# Copyright 2026 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.

import numpy as np
import pytest
import torch

from diffusers import MagCacheConfig, apply_mag_cache
from diffusers.hooks._helpers import TransformerBlockMetadata, TransformerBlockRegistry
from diffusers.models import ModelMixin
from diffusers.utils import logging


logger = logging.get_logger(__name__)


class DummyBlock(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, hidden_states, encoder_hidden_states=None, **kwargs):
        # Output is double input
        # This ensures Residual = 2*Input - Input = Input
        return hidden_states * 2.0


class DummyTransformer(ModelMixin):
    def __init__(self):
        super().__init__()
        self.transformer_blocks = torch.nn.ModuleList([DummyBlock(), DummyBlock()])

    def forward(self, hidden_states, encoder_hidden_states=None):
        for block in self.transformer_blocks:
            hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states)
        return hidden_states


class TupleOutputBlock(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, hidden_states, encoder_hidden_states=None, **kwargs):
        # Returns a tuple
        return hidden_states * 2.0, encoder_hidden_states


class TupleTransformer(ModelMixin):
    def __init__(self):
        super().__init__()
        self.transformer_blocks = torch.nn.ModuleList([TupleOutputBlock()])

    def forward(self, hidden_states, encoder_hidden_states=None):
        for block in self.transformer_blocks:
            # Emulate Flux-like behavior
            output = block(hidden_states, encoder_hidden_states=encoder_hidden_states)
            hidden_states = output[0]
            encoder_hidden_states = output[1]
        return hidden_states, encoder_hidden_states


@pytest.fixture(autouse=True)
def register_dummy_blocks():
    # Register standard dummy block
    TransformerBlockRegistry.register(
        DummyBlock,
        TransformerBlockMetadata(return_hidden_states_index=None, return_encoder_hidden_states_index=None),
    )
    # Register tuple block (Flux style)
    TransformerBlockRegistry.register(
        TupleOutputBlock,
        TransformerBlockMetadata(return_hidden_states_index=0, return_encoder_hidden_states_index=1),
    )


def _set_context(model, context_name):
    """Helper to set context on all hooks in the model."""
    for module in model.modules():
        if hasattr(module, "_diffusers_hook"):
            module._diffusers_hook._set_context(context_name)


def _get_calibration_data(model):
    for module in model.modules():
        if hasattr(module, "_diffusers_hook"):
            hook = module._diffusers_hook.get_hook("mag_cache_block_hook")
            if hook:
                return hook.state_manager.get_state().calibration_ratios
    return []


def test_mag_cache_validation():
    """Test that missing mag_ratios raises ValueError."""
    with pytest.raises(ValueError):
        MagCacheConfig(num_inference_steps=10, calibrate=False)


def test_mag_cache_skipping_logic():
    """
    Tests that MagCache correctly calculates residuals and skips blocks when conditions are met.
    """
    model = DummyTransformer()

    # Dummy ratios: [1.0, 1.0] implies 0 accumulated error if we skip
    ratios = np.array([1.0, 1.0])

    config = MagCacheConfig(
        threshold=100.0,
        num_inference_steps=2,
        retention_ratio=0.0,  # Enable immediate skipping
        max_skip_steps=5,
        mag_ratios=ratios,
    )

    apply_mag_cache(model, config)
    _set_context(model, "test_context")

    # Step 0: Input 10.0 -> Output 40.0 (2 blocks * 2x each)
    # HeadInput=10. Output=40. Residual=30.
    input_t0 = torch.tensor([[[10.0]]])
    output_t0 = model(input_t0)
    assert torch.allclose(output_t0, torch.tensor([[[40.0]]])), "Step 0 failed"

