# Copyright 2025 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Testing suite for the PyTorch LLaMA model."""

import unittest

from transformers import AutoTokenizer, is_torch_available, set_seed
from transformers.testing_utils import (
    Expectations,
    cleanup,
    require_deterministic_for_xpu,
    require_torch,
    require_torch_accelerator,
    slow,
    torch_device,
)

from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester


if is_torch_available():
    import torch

    from transformers import Lfm2MoeConfig, Lfm2MoeForCausalLM, Lfm2MoeModel
    from transformers.models.lfm2_moe.modeling_lfm2_moe import Lfm2MoeHybridConvCache


class Lfm2MoeModelTester(CausalLMModelTester):
    if is_torch_available():
        config_class = Lfm2MoeConfig
        base_model_class = Lfm2MoeModel
        causal_lm_class = Lfm2MoeForCausalLM

    def __init__(
        self,
        parent,
        num_dense_layers=1,
        num_hidden_layers=2,
        layer_types=["full_attention", "conv"],
    ):
        super().__init__(parent)
        self.layer_types = layer_types
        self.num_dense_layers = num_dense_layers
        self.num_hidden_layers = num_hidden_layers


@require_torch
class Lfm2MoeModelTest(CausalLMModelTest, unittest.TestCase):
    all_model_classes = (Lfm2MoeModel, Lfm2MoeForCausalLM) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Lfm2MoeModel,
            "text-generation": Lfm2MoeForCausalLM,
        }
        if is_torch_available()
        else {}
    )
    model_tester_class = Lfm2MoeModelTester
    # used in `test_torch_compile_for_training`
    _torch_compile_train_cls = Lfm2MoeForCausalLM if is_torch_available() else None

    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
        self.assertIsInstance(past_key_values, Lfm2MoeHybridConvCache)

        # (batch, kv heads, seq_length, head_dim)
        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        attention_shape = (batch_size, num_heads, seq_length, head_dim)
        conv_shape = (batch_size, config.hidden_size, config.conv_L_cache)

        for i in range(config.num_hidden_layers):
            if config.layer_types[i] == "full_attention":
                self.assertEqual(past_key_values.key_cache[i].shape, attention_shape)
                self.assertEqual(past_key_values.value_cache[i].shape, attention_shape)
            else:
                self.assertEqual(past_key_values.conv_cache[i].shape, conv_shape)

    def _check_caches_are_equal(self, cache1: Lfm2MoeHybridConvCache, cache2: Lfm2MoeHybridConvCache):
        if not isinstance(cache1, Lfm2MoeHybridConvCache) or not isinstance(cache2, Lfm2MoeHybridConvCache):
            raise ValueError("The wrong cache is being used!")

        if not len(cache1) == len(cache2):
            raise ValueError("Both caches do not have the same number of layers.")

        num_layers = len(cache1)
        for idx in range(num_layers):
            torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
            torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
            torch.testing.assert_close(cache1.conv_cache[idx], cache2.conv_cache[idx])

    def test_attention_outputs(self):
        """Lfm2Moe alternates between attention and short-conv layers."""
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True
        # force eager attention to support output attentions
        config._attn_implementation = "eager"
        seq_len = getattr(self.model_tester, "seq_length", None)

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class._from_config(config, attn_implementation="eager").to(torch_device).eval()
            config = model.config
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            self.assertEqual(len(attentions), sum(layer == "full_attention" for layer in config.layer_types))

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config).to(torch_device).eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            self.assertEqual(len(attentions), sum(layer == "full_attention" for layer in config.layer_types))
            self.assertListEqual(list(attentions[0].shape[-3:]), [config.num_attention_heads, seq_len, seq_len])
            out_len = len(outputs)

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config).to(torch_device).eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
                self_attentions = outputs.attentions

            self.assertEqual(out_len + 1, len(outputs))
            self.assertEqual(len(self_attentions), sum(layer == "full_attention" for layer in config.layer_types))
            self.assertListEqual(list(self_attentions[0].shape[-3:]), [config.num_attention_heads, seq_len, seq_len])


@require_torch_accelerator
@slow
class Lfm2MoeIntegrationTest(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = None

    @classmethod
    def tearDownClass(cls):
        del cls.model
        cleanup(torch_device, gc_collect=True)

