# 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 tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort


class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
    hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"

    def get_dummy_inputs(self, seed=0):
        generator = np.random.RandomState(seed)
        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 7.5,
            "output_type": "np",
        }
        return inputs

    def test_pipeline_default_ddim(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        image = pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_pipeline_pndm(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        image = pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_pipeline_lms(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        image = pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_pipeline_euler(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        image = pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_pipeline_euler_ancestral(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        image = pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_pipeline_dpm_multistep(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        image = pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_stable_diffusion_prompt_embeds(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        inputs["prompt"] = 3 * [inputs["prompt"]]

        # forward
        output = pipe(**inputs)
        image_slice_1 = output.images[0, -3:, -3:, -1]

        inputs = self.get_dummy_inputs()
        prompt = 3 * [inputs.pop("prompt")]

        text_inputs = pipe.tokenizer(
            prompt,
            padding="max_length",
            max_length=pipe.tokenizer.model_max_length,
            truncation=True,
            return_tensors="np",
        )
        text_inputs = text_inputs["input_ids"]

        prompt_embeds = pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0]

        inputs["prompt_embeds"] = prompt_embeds

        # forward
        output = pipe(**inputs)
        image_slice_2 = output.images[0, -3:, -3:, -1]

        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4

    def test_stable_diffusion_negative_prompt_embeds(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs()
        negative_prompt = 3 * ["this is a negative prompt"]
        inputs["negative_prompt"] = negative_prompt
        inputs["prompt"] = 3 * [inputs["prompt"]]

        # forward
        output = pipe(**inputs)
        image_slice_1 = output.images[0, -3:, -3:, -1]

        inputs = self.get_dummy_inputs()
        prompt = 3 * [inputs.pop("prompt")]

        embeds = []
        for p in [prompt, negative_prompt]:
            text_inputs = pipe.tokenizer(
                p,
                padding="max_length",
                max_length=pipe.tokenizer.model_max_length,
                truncation=True,
                return_tensors="np",
            )
            text_inputs = text_inputs["input_ids"]

            embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0])

        inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds

        # forward
        output = pipe(**inputs)
        image_slice_2 = output.images[0, -3:, -3:, -1]

        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4


@nightly
@require_onnxruntime
@require_torch_gpu
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
    @property
    def gpu_provider(self):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

    @property
    def gpu_options(self):
        options = ort.SessionOptions()
        options.enable_mem_pattern = False
        return options

    def test_inference_default_pndm(self):
        # using the PNDM scheduler by default
        sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4",
            revision="onnx",
            safety_checker=None,
            feature_extractor=None,
            provider=self.gpu_provider,
            sess_options=self.gpu_options,
        )
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        np.random.seed(0)
        output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type="np")
        image = output.images

        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    def test_inference_ddim(self):
        ddim_scheduler = DDIMScheduler.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
        )
        sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            revision="onnx",
            scheduler=ddim_scheduler,
            safety_checker=None,
            feature_extractor=None,
            provider=self.gpu_provider,
            sess_options=self.gpu_options,
        )
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "open neural network exchange"
        generator = np.random.RandomState(0)
        output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
        image = output.images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    def test_inference_k_lms(self):
        lms_scheduler = LMSDiscreteScheduler.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
        )
        sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            revision="onnx",
            scheduler=lms_scheduler,
            safety_checker=None,
            feature_extractor=None,
            provider=self.gpu_provider,
            sess_options=self.gpu_options,
        )
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "open neural network exchange"
        generator = np.random.RandomState(0)
        output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
        image = output.images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    def test_intermediate_state(self):
        number_of_steps = 0

        def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
            test_callback_fn.has_been_called = True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
                assert latents.shape == (1, 4, 64, 64)
                latents_slice = latents[0, -3:, -3:, -1]
                expected_slice = np.array(
                    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]
                )

                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
            elif step == 5:
                assert latents.shape == (1, 4, 64, 64)
                latents_slice = latents[0, -3:, -3:, -1]
                expected_slice = np.array(
                    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]
                )

                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

        test_callback_fn.has_been_called = False

        pipe = OnnxStableDiffusionPipeline.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            revision="onnx",
            safety_checker=None,
            feature_extractor=None,
            provider=self.gpu_provider,
            sess_options=self.gpu_options,
        )
        pipe.set_progress_bar_config(disable=None)

        prompt = "Andromeda galaxy in a bottle"

        generator = np.random.RandomState(0)
        pipe(
            prompt=prompt,
            num_inference_steps=5,
            guidance_scale=7.5,
            generator=generator,
            callback=test_callback_fn,
            callback_steps=1,
        )
        assert test_callback_fn.has_been_called
        assert number_of_steps == 6

    def test_stable_diffusion_no_safety_checker(self):
        pipe = OnnxStableDiffusionPipeline.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            revision="onnx",
            safety_checker=None,
            feature_extractor=None,
            provider=self.gpu_provider,
            sess_options=self.gpu_options,
        )
        assert isinstance(pipe, OnnxStableDiffusionPipeline)
        assert pipe.safety_checker is None

        image = pipe("example prompt", num_inference_steps=2).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(tmpdirname)
            pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname)

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        image = pipe("example prompt", num_inference_steps=2).images[0]
        assert image is not None
