# Copyright 2024 The HuggingFace Team.
#
# 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 gc
import inspect
import unittest

import numpy as np
import torch
from transformers import Gemma2Config, Gemma2Model, GemmaTokenizer

from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPipeline, SanaTransformer2DModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, to_np


enable_full_determinism()


class SanaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = SanaPipeline
    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    required_optional_params = frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback_on_step_end",
            "callback_on_step_end_tensor_inputs",
        ]
    )
    test_xformers_attention = False
    test_layerwise_casting = True
    test_group_offloading = True

    def get_dummy_components(self):
        torch.manual_seed(0)
        transformer = SanaTransformer2DModel(
            patch_size=1,
            in_channels=4,
            out_channels=4,
            num_layers=1,
            num_attention_heads=2,
            attention_head_dim=4,
            num_cross_attention_heads=2,
            cross_attention_head_dim=4,
            cross_attention_dim=8,
            caption_channels=8,
            sample_size=32,
        )

        torch.manual_seed(0)
        vae = AutoencoderDC(
            in_channels=3,
            latent_channels=4,
            attention_head_dim=2,
            encoder_block_types=(
                "ResBlock",
                "EfficientViTBlock",
            ),
            decoder_block_types=(
                "ResBlock",
                "EfficientViTBlock",
            ),
            encoder_block_out_channels=(8, 8),
            decoder_block_out_channels=(8, 8),
            encoder_qkv_multiscales=((), (5,)),
            decoder_qkv_multiscales=((), (5,)),
            encoder_layers_per_block=(1, 1),
            decoder_layers_per_block=[1, 1],
            downsample_block_type="conv",
            upsample_block_type="interpolate",
            decoder_norm_types="rms_norm",
            decoder_act_fns="silu",
            scaling_factor=0.41407,
        )

        torch.manual_seed(0)
        scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)

        torch.manual_seed(0)
        text_encoder_config = Gemma2Config(
            head_dim=16,
            hidden_size=8,
            initializer_range=0.02,
            intermediate_size=64,
            max_position_embeddings=8192,
            model_type="gemma2",
            num_attention_heads=2,
            num_hidden_layers=1,
            num_key_value_heads=2,
            vocab_size=8,
            attn_implementation="eager",
        )
        text_encoder = Gemma2Model(text_encoder_config)
        tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma")

        components = {
            "transformer": transformer,
            "vae": vae,
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
        }
        return components

    def get_dummy_inputs(self, device, seed=0):
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "prompt": "",
            "negative_prompt": "",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "height": 32,
            "width": 32,
            "max_sequence_length": 16,
            "output_type": "pt",
            "complex_human_instruction": None,
        }
        return inputs

    def test_inference(self):
        device = "cpu"

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        image = pipe(**inputs)[0]
        generated_image = image[0]

        self.assertEqual(generated_image.shape, (3, 32, 32))
        expected_image = torch.randn(3, 32, 32)
        max_diff = np.abs(generated_image - expected_image).max()
        self.assertLessEqual(max_diff, 1e10)

    def test_callback_inputs(self):
        sig = inspect.signature(self.pipeline_class.__call__)
        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
        has_callback_step_end = "callback_on_step_end" in sig.parameters

        if not (has_callback_tensor_inputs and has_callback_step_end):
            return

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        self.assertTrue(
            hasattr(pipe, "_callback_tensor_inputs"),
            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
        )

        def callback_inputs_subset(pipe, i, t, callback_kwargs):
            # iterate over callback args
            for tensor_name, tensor_value in callback_kwargs.items():
                # check that we're only passing in allowed tensor inputs
                assert tensor_name in pipe._callback_tensor_inputs

            return callback_kwargs

        def callback_inputs_all(pipe, i, t, callback_kwargs):
            for tensor_name in pipe._callback_tensor_inputs:
                assert tensor_name in callback_kwargs

            # iterate over callback args
            for tensor_name, tensor_value in callback_kwargs.items():
                # check that we're only passing in allowed tensor inputs
                assert tensor_name in pipe._callback_tensor_inputs

            return callback_kwargs

        inputs = self.get_dummy_inputs(torch_device)

        # Test passing in a subset
        inputs["callback_on_step_end"] = callback_inputs_subset
        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
        output = pipe(**inputs)[0]

        # Test passing in a everything
        inputs["callback_on_step_end"] = callback_inputs_all
        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
        output = pipe(**inputs)[0]

        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
            is_last = i == (pipe.num_timesteps - 1)
            if is_last:
                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
            return callback_kwargs

        inputs["callback_on_step_end"] = callback_inputs_change_tensor
        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
        output = pipe(**inputs)[0]
        assert output.abs().sum() < 1e10

    def test_attention_slicing_forward_pass(
        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
    ):
        if not self.test_attention_slicing:
            return

