# Copyright 2026 the HuggingFace Team. All rights reserved.
#
# 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,
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"""Testing suite for the PyTorch Gemma4Unified model."""

import copy
import string
import tempfile
import unittest
from unittest.mock import patch

import pytest
from parameterized import parameterized

from transformers import (
    AutoTokenizer,
    Gemma4UnifiedConfig,
    Gemma4UnifiedTextConfig,
    is_torch_available,
)
from transformers.testing_utils import (
    Expectations,
    cleanup,
    require_torch,
    require_torch_accelerator,
    require_torch_multi_gpu,
    slow,
    torch_device,
)

from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_processing_common import url_to_local_path


if is_torch_available():
    import torch

    from transformers import (
        AutoModelForCausalLM,
        Gemma4UnifiedForCausalLM,
        Gemma4UnifiedForConditionalGeneration,
        Gemma4UnifiedModel,
        Gemma4UnifiedProcessor,
        Gemma4UnifiedTextModel,
        PreTrainedModel,
        set_seed,
    )
    from transformers.models.gemma4_unified.modeling_gemma4_unified import Gemma4UnifiedRMSNorm


def _normalize_text(text):
    text = text.lower()
    translator = str.maketrans("", "", string.punctuation)
    text = text.translate(translator)
    return text


class Gemma4UnifiedTextModelTester(CausalLMModelTester):
    if is_torch_available():
        config_class = Gemma4UnifiedTextConfig
        base_model_class = Gemma4UnifiedTextModel
        causal_lm_class = Gemma4UnifiedForCausalLM

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.num_hidden_layers = 4  # override to correctly test sharing cache pattern
        self.num_kv_shared_layers = 2  # important to override
        self.layer_types = [
            "sliding_attention",
            "full_attention",
            "sliding_attention",
            "full_attention",
        ]  # similarly we want to test sharing on both types
        self.global_head_dim = self.head_dim  # gemma4 use a different head_dim for full and sliding layers

        # Test if bidirectional image mask path works
        self.use_bidirectional_attention = "vision"


@require_torch
class Gemma4UnifiedTextModelTest(CausalLMModelTest, unittest.TestCase):
    model_tester_class = Gemma4UnifiedTextModelTester
    # used in `test_torch_compile_for_training`
    _torch_compile_train_cls = Gemma4UnifiedForCausalLM if is_torch_available() else None

    @unittest.skip("We need 4 layers to correctly test cache sharing.")
    def test_num_layers_is_small(self):
        pass

    @unittest.skip("Gemma4Unified uses different rope per layer type, which is not compatible with this test")
    def test_model_rope_scaling_frequencies(self):
        pass

    @parameterized.expand([("linear",), ("dynamic",), ("yarn",)])
    @unittest.skip("Gemma4Unified uses different rope per layer type, which is not compatible with this test")
    def test_model_rope_scaling_from_config(self):
        pass

    @unittest.skip(
        "Flaky on CI, but not locally on Mac. If model is set to fp32 instead of bf16, not flaky anymore."
        "TODO Cyril/Anton: investigate where the loss of precision between bf16 and fp32 comes from."
    )
    def test_sdpa_padding_matches_padding_free_with_position_ids(self):
        pass

    @unittest.skip(
        "Same as gemma4: Fails after fully removing the unused weights, even if `forward` is exactly the same. Investigate why."
    )
    def test_tp_generation_quantized(self):
        pass

    def test_flash_attn_2_equivalence(self):
        """Override: Exchange RMS norm with identity as it creates too big shifts otherwise"""

        def identity_forward(self, hidden_states):
            return hidden_states

        with patch.object(Gemma4UnifiedRMSNorm, "forward", identity_forward):
            super().test_flash_attn_2_equivalence()

    def flash_attn_inference_equivalence(
        self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2
    ) -> None:
        """Override: Exchange RMS norm with identity as it creates too big shifts otherwise"""

        def identity_forward(self, hidden_states):
            return hidden_states

        with patch.object(Gemma4UnifiedRMSNorm, "forward", identity_forward):
            super().flash_attn_inference_equivalence(attn_implementation, padding_side, atol, rtol)


