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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.
# 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
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"""Testing suite for the PyTorch Arcee model."""

import unittest

from pytest import mark

from transformers import AutoTokenizer, is_torch_available
from transformers.testing_utils import (
    require_flash_attn,
    require_torch,
    require_torch_accelerator,
    slow,
)

from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester


if is_torch_available():
    import torch

    from transformers import (
        ArceeConfig,
        ArceeForCausalLM,
        ArceeModel,
    )


class ArceeModelTester(CausalLMModelTester):
    if is_torch_available():
        base_model_class = ArceeModel


@require_torch
class ArceeModelTest(CausalLMModelTest, unittest.TestCase):
    model_tester_class = ArceeModelTester

    # Need to use `0.8` instead of `0.9` for `test_cpu_offload`
    # This is because we are hitting edge cases with the causal_mask buffer
    model_split_percents = [0.5, 0.7, 0.8]

    # used in `test_torch_compile_for_training`
    _torch_compile_train_cls = ArceeForCausalLM if is_torch_available() else None

    def test_arcee_mlp_uses_relu_squared(self):
        """Test that ArceeMLP uses ReLU² activation instead of SiLU."""
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        config.hidden_act = "relu2"  # Ensure we're using relu2 activation
        model = ArceeModel(config)

        # Check that the MLP layers use the correct activation
        mlp = model.layers[0].mlp
        # Test with a simple input
        x = torch.randn(1, 10, config.hidden_size)
        up_output = mlp.up_proj(x)

        # Verify ReLU² activation: x * relu(x)
        expected_activation = up_output * torch.relu(up_output)
        actual_activation = mlp.act_fn(up_output)

        self.assertTrue(torch.allclose(expected_activation, actual_activation, atol=1e-5))


@require_torch_accelerator
class ArceeIntegrationTest(unittest.TestCase):
    def tearDown(self):
        import gc

        gc.collect()
        torch.cuda.empty_cache()

    @slow
    def test_model_from_pretrained(self):
        # This test would be enabled once a pretrained model is available
        # For now, we just test that the model can be instantiated
        config = ArceeConfig()
        model = ArceeForCausalLM(config)
        self.assertIsInstance(model, ArceeForCausalLM)

    @mark.skip(reason="Model is not currently public - will update test post release")
    @slow
    def test_model_generation(self):
        EXPECTED_TEXT_COMPLETION = (
            """Once upon a time,In a village there was a farmer who had three sons. The farmer was very old and he"""
        )
        prompt = "Once upon a time"
        tokenizer = AutoTokenizer.from_pretrained("arcee-ai/model-id")
        model = ArceeForCausalLM.from_pretrained("arcee-ai/model-id", device_map="auto")
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)

        generated_ids = model.generate(input_ids, max_new_tokens=20)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @mark.skip(reason="Model is not currently public - will update test post release")
    @slow
    @require_flash_attn
    @mark.flash_attn_test
    def test_model_generation_flash_attn(self):
        EXPECTED_TEXT_COMPLETION = (
            " the food, the people, and the overall experience. I would definitely recommend this place to others."
        )
        prompt = "This is a nice place. " * 1024 + "I really enjoy the scenery,"
        tokenizer = AutoTokenizer.from_pretrained("arcee-ai/model-id")
        model = ArceeForCausalLM.from_pretrained(
            "arcee-ai/model-id", device_map="auto", attn_implementation="flash_attention_2", dtype="auto"
        )
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)

        generated_ids = model.generate(input_ids, max_new_tokens=20)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text[len(prompt) :])