# coding=utf-8
# Copyright 2025 The HuggingFace Team 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 clone 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 torch

from diffusers import DiffusionPipeline, QuantoConfig
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.utils.testing_utils import (
    is_transformers_available,
    require_accelerate,
    require_bitsandbytes_version_greater,
    require_quanto,
    require_torch,
    require_torch_accelerator,
    slow,
    torch_device,
)


if is_transformers_available():
    from transformers import BitsAndBytesConfig as TranBitsAndBytesConfig
else:
    TranBitsAndBytesConfig = None


@require_bitsandbytes_version_greater("0.43.2")
@require_quanto
@require_accelerate
@require_torch
@require_torch_accelerator
@slow
class PipelineQuantizationTests(unittest.TestCase):
    model_name = "hf-internal-testing/tiny-flux-pipe"
    prompt = "a beautiful sunset amidst the mountains."
    num_inference_steps = 10
    seed = 0

    def test_quant_config_set_correctly_through_kwargs(self):
        components_to_quantize = ["transformer", "text_encoder_2"]
        quant_config = PipelineQuantizationConfig(
            quant_backend="bitsandbytes_4bit",
            quant_kwargs={
                "load_in_4bit": True,
                "bnb_4bit_quant_type": "nf4",
                "bnb_4bit_compute_dtype": torch.bfloat16,
            },
            components_to_quantize=components_to_quantize,
        )
        pipe = DiffusionPipeline.from_pretrained(
            self.model_name,
            quantization_config=quant_config,
            torch_dtype=torch.bfloat16,
        ).to(torch_device)
        for name, component in pipe.components.items():
            if name in components_to_quantize:
                self.assertTrue(getattr(component.config, "quantization_config", None) is not None)
                quantization_config = component.config.quantization_config
                self.assertTrue(quantization_config.load_in_4bit)
                self.assertTrue(quantization_config.quant_method == "bitsandbytes")

        _ = pipe(self.prompt, num_inference_steps=self.num_inference_steps)

    def test_quant_config_set_correctly_through_granular(self):
        quant_config = PipelineQuantizationConfig(
            quant_mapping={
                "transformer": QuantoConfig(weights_dtype="int8"),
                "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16),
            }
        )
        components_to_quantize = list(quant_config.quant_mapping.keys())
        pipe = DiffusionPipeline.from_pretrained(
            self.model_name,
            quantization_config=quant_config,
            torch_dtype=torch.bfloat16,
        ).to(torch_device)
        for name, component in pipe.components.items():
            if name in components_to_quantize:
                self.assertTrue(getattr(component.config, "quantization_config", None) is not None)
                quantization_config = component.config.quantization_config

                if name == "text_encoder_2":
                    self.assertTrue(quantization_config.load_in_4bit)
                    self.assertTrue(quantization_config.quant_method == "bitsandbytes")
                else:
                    self.assertTrue(quantization_config.quant_method == "quanto")

        _ = pipe(self.prompt, num_inference_steps=self.num_inference_steps)

    def test_raises_error_for_invalid_config(self):
        with self.assertRaises(ValueError) as err_context:
            _ = PipelineQuantizationConfig(
                quant_mapping={
                    "transformer": QuantoConfig(weights_dtype="int8"),
                    "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16),
                },
                quant_backend="bitsandbytes_4bit",
            )

        self.assertTrue(
            str(err_context.exception)
            == "Both `quant_backend` and `quant_mapping` cannot be specified at the same time."
        )

    def test_validation_for_kwargs(self):
        components_to_quantize = ["transformer", "text_encoder_2"]
        with self.assertRaises(ValueError) as err_context:
            _ = PipelineQuantizationConfig(
                quant_backend="quanto",
                quant_kwargs={"weights_dtype": "int8"},
                components_to_quantize=components_to_quantize,
            )

        self.assertTrue(
            "The signatures of the __init__ methods of the quantization config classes" in str(err_context.exception)
        )

    def test_raises_error_for_wrong_config_class(self):
        quant_config = {
            "transformer": QuantoConfig(weights_dtype="int8"),
            "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16),
        }
        with self.assertRaises(ValueError) as err_context:
            _ = DiffusionPipeline.from_pretrained(
                self.model_name,
                quantization_config=quant_config,
                torch_dtype=torch.bfloat16,
            )
        self.assertTrue(
            str(err_context.exception) == "`quantization_config` must be an instance of `PipelineQuantizationConfig`."
        )

    def test_validation_for_mapping(self):
        with self.assertRaises(ValueError) as err_context:
            _ = PipelineQuantizationConfig(
                quant_mapping={
                    "transformer": DiffusionPipeline(),
                    "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16),
                }
            )

        self.assertTrue("Provided config for module_name=transformer could not be found" in str(err_context.exception))

    def test_saving_loading(self):
        quant_config = PipelineQuantizationConfig(
            quant_mapping={
                "transformer": QuantoConfig(weights_dtype="int8"),
                "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16),
            }
        )
        components_to_quantize = list(quant_config.quant_mapping.keys())
        pipe = DiffusionPipeline.from_pretrained(
            self.model_name,
            quantization_config=quant_config,
            torch_dtype=torch.bfloat16,
        ).to(torch_device)

        pipe_inputs = {"prompt": self.prompt, "num_inference_steps": self.num_inference_steps, "output_type": "latent"}
        output_1 = pipe(**pipe_inputs, generator=torch.manual_seed(self.seed)).images

        with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(tmpdir)
            loaded_pipe = DiffusionPipeline.from_pretrained(tmpdir, torch_dtype=torch.bfloat16).to(torch_device)
        for name, component in loaded_pipe.components.items():
            if name in components_to_quantize:
                self.assertTrue(getattr(component.config, "quantization_config", None) is not None)
                quantization_config = component.config.quantization_config

                if name == "text_encoder_2":
                    self.assertTrue(quantization_config.load_in_4bit)
                    self.assertTrue(quantization_config.quant_method == "bitsandbytes")
                else:
                    self.assertTrue(quantization_config.quant_method == "quanto")

        output_2 = loaded_pipe(**pipe_inputs, generator=torch.manual_seed(self.seed)).images

        self.assertTrue(torch.allclose(output_1, output_2))