class Gemma4UnifiedAudio2TextModelTester:
    def __init__(
        self,
        parent,
        image_token_id=4,
        boi_token_id=5,
        eoi_token_id=6,
        audio_token_id=7,
        boa_token_id=8,
        eoa_token_index=9,
        video_token_id=10,
        seq_length=50,
        audio_seq_length=50,
        audio_num_channels=32,
        is_training=True,
        audio_config={"audio_embed_dim": 32},
    ):
        self.parent = parent
        self.image_token_id = image_token_id
        self.boi_token_id = boi_token_id
        self.eoi_token_id = eoi_token_id
        self.audio_token_id = audio_token_id
        self.boa_token_id = boa_token_id
        self.eoa_token_index = eoa_token_index
        self.video_token_id = video_token_id
        self.llm_tester = Gemma4UnifiedTextModelTester(self.parent)
        self.llm_tester.use_bidirectional_attention = None
        self.text_config = self.llm_tester.get_config()
        self.audio_config = audio_config
        self.seq_length = seq_length
        self.audio_seq_length = audio_seq_length
        self.audio_num_channels = audio_num_channels
        self.pad_token_id = self.text_config.pad_token_id

        self.num_hidden_layers = self.text_config.num_hidden_layers
        self.vocab_size = self.text_config.vocab_size
        self.hidden_size = self.text_config.hidden_size
        self.num_attention_heads = self.text_config.num_attention_heads
        self.is_training = is_training

        self.batch_size = 3
        self.encoder_seq_length = seq_length

    def get_config(self):
        return Gemma4UnifiedConfig(
            text_config=self.text_config,
            vision_config=None,
            audio_config=self.audio_config,
            image_token_id=self.image_token_id,
            boi_token_id=self.boi_token_id,
            eoi_token_id=self.eoi_token_id,
            audio_token_id=self.audio_token_id,
            boa_token_id=self.boa_token_id,
            eoa_token_index=self.eoa_token_index,
            video_token_id=self.video_token_id,
        )

    def prepare_config_and_inputs(self):
        input_features = floats_tensor([self.batch_size, self.audio_seq_length, self.audio_num_channels])
        input_features_mask = torch.ones(self.batch_size, self.audio_seq_length, dtype=torch.bool)
        config = self.get_config()
        return config, input_features, input_features_mask

    def prepare_config_and_inputs_for_common(self):
        config, input_features, input_features_mask = self.prepare_config_and_inputs()
        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
        attention_mask = input_ids.ne(self.pad_token_id).to(torch_device)

        # Ensure no tokens accidentally match special token IDs
        for token_id in [config.image_token_id, config.video_token_id, config.audio_token_id]:
            input_ids[input_ids == token_id] = self.pad_token_id

        # For the unified model, there is no subsampling.
        # We need as many placeholder tokens as audio features.
        num_audio_tokens = self.audio_seq_length
        input_ids[:, :num_audio_tokens] = config.audio_token_id

        inputs_dict = {
            "input_features": input_features,
            "input_features_mask": input_features_mask,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "mm_token_type_ids": torch.zeros_like(input_ids),
        }
        return config, inputs_dict


@require_torch
class Gemma4UnifiedAudio2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (Gemma4UnifiedModel, Gemma4UnifiedForConditionalGeneration) if is_torch_available() else ()
    all_generative_model_classes = (Gemma4UnifiedForConditionalGeneration,) if is_torch_available() else ()
    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = Gemma4UnifiedAudio2TextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Gemma4UnifiedConfig, hidden_size=37)
        self.skip_mm_output_format()

    def skip_mm_output_format(self):
        skippable_tests = [
            "test_get_image_features_hidden_states",
            "test_get_image_features_attentions",
            "test_get_video_features_hidden_states",
            "test_get_video_features_attentions",
            "test_get_audio_features_hidden_states",
            "test_get_audio_features_attentions",
            "test_get_image_features_output",
            "test_get_video_features_output",
            "test_get_audio_features_output",
        ]

        for test in skippable_tests:
            if self._testMethodName.startswith(test):
                self.skipTest(reason="Gemma4 unified does not collect any hidden states or attentions (no mm tower)")

    @unittest.skip("We need 4 layers to correctly test cache sharing.")
    def test_num_layers_is_small(self):
        pass