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

    @classmethod
    def get_model(cls):
        if cls.model is None:
            cls.model = Lfm2MoeForCausalLM.from_pretrained(
                "LiquidAI/LFM2-8B-A1B",
                device_map="auto",
                dtype=torch.bfloat16,
                experts_implementation="eager",
            )
        return cls.model

    @slow
    def test_model_1a8b_logits(self):
        set_seed(1789)
        input_ids = [1, 22998, 768, 1947, 797, 22017, 811, 6332, 928, 5743, 797, 779, 48123, 772, 33551, 60996, 523]
        model = self.get_model()
        input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
        with torch.no_grad():
            out = model(input_ids).logits.float().cpu()
        # fmt: off
        # Expected mean on dim = -1
        EXPECTED_MEANS = Expectations(
            {
                ("cuda", None): torch.tensor([[-1.3912, -0.4653, -1.3339, -1.3249, -1.0985, -1.2373, -1.4599, -0.7515, -0.6140, -1.2329, -1.1481, -1.0081, -0.9937, -0.8875, -1.5539, -1.7283, -1.6284]]),
                ("xpu", None): torch.tensor([[-1.3879, -0.4730, -1.3193, -1.3139, -1.0826, -1.2129, -1.4744, -0.7485, -0.6004, -1.2353, -1.1602, -1.0432, -1.0180, -0.9099, -1.5949, -1.7487, -1.5991]]),
            }
        )
        # fmt: on
        EXPECTED_MEAN = EXPECTED_MEANS.get_expectation()
        out_mean = out.mean(-1)
        torch.testing.assert_close(out_mean, EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
        # fmt: off
        # Expected portion of the logits
        EXPECTED_SLICES = Expectations(
            {
                ("cuda", None): torch.tensor([-1.2734, 2.4844, 5.5000, -1.3438, -1.3281, -1.3516, 1.9375, 5.8438, -0.6641, -1.2969]),
                ("xpu", None): torch.tensor([-1.2734,  2.4531, 5.4688, -1.3438, -1.3281, -1.3516, 1.9297, 5.7812, -0.6719, -1.3125]),
            }
        )
        # fmt: on
        EXPECTED_SLICE = EXPECTED_SLICES.get_expectation()
        out_slice = out[0, 0, :10]
        torch.testing.assert_close(out_slice, EXPECTED_SLICE, rtol=1e-4, atol=1e-4)

    @slow
    def test_model_1a8b_generation(self):
        EXPECTED_TEXT_COMPLETION = """In 1st century A.D., the Roman Empire controlled much of Europe, North Africa, and parts of the Middle East."""
        set_seed(1789)
        prompt = "In 1st century A.D., the Roman Empire"
        tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-8B-A1B", use_fast=False)
        model = self.get_model()
        input_ids = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True).to(
            model.model.embed_tokens.weight.device
        )
        with torch.no_grad():
            generated_ids = model.generate(input_ids, max_new_tokens=15, do_sample=False)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @slow
    @require_deterministic_for_xpu
    def test_model_1a8b_batched_chat_generation(self):
        prompts = ["Who are you?", "Complete the text: Lorem ipsum dolor ", "The Meji Restoration in Japan ended"]
        # fmt: off
        EXPECTED_TEXT_COMPLETIONS = Expectations(
            {
                ("cuda", None): [
                    "Who are you? (AI) designed to assist?  \nI am an AI assistant developed to",
                    "Complete the text: Lorem ipsum dolor ipsum dolor ipsum dolor ipsum dolor ipsum.",
                    "The Meji Restoration in Japan ended**  \n**A.** The shogunate was abolished, and imperial"
                ],
                ("xpu", None): [
                    "Who are you? (AI) designed to assist?  \nI am an AI language model developed",
                    "Complete the text: Lorem ipsum dolor ipsum dolor ipsum dolor ipsum dolor ipsum dolor",
                    "The Meji Restoration in Japan ended, which occurred in 1868, marked the:  \nA) Establish"
                ],
            }
        )
        # fmt: on
        EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation()

        set_seed(1789)
        tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-8B-A1B", use_fast=False)
        model = self.get_model()
        batched_input_ids = tokenizer(prompts, return_tensors="pt", padding=True).to(
            model.model.embed_tokens.weight.device
        )
        with torch.no_grad():
            generated_ids = model.generate(**batched_input_ids, max_new_tokens=15, do_sample=False)
        text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
