test_batch, test_batch_labels = next(iter(tf_dataset))
                self.assertGreater(len(test_batch_labels), 0)  # Assert the labels are present
                feature_columns = 1 if isinstance(test_batch, tf.Tensor) else len(test_batch)
                label_columns = 1 if isinstance(test_batch_labels, tf.Tensor) else len(test_batch_labels)
                # Assert we discarded the unwanted extra column but kept everything else
                self.assertEqual(feature_columns + label_columns, len(input_dataset.features) - 1)
                if isinstance(test_batch, dict):
                    self.assertNotIn("extra_unwanted_column", test_batch)
                if isinstance(test_batch_labels, dict):
                    self.assertNotIn("extra_unwanted_column", test_batch_labels)
                model.compile(optimizer="sgd", run_eagerly=True)
                model.train_on_batch(test_batch, test_batch_labels)


def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
    """Creates a random int32 tensor of the shape within the vocab size."""
    if rng is None:
        rng = random.Random()

    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(rng.randint(0, vocab_size - 1))

    output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32)

    return output


def random_attention_mask(shape, rng=None, name=None, dtype=None):
    attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype)
    # Mark the first token as 1 (matches behaviour of PyTorch/Flax function)
    attn_mask = tf.concat([tf.ones_like(attn_mask[:, :1]), attn_mask[:, 1:]], axis=1)
    return attn_mask


def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
    """Creates a random float32 tensor"""
    if rng is None:
        rng = random.Random()

    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(rng.random() * scale)

    return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape)