# coding=utf-8
# Copyright 2024 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 unittest

import torch

from diffusers import Lumina2Transformer2DModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    torch_device,
)

from ..test_modeling_common import ModelTesterMixin


enable_full_determinism()


class Lumina2Transformer2DModelTransformerTests(ModelTesterMixin, unittest.TestCase):
    model_class = Lumina2Transformer2DModel
    main_input_name = "hidden_states"
    uses_custom_attn_processor = True

    @property
    def dummy_input(self):
        batch_size = 2  # N
        num_channels = 4  # C
        height = width = 16  # H, W
        embedding_dim = 32  # D
        sequence_length = 16  # L

        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
        timestep = torch.rand(size=(batch_size,)).to(torch_device)
        attention_mask = torch.ones(size=(batch_size, sequence_length), dtype=torch.bool).to(torch_device)

        return {
            "hidden_states": hidden_states,
            "encoder_hidden_states": encoder_hidden_states,
            "timestep": timestep,
            "encoder_attention_mask": attention_mask,
        }

    @property
    def input_shape(self):
        return (4, 16, 16)

    @property
    def output_shape(self):
        return (4, 16, 16)

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "sample_size": 16,
            "patch_size": 2,
            "in_channels": 4,
            "hidden_size": 24,
            "num_layers": 2,
            "num_refiner_layers": 1,
            "num_attention_heads": 3,
            "num_kv_heads": 1,
            "multiple_of": 2,
            "ffn_dim_multiplier": None,
            "norm_eps": 1e-5,
            "scaling_factor": 1.0,
            "axes_dim_rope": (4, 2, 2),
            "axes_lens": (128, 128, 128),
            "cap_feat_dim": 32,
        }

        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {"Lumina2Transformer2DModel"}
        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)

# coding=utf-8
# Copyright 2024 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 unittest

import torch

from diffusers import Lumina2Transformer2DModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    torch_device,
)

from ..test_modeling_common import ModelTesterMixin


enable_full_determinism()


class Lumina2Transformer2DModelTransformerTests(ModelTesterMixin, unittest.TestCase):
    model_class = Lumina2Transformer2DModel
    main_input_name = "hidden_states"
    uses_custom_attn_processor = True

    @property
    def dummy_input(self):
        batch_size = 2  # N
        num_channels = 4  # C
        height = width = 16  # H, W
        embedding_dim = 32  # D
        sequence_length = 16  # L

        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
        timestep = torch.rand(size=(batch_size,)).to(torch_device)
        attention_mask = torch.ones(size=(batch_size, sequence_length), dtype=torch.bool).to(torch_device)

        return {
            "hidden_states": hidden_states,
            "encoder_hidden_states": encoder_hidden_states,
            "timestep": timestep,
            "attention_mask": attention_mask,
        }

    @property
    def input_shape(self):
        return (4, 16, 16)

    @property
    def output_shape(self):
        return (4, 16, 16)

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "sample_size": 16,
            "patch_size": 2,
            "in_channels": 4,
            "hidden_size": 24,
            "num_layers": 2,
            "num_refiner_layers": 1,
            "num_attention_heads": 3,
            "num_kv_heads": 1,
            "multiple_of": 2,
            "ffn_dim_multiplier": None,
            "norm_eps": 1e-5,
            "scaling_factor": 1.0,
            "axes_dim_rope": (4, 2, 2),
            "axes_lens": (128, 128, 128),
            "cap_feat_dim": 32,
        }

        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {"Lumina2Transformer2DModel"}
        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