    @unittest.skip("Conversions only happen when a vision embedder is included!")
    def test_reverse_loading_mapping(self, check_keys_were_modified=False, skip_base_model=False):
        pass

    def flash_attn_inference_equivalence(
        self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2
    ) -> None:
        """Override: Exchange RMS norm with identity as it creates too big shifts otherwise"""

        def identity_forward(self, hidden_states):
            return hidden_states

        with patch.object(Gemma4UnifiedRMSNorm, "forward", identity_forward):
            super().flash_attn_inference_equivalence(attn_implementation, padding_side, atol, rtol)


class Gemma4UnifiedVision2TextModelTester:
    def __init__(
        self,
        parent,
        mm_tokens_per_image=2,
        image_token_id=4,
        video_token_id=7,
        audio_token_id=8,
        boi_token_id=5,
        eoi_token_id=6,
        seq_length=25,
        is_training=True,
        vision_config={
            "use_labels": True,
            "mm_embed_dim": 64,
            "output_proj_dims": 64,
            "image_size": 20,
            "patch_size": 5,
            "num_channels": 3,
            "is_training": True,
            "initializer_range": 0.02,
            "pooling_kernel_size": 2,
        },
    ):
        self.parent = parent
        # `image_token_id` is set to 0 to pass "resize_embeddings" test, do not modify
        self.mm_tokens_per_image = mm_tokens_per_image
        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        self.audio_token_id = audio_token_id
        self.boi_token_id = boi_token_id
        self.eoi_token_id = eoi_token_id
        self.llm_tester = Gemma4UnifiedTextModelTester(self.parent)
        self.text_config = self.llm_tester.get_config()
        self.vision_config = vision_config
        self.seq_length = seq_length
        self.pad_token_id = self.text_config.pad_token_id

        self.num_hidden_layers = self.text_config.num_hidden_layers
        self.vocab_size = self.text_config.vocab_size
        self.hidden_size = self.text_config.hidden_size
        self.num_attention_heads = self.text_config.num_attention_heads
        self.is_training = is_training

        self.batch_size = 3
        self.num_channels = vision_config["num_channels"]
        self.image_size = vision_config["image_size"]
        self.encoder_seq_length = seq_length

    def get_config(self):
        return Gemma4UnifiedConfig(
            text_config=self.text_config,
            vision_config=self.vision_config,
            image_token_id=self.image_token_id,
            video_token_id=self.video_token_id,
            audio_token_id=self.audio_token_id,
            boi_token_id=self.boi_token_id,
            eoi_token_id=self.eoi_token_id,
            mm_tokens_per_image=self.mm_tokens_per_image,
        )

    def prepare_config_and_inputs(self):
        config = self.get_config()

        # (num_images, max_num_patches, model_patch_size * model_patch_size * num_channels)
        pixel_values = floats_tensor(
            [
                self.batch_size,
                self.vision_config["image_size"],
                config.vision_config.model_patch_size
                * config.vision_config.model_patch_size
                * self.vision_config["num_channels"],
            ]
        )
        # (num_images, max_num_patches, 2) for height/width positions. Let it be all ones for testign
        pixel_position_ids = torch.ones(self.vision_config["image_size"], device=torch_device, dtype=torch.long)
        pixel_position_ids = pixel_position_ids[None, :, None].repeat(self.batch_size, 1, 2)

        return config, pixel_values, pixel_position_ids

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, pixel_position_ids = config_and_inputs
        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
        attention_mask = input_ids.ne(self.pad_token_id).to(torch_device)

        # Ensure no tokens accidentally match special token IDs
        for token_id in [config.image_token_id, config.video_token_id, config.audio_token_id]:
            input_ids[input_ids == token_id] = self.pad_token_id

        num_image_tokens = 1
        pixel_values = pixel_values[:, :num_image_tokens, :]
        pixel_position_ids = pixel_position_ids[:, :num_image_tokens, :]
        input_ids[:, :num_image_tokens] = config.image_token_id

        mm_token_type_ids = torch.zeros_like(input_ids)
        mm_token_type_ids[input_ids == config.image_token_id] = 1

        inputs_dict = {
            "pixel_values": pixel_values,
            "image_position_ids": pixel_position_ids,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "mm_token_type_ids": mm_token_type_ids,
        }
        return config, inputs_dict