    # Step 1: Input 11.0.
    # If Skipped: Output = Input(11) + Residual(30) = 41.0
    # If Computed: Output = 11 * 4 = 44.0
    input_t1 = torch.tensor([[[11.0]]])
    output_t1 = model(input_t1)

    assert torch.allclose(output_t1, torch.tensor([[[41.0]]])), f"Expected Skip (41.0), got {output_t1.item()}"


def test_mag_cache_retention():
    """Test that retention_ratio prevents skipping even if error is low."""
    model = DummyTransformer()
    # Ratios that imply 0 error, so it *would* skip if retention allowed it
    ratios = np.array([1.0, 1.0])

    config = MagCacheConfig(
        threshold=100.0,
        num_inference_steps=2,
        retention_ratio=1.0,  # Force retention for ALL steps
        mag_ratios=ratios,
    )

    apply_mag_cache(model, config)
    _set_context(model, "test_context")

    # Step 0
    model(torch.tensor([[[10.0]]]))

    # Step 1: Should COMPUTE (44.0) not SKIP (41.0) because of retention
    input_t1 = torch.tensor([[[11.0]]])
    output_t1 = model(input_t1)

    assert torch.allclose(output_t1, torch.tensor([[[44.0]]])), (
        f"Expected Compute (44.0) due to retention, got {output_t1.item()}"
    )


def test_mag_cache_tuple_outputs():
    """Test compatibility with models returning (hidden, encoder_hidden) like Flux."""
    model = TupleTransformer()
    ratios = np.array([1.0, 1.0])

    config = MagCacheConfig(threshold=100.0, num_inference_steps=2, retention_ratio=0.0, mag_ratios=ratios)

    apply_mag_cache(model, config)
    _set_context(model, "test_context")

    # Step 0: Compute. Input 10.0 -> Output 20.0 (1 block * 2x)
    # Residual = 10.0
    input_t0 = torch.tensor([[[10.0]]])
    enc_t0 = torch.tensor([[[1.0]]])
    out_0, _ = model(input_t0, encoder_hidden_states=enc_t0)
    assert torch.allclose(out_0, torch.tensor([[[20.0]]]))

    # Step 1: Skip. Input 11.0.
    # Skipped Output = 11 + 10 = 21.0
    input_t1 = torch.tensor([[[11.0]]])
    out_1, _ = model(input_t1, encoder_hidden_states=enc_t0)

    assert torch.allclose(out_1, torch.tensor([[[21.0]]])), f"Tuple skip failed. Expected 21.0, got {out_1.item()}"


def test_mag_cache_reset():
    """Test that state resets correctly after num_inference_steps."""
    model = DummyTransformer()
    config = MagCacheConfig(
        threshold=100.0, num_inference_steps=2, retention_ratio=0.0, mag_ratios=np.array([1.0, 1.0])
    )
    apply_mag_cache(model, config)
    _set_context(model, "test_context")

    input_t = torch.ones(1, 1, 1)

    model(input_t)  # Step 0
    model(input_t)  # Step 1 (Skipped)

    # Step 2 (Reset -> Step 0) -> Should Compute
    # Input 2.0 -> Output 8.0
    input_t2 = torch.tensor([[[2.0]]])
    output_t2 = model(input_t2)

    assert torch.allclose(output_t2, torch.tensor([[[8.0]]])), "State did not reset correctly"


def test_mag_cache_calibration():
    """Test that calibration mode records ratios."""
    model = DummyTransformer()
    config = MagCacheConfig(num_inference_steps=2, calibrate=True)
    apply_mag_cache(model, config)
    _set_context(model, "test_context")

    # Step 0
    # HeadInput = 10. Output = 40. Residual = 30.
    # Ratio 0 is placeholder 1.0
    model(torch.tensor([[[10.0]]]))

    # Check intermediate state
    ratios = _get_calibration_data(model)
    assert len(ratios) == 1
    assert ratios[0] == 1.0

    # Step 1
    # HeadInput = 10. Output = 40. Residual = 30.
    # PrevResidual = 30. CurrResidual = 30.
    # Ratio = 30/30 = 1.0
    model(torch.tensor([[[10.0]]]))

    # Verify it computes fully (no skip)
    # If it skipped, output would be 41.0. It should be 40.0
    # Actually in test setup, input is same (10.0) so output 40.0.
    # Let's ensure list is empty after reset (end of step 1)
    ratios_after = _get_calibration_data(model)
    assert ratios_after == []