# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Testing suite for the PyTorch Gemma2 model."""

import unittest

from transformers import AutoModelForCausalLM, AutoTokenizer, Gemma2Config, is_torch_available
from transformers.testing_utils import (
    require_read_token,
    require_torch,
    require_torch_gpu,
    slow,
    torch_device,
)

from ...models.gemma.test_modeling_gemma import GemmaModelTest, GemmaModelTester
from ...test_configuration_common import ConfigTester


if is_torch_available():
    import torch

    from transformers import (
        Gemma2ForCausalLM,
        Gemma2ForSequenceClassification,
        Gemma2ForTokenClassification,
        Gemma2Model,
    )


class Gemma2ModelTester(GemmaModelTester):
    config_class = Gemma2Config
    model_class = Gemma2Model
    for_causal_lm_class = Gemma2ForCausalLM
    for_sequence_class = Gemma2ForSequenceClassification
    for_token_class = Gemma2ForTokenClassification


@require_torch
class Gemma2ModelTest(GemmaModelTest, unittest.TestCase):
    all_model_classes = (
        (Gemma2Model, Gemma2ForCausalLM, Gemma2ForSequenceClassification, Gemma2ForTokenClassification)
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Gemma2Model,
            "text-classification": Gemma2ForSequenceClassification,
            "token-classification": Gemma2ForTokenClassification,
            "text-generation": Gemma2ForCausalLM,
            "zero-shot": Gemma2ForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False
    _is_stateful = True
    model_split_percents = [0.5, 0.6]
    _torch_compile_test_ckpt = "google/gemma-2-9b"

    def setUp(self):
        self.model_tester = Gemma2ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Gemma2Config, hidden_size=37)

    @unittest.skip("Eager and SDPA do not produce the same outputs, thus this test fails")
    def test_model_outputs_equivalence(self, **kwargs):
        pass

    @unittest.skip("Gemma2's outputs are expected to be different")
    def test_eager_matches_sdpa_inference(self):
        pass


@slow
@require_torch_gpu
class Gemma2IntegrationTest(unittest.TestCase):
    input_text = ["Hello I am doing", "Hi today"]
    # This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
    # Depending on the hardware we get different logits / generations
    cuda_compute_capability_major_version = None

    @classmethod
    def setUpClass(cls):
        if is_torch_available() and torch.cuda.is_available():
            # 8 is for A100 / A10 and 7 for T4
            cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]

    @require_read_token
    def test_model_2b_bf16(self):
        model_id = "google/gemma-2-9b"
        EXPECTED_TEXTS = [
            "<bos>Hello I am doing a project for a class and I am trying to use the <code><a-image></code>",
            "<pad><pad><bos>Hi today. So, I'm going to show you how to do a problem from the textbook. So",
        ]

        model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
            torch_device
        )

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    @require_read_token
    def test_model_2b_fp16(self):
        model_id = "google/gemma-2-9b"
        EXPECTED_TEXTS = [
            "<bos>Hello I am doing a project on the effect of the temperature on the rate of a reaction. I am using a ",
            "<pad><pad><bos>Hi today I'm going to be talking about the 1000-4000-",
        ]

        model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to(
            torch_device
        )

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

        self.assertEqual(output_text, EXPECTED_TEXTS)