@require_torch
class Gemma4UnifiedVision2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (Gemma4UnifiedModel, Gemma4UnifiedForConditionalGeneration) if is_torch_available() else ()
    all_generative_model_classes = (Gemma4UnifiedForConditionalGeneration,) if is_torch_available() else ()
    additional_model_inputs = ["mm_token_type_ids"]
    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = Gemma4UnifiedVision2TextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Gemma4UnifiedConfig, hidden_size=37)
        self.skip_mm_output_format()

    def skip_mm_output_format(self):
        skippable_tests = [
            "test_get_image_features_hidden_states",
            "test_get_image_features_attentions",
            "test_get_video_features_hidden_states",
            "test_get_video_features_attentions",
            "test_get_audio_features_hidden_states",
            "test_get_audio_features_attentions",
            "test_get_image_features_output",
            "test_get_video_features_output",
            "test_get_audio_features_output",
        ]

        for test in skippable_tests:
            if self._testMethodName.startswith(test):
                self.skipTest(reason="Gemma4 unified does not collect any hidden states or attentions (no mm tower)")

    @unittest.skip("We need 4 layers to correctly test cache sharing.")
    def test_num_layers_is_small(self):
        pass

    @unittest.skip("We use vision based masks that use `block_sequence_ids` which force mask materialization")
    def test_sdpa_can_dispatch_on_flash(self):
        pass

    def flash_attn_inference_equivalence(
        self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2
    ) -> None:
        """
        Overriden to allow passing image position ids as it's mandatory in the vision portion
        #
        Exchange RMS norm with identity as it creates too big shifts otherwise
        """

        def identity_forward(self, hidden_states):
            return hidden_states

        with patch.object(Gemma4UnifiedRMSNorm, "forward", identity_forward):
            if not self.has_attentions:
                self.skipTest(reason="Model architecture does not support attentions")

            # This flag is used to know if the test was skipped for all `self.all_model_classes` or not
            _has_run_at_least_one_model = False

            for model_class in self.all_model_classes:
                # Custom kernel which needs the mask interface to be properly usable on these models
                if not model_class._supports_attention_backend and not attn_implementation.startswith(
                    "flash_attention"
                ):
                    continue

                # Set seed for deterministic test - ensures reproducible model initialization and inputs
                set_seed(42)
                config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

                # flash attention variants does not always support arbitrary headim
                config = self._prepare_config_headdim(config, 16)

                # forcing the prefill size to go over sliding window size to check for SWA correctness
                if getattr(config, "sliding_window", None):
                    config.sliding_window = 2

                model = model_class(config)
                if not all(
                    submodel._supports_flash_attn
                    for submodel in model.modules()
                    if isinstance(submodel, PreTrainedModel)
                ):
                    continue

                # Some models only support a sub set of all FA implementations
                valid_fa_implementations = model._compatible_flash_implementations
                if valid_fa_implementations is not None and attn_implementation not in valid_fa_implementations:
                    continue

                # If we end up here, at least one model class was not skipped
                _has_run_at_least_one_model = True
                with tempfile.TemporaryDirectory() as tmpdirname:
                    # Save the model so we can reload with correct attention
                    model.save_pretrained(tmpdirname)

                    # Create first inputs without attention mask
                    main_input = inputs_dict[model.main_input_name]
                    # Only keep first batch sequence
                    if isinstance(main_input, torch.Tensor):
                        main_input = main_input[:1]
                        # Fix the dtype
                        if torch.is_floating_point(main_input):
                            main_input = main_input.to(torch.bfloat16)
                    first_inputs = {model.main_input_name: main_input, "output_hidden_states": True}
                    # Some models have main input name which is different from input_ids, but require input_ids... e.g. BarkFine
                    if model.main_input_name != "input_ids" and "input_ids" in inputs_dict:
                        first_inputs["input_ids"] = inputs_dict["input_ids"][:1]
                    # If we have some pixel values, use them as well
                    if model.main_input_name != "pixel_values" and "pixel_values" in inputs_dict:
                        # NOTE: this fixes qwen2_5_vl/omni because test break w/ pixel values
                        if "image_grid_thw" in inputs_dict:
                            continue
                        first_inputs["pixel_values"] = inputs_dict["pixel_values"][:1].to(torch.bfloat16)
                    # Some VLMs require image_sizes alongside pixel_values, e.g. lighton_ocr, llava_onevision
                    if "image_sizes" in inputs_dict:
                        first_inputs["image_sizes"] = inputs_dict["image_sizes"][:1]
                    # Key change: Allow image position ids to be passed as well
                    if "image_position_ids" in inputs_dict:
                        first_inputs["image_position_ids"] = inputs_dict["image_position_ids"][:1]
                    if model.config.is_encoder_decoder:
                        decoder_input_ids = inputs_dict.get("decoder_input_ids", first_inputs.get("input_ids"))
                        if decoder_input_ids is not None:
                            first_inputs["decoder_input_ids"] = decoder_input_ids[:1]