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        for component in pipe.components.values():
            if hasattr(component, "set_default_attn_processor"):
                component.set_default_attn_processor()
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator_device = "cpu"
        inputs = self.get_dummy_inputs(generator_device)
        output_without_slicing = pipe(**inputs)[0]

        pipe.enable_attention_slicing(slice_size=1)
        inputs = self.get_dummy_inputs(generator_device)
        output_with_slicing1 = pipe(**inputs)[0]

        pipe.enable_attention_slicing(slice_size=2)
        inputs = self.get_dummy_inputs(generator_device)
        output_with_slicing2 = pipe(**inputs)[0]

        if test_max_difference:
            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
            self.assertLess(
                max(max_diff1, max_diff2),
                expected_max_diff,
                "Attention slicing should not affect the inference results",
            )

    def test_vae_tiling(self, expected_diff_max: float = 0.2):
        generator_device = "cpu"
        components = self.get_dummy_components()

        pipe = self.pipeline_class(**components)
        pipe.to("cpu")
        pipe.set_progress_bar_config(disable=None)

        # Without tiling
        inputs = self.get_dummy_inputs(generator_device)
        inputs["height"] = inputs["width"] = 128
        output_without_tiling = pipe(**inputs)[0]

        # With tiling
        pipe.vae.enable_tiling(
            tile_sample_min_height=96,
            tile_sample_min_width=96,
            tile_sample_stride_height=64,
            tile_sample_stride_width=64,
        )
        inputs = self.get_dummy_inputs(generator_device)
        inputs["height"] = inputs["width"] = 128
        output_with_tiling = pipe(**inputs)[0]

        self.assertLess(
            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
            expected_diff_max,
            "VAE tiling should not affect the inference results",
        )

    # TODO(aryan): Create a dummy gemma model with smol vocab size
    @unittest.skip(
        "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
    )
    def test_inference_batch_consistent(self):
        pass

    @unittest.skip(
        "A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
    )
    def test_inference_batch_single_identical(self):
        pass

    def test_float16_inference(self):
        # Requires higher tolerance as model seems very sensitive to dtype
        super().test_float16_inference(expected_max_diff=0.08)


@slow
@require_torch_gpu
class SanaPipelineIntegrationTests(unittest.TestCase):
    prompt = "A painting of a squirrel eating a burger."

    def setUp(self):
        super().setUp()
        gc.collect()
        torch.cuda.empty_cache()

    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def test_sana_1024(self):
        generator = torch.Generator("cpu").manual_seed(0)

        pipe = SanaPipeline.from_pretrained(
            "Efficient-Large-Model/Sana_1600M_1024px_diffusers", torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload()

        image = pipe(
            prompt=self.prompt,
            height=1024,
            width=1024,
            generator=generator,
            num_inference_steps=20,
            output_type="np",
        ).images[0]

        image = image.flatten()
        output_slice = np.concatenate((image[:16], image[-16:]))

        # fmt: off
        expected_slice = np.array([0.0427, 0.0789, 0.0662, 0.0464, 0.082, 0.0574, 0.0535, 0.0886, 0.0647, 0.0549, 0.0872, 0.0605, 0.0593, 0.0942, 0.0674, 0.0581, 0.0076, 0.0168, 0.0027, 0.0063, 0.0159, 0.0, 0.0071, 0.0198, 0.0034, 0.0105, 0.0212, 0.0, 0.0, 0.0166, 0.0042, 0.0125])
        # fmt: on

        self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-4))

    def test_sana_512(self):
        generator = torch.Generator("cpu").manual_seed(0)

        pipe = SanaPipeline.from_pretrained(
            "Efficient-Large-Model/Sana_1600M_512px_diffusers", torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload()

        image = pipe(
            prompt=self.prompt,
            height=512,
            width=512,
            generator=generator,
            num_inference_steps=20,
            output_type="np",
        ).images[0]

        image = image.flatten()
        output_slice = np.concatenate((image[:16], image[-16:]))

        # fmt: off
        expected_slice = np.array([0.0803, 0.0774, 0.1108, 0.0872, 0.093, 0.1118, 0.0952, 0.0898, 0.1038, 0.0818, 0.0754, 0.0894, 0.074, 0.0691, 0.0906, 0.0671, 0.0154, 0.0254, 0.0203, 0.0178, 0.0283, 0.0193, 0.0215, 0.0273, 0.0188, 0.0212, 0.0273, 0.0151, 0.0061, 0.0244, 0.0212, 0.0259])
        # fmt: on

        self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-4))