                    # Create attention mask with padding
                    dummy_attention_mask = inputs_dict.get("attention_mask", None)
                    if dummy_attention_mask is not None:
                        dummy_attention_mask = dummy_attention_mask[:1]
                        if padding_side == "left":
                            dummy_attention_mask[:, 1:] = 1
                            dummy_attention_mask[:, 0] = 0
                        else:
                            dummy_attention_mask[:, :-1] = 1
                            dummy_attention_mask[:, -1] = 0

                    # Create second inputs with attention mask and padding
                    second_inputs = copy.deepcopy(first_inputs)
                    if dummy_attention_mask is not None:
                        second_inputs["attention_mask"] = dummy_attention_mask
                        if model.config.is_encoder_decoder:
                            second_inputs["decoder_attention_mask"] = dummy_attention_mask

                    # Use prepare for class to account for special attributes (e.g. in QnA models)
                    first_inputs = self._prepare_for_class(first_inputs, model_class)
                    first_inputs = {
                        k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in first_inputs.items()
                    }
                    second_inputs = self._prepare_for_class(second_inputs, model_class)
                    second_inputs = {
                        k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in second_inputs.items()
                    }

                    model = model_class.from_pretrained(
                        tmpdirname, dtype=torch.bfloat16, attn_implementation="eager", device_map=torch_device
                    )

                    def _get_output_logits(outputs):
                        if "hidden_states" in outputs:
                            return outputs.hidden_states[-1]
                        elif model.config.is_encoder_decoder:
                            return outputs.decoder_hidden_states[-1]
                        elif "logits_per_image" in outputs:
                            return outputs.logits_per_image
                        elif "logits_per_video" in outputs:
                            return outputs.logits_per_video
                        else:
                            return outputs.logits

                    # First run without attention mask
                    outputs = model(**first_inputs)
                    logits_1_eager = _get_output_logits(outputs)
                    # Second run with attention mask and padding
                    outputs = model(**second_inputs)
                    logits_2_eager = _get_output_logits(outputs)

                    # Switch to FA
                    del model
                    model = model_class.from_pretrained(
                        tmpdirname,
                        dtype=torch.bfloat16,
                        attn_implementation=attn_implementation,
                        device_map=torch_device,
                    )
                    outputs = model(**first_inputs)
                    logits_1_fa = _get_output_logits(outputs)
                    # Second run with attention mask and padding
                    outputs = model(**second_inputs)
                    logits_2_fa = _get_output_logits(outputs)

                    # Check the results
                    torch.testing.assert_close(logits_1_eager, logits_1_fa, atol=atol, rtol=rtol)
                    if padding_side == "left":
                        torch.testing.assert_close(logits_2_eager[1:], logits_2_fa[1:], atol=atol, rtol=rtol)
                    else:
                        torch.testing.assert_close(logits_2_eager[:-1], logits_2_fa[:-1], atol=atol, rtol=rtol)

            # In this case, the test should appear as skipped, not successful
            if not _has_run_at_least_one_model:
                self.skipTest(
                    f"Model architecture does not support {attn_implementation}, or setting its attention dynamically"
                )


@slow
@require_torch_accelerator
@unittest.skip(reason="Update after release")  # TODO(vasqu)
class Gemma4UnifiedIntegrationTest(unittest.TestCase):
    def setUp(self):
        self.model_name = "gg-hf-gu/gemma-4-12B-it"
        self.processor = Gemma4UnifiedProcessor.from_pretrained(self.model_name)

        self.url1 = url_to_local_path(
            "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
        )
        self.url2 = url_to_local_path(
            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        )
        self.messages = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {
                "role": "user",
                "content": [
                    {"type": "image", "url": self.url1},
                    {"type": "text", "text": "What is shown in this image?"},
                ],
            },
        ]

    def tearDown(self):
        cleanup(torch_device, gc_collect=True)

    def test_model_with_image(self):
        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)

        inputs = self.processor.apply_chat_template(
            self.messages,
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
        ).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)

        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", 8): ['This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background'],
            }
        )  # fmt: skip
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(output_text, EXPECTED_TEXT)

    def test_model_with_image_batch(self):
        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)

        messages_2 = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "url": self.url1,
                    },
                    {"type": "image", "url": self.url2},
                    {"type": "text", "text": "Are these images identical?"},
                ],
            },
        ]

        inputs = self.processor.apply_chat_template(
            [self.messages, messages_2],
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
            processor_kwargs={"padding": True},
        ).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)

        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", (8, 0)): [
                    "This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background",
                    "No, these images are not identical.\n\nThe first image is a photograph of a **cow** standing on a beach under a blue sky.\n\n",
                ],
                ("cuda", (8, 6)): [
                    "This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background",
                    "No, these images are not identical.\n\nThe first image is a photograph of a **brown and white cow standing on a beach** under a blue",
                ],
            }
        )
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(output_text, EXPECTED_TEXT)

    def test_model_multiimage(self):
        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)

        messages = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {
                "role": "user",
                "content": [
                    {"type": "image", "url": self.url2},
                    {"type": "text", "text": "What do you see here?"},
                ],
            },
        ]

        inputs = self.processor.apply_chat_template(
            messages,
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
            processor_kwargs={"padding": True},
        ).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)
        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", 8): ['Based on the image, here is a description of what I see:\n\n**Foreground & Street Scene:**\n* **Traffic Sign:** The most prominent'],
            }
        )  # fmt: skip
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(output_text, EXPECTED_TEXT)

    @require_torch_multi_gpu
    def test_model_text_only_multigpu(self):
        """Accelerate destroys the input dict `shared_kv_states` if it's not passed as kwarg and part of
        `_skip_keys_device_placement`, so test this to avoid regresions.
        """
        model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map="auto")
        tokenizer = AutoTokenizer.from_pretrained(self.model_name, padding_side="left")
        inputs = tokenizer.apply_chat_template(
            [{"role": "user", "content": "Write a poem about Machine Learning."}],
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
        ).to(model.device)

        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)

        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", (8, 0)): ['## The Algorithmic Mind\n\nA whisper starts, a seed unseen,\nOf data vast, a vibrant sheen.\nA sea of numbers,'],
                ("cuda", (8, 6)): ['## The Algorithmic Mind\n\nA tapestry of data, vast and deep,\nWhere silent numbers in their slumber sleep.\nA sea of text'],
            }
        )  # fmt: skip
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(output_text, EXPECTED_TEXT)

    def test_model_text_only(self):
        model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map=torch_device)
        tokenizer = AutoTokenizer.from_pretrained(self.model_name, padding_side="left")
        inputs = tokenizer.apply_chat_template(
            [{"role": "user", "content": "Write a poem about Machine Learning."}],
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
        ).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)

        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", (8, 0)): ['## The Algorithmic Mind\n\nA whisper starts, a seed unseen,\nOf data vast, a vibrant sheen.\nA sea of numbers,'],
                ("cuda", (8, 6)): ['## The Algorithmic Mind\n\nA tapestry of data, vast and deep,\nWhere silent numbers in their slumber sleep.\nA sea of text'],
            }
        )  # fmt: skip
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(output_text, EXPECTED_TEXT)

    def test_states_sharing_with_and_without_cache(self):
        model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map=torch_device)
        tokenizer = AutoTokenizer.from_pretrained(self.model_name, padding_side="left")
        inputs = tokenizer.apply_chat_template(
            [{"role": "user", "content": "Who are you? What can you do?"}],
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
        ).to(torch_device)
        input_size = inputs.input_ids.shape[-1]

        # With and without cache generatiom should share kv states the same way
        output_with_cache = model.generate(**inputs, max_new_tokens=30, do_sample=False, use_cache=True)
        output_without_cache = model.generate(**inputs, max_new_tokens=30, do_sample=False, use_cache=False)

        output_text_with_cache = tokenizer.batch_decode(output_with_cache[:, input_size:], skip_special_tokens=True)
        output_text_without_cache = tokenizer.batch_decode(
            output_without_cache[:, input_size:], skip_special_tokens=True
        )

        self.assertEqual(output_text_with_cache, output_text_without_cache)

    # Note: we do not test FA2 as the head dim is 512 on some layers, which is not compatible with the kernels
    @parameterized.expand([("sdpa",), ("eager",)])
    def test_generation_beyond_sliding_window(self, attn_implementation: str):
        """Test that we can correctly generate beyond the sliding window. Outputs for every attention functions
        should be coherent and identical.
        """

        input_text = [
            "This is a nice place. " * 800 + "I really enjoy the scenery,",  # This is larger than 4096 tokens
            "A list of colors: red, blue",  # This will almost all be padding tokens
        ]
        tokenizer = AutoTokenizer.from_pretrained(self.model_name, padding="left")
        input_text = [
            tokenizer.apply_chat_template(
                [{"role": "user", "content": item}],
                tokenize=False,
                add_generation_prompt=True,
            )
            for item in input_text
        ]
        inputs = tokenizer(input_text, padding=True, return_tensors="pt").to(torch_device)

        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(
            self.model_name,
            device_map=torch_device,
            attn_implementation=attn_implementation,
        )

        # Make sure prefill is larger than sliding window
        input_size = inputs.input_ids.shape[-1]
        self.assertTrue(input_size > model.config.get_text_config().sliding_window)

        out = model.generate(**inputs, max_new_tokens=16, do_sample=False, cache_implementation="static")
        output_text = tokenizer.batch_decode(out[:, input_size:])

        EXPECTED_COMPLETIONS = Expectations(
            {
                ("cuda", 8): [
                    "That sounds lovely! It seems like you're really enjoying the place you'",
                    "Here are a few ways you could use or expand upon that list, depending on",
                ]
            }
        )
        self.assertEqual(output_text, EXPECTED_COMPLETIONS.get_expectation())

    def test_model_with_audio(self):
        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)

        audio_url = url_to_local_path(
            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav"
        )
        messages = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Transcribe this:"},
                    {"type": "audio", "url": audio_url},
                ],
            },
        ]

        inputs = self.processor.apply_chat_template(
            messages,
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
        ).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=10, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)

        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", 8): ["come on dude you got a tattoo"],
            }
        )
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(_normalize_text(output_text[0]), EXPECTED_TEXT[0])

    def test_model_with_audio_batch(self):
        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)

        audio_url1 = url_to_local_path(
            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav"
        )
        audio_url2 = url_to_local_path(
            "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3"
        )
        messages_1 = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Transcribe this:"},
                    {"type": "audio", "url": audio_url1},
                ],
            },
        ]
        messages_2 = [
            {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Are these audio clips saying the same thing?"},
                    {"type": "audio", "url": audio_url2},
                    {"type": "audio", "url": audio_url1},
                ],
            },
        ]

        inputs = self.processor.apply_chat_template(
            [messages_1, messages_2],
            tokenize=True,
            return_dict=True,
            return_tensors="pt",
            add_generation_prompt=True,
            processor_kwargs={"padding": True},
        ).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=10, do_sample=False)
        input_size = inputs.input_ids.shape[-1]
        output_text = self.processor.batch_decode(output[:, input_size:], skip_special_tokens=True)

        EXPECTED_TEXTS = Expectations(
            {
                ("cuda", 8): ["come on dude you got a tattoo", "the first audio clip is a speech and the"],
            }
        )
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        self.assertEqual(_normalize_text(output_text[0]), EXPECTED_TEXT[0])
        self.assertEqual(_normalize_text(output_text[1]), EXPECTED_TEXT[1])

    @pytest.mark.torch_export_test
    def test_export_text_only(self):
        from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM

        model = Gemma4UnifiedForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)

        exportable_module = TorchExportableModuleForDecoderOnlyLM(
            model, batch_size=1, max_cache_len=1024, device=torch_device
        )
        exported_program = exportable_module.export(
            input_ids=torch.tensor([[1]], device=torch_device, dtype=torch.long),
        )

        # Test generation with the exported model
        prompt = tokenizer.apply_chat_template(
            [{"role": "user", "content": "What is the capital of France?"}],
            tokenize=False,
            add_generation_prompt=True,
        )

        max_new_tokens_to_generate = 20
        # Generate text with the exported model
        export_generated_text = TorchExportableModuleForDecoderOnlyLM.generate(
            exported_program, tokenizer, prompt, max_new_tokens=max_new_tokens_to_generate, device=torch_device
        )

        input_text = tokenizer(prompt, return_tensors="pt").to(torch_device)
        eager_outputs = model.generate(
            **input_text,
            max_new_tokens=max_new_tokens_to_generate,
            do_sample=False,  # Use greedy decoding to match the exported model
        )

        eager_generated_text = tokenizer.decode(eager_outputs[0], skip_special_tokens=True)
        self.assertEqual(export_generated_text, eager_generated_text)
