# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# 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 os
import textwrap
from collections import defaultdict
from typing import Any, Callable, Optional, Union
from warnings import warn

import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import PyTorchModelHubMixin
from transformers import is_wandb_available

from ..models import DDPOStableDiffusionPipeline
from .alignprop_config import AlignPropConfig
from .utils import generate_model_card, get_comet_experiment_url


if is_wandb_available():
    import wandb

logger = get_logger(__name__)


class AlignPropTrainer(PyTorchModelHubMixin):
    """
    The AlignPropTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models.
    Note, this trainer is heavily inspired by the work here: https://github.com/mihirp1998/AlignProp/
    As of now only Stable Diffusion based pipelines are supported

    Attributes:
        config (`AlignPropConfig`):
            Configuration object for AlignPropTrainer. Check the documentation of `PPOConfig` for more details.
        reward_function (`Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor]`):
            Reward function to be used
        prompt_function (`Callable[[], tuple[str, Any]]`):
            Function to generate prompts to guide model
        sd_pipeline (`DDPOStableDiffusionPipeline`):
            Stable Diffusion pipeline to be used for training.
        image_samples_hook (`Optional[Callable[[Any, Any, Any], Any]]`):
            Hook to be called to log images
    """

    _tag_names = ["trl", "alignprop"]

    def __init__(
        self,
        config: AlignPropConfig,
        reward_function: Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor],
        prompt_function: Callable[[], tuple[str, Any]],
        sd_pipeline: DDPOStableDiffusionPipeline,
        image_samples_hook: Optional[Callable[[Any, Any, Any], Any]] = None,
    ):
        if image_samples_hook is None:
            warn("No image_samples_hook provided; no images will be logged")

        self.prompt_fn = prompt_function
        self.reward_fn = reward_function
        self.config = config
        self.image_samples_callback = image_samples_hook

        accelerator_project_config = ProjectConfiguration(**self.config.project_kwargs)

        if self.config.resume_from:
            self.config.resume_from = os.path.normpath(os.path.expanduser(self.config.resume_from))
            if "checkpoint_" not in os.path.basename(self.config.resume_from):
                # get the most recent checkpoint in this directory
                checkpoints = list(
                    filter(
                        lambda x: "checkpoint_" in x,
                        os.listdir(self.config.resume_from),
                    )
                )
                if len(checkpoints) == 0:
                    raise ValueError(f"No checkpoints found in {self.config.resume_from}")
                checkpoint_numbers = sorted([int(x.split("_")[-1]) for x in checkpoints])
                self.config.resume_from = os.path.join(
                    self.config.resume_from,
                    f"checkpoint_{checkpoint_numbers[-1]}",
                )

                accelerator_project_config.iteration = checkpoint_numbers[-1] + 1

        self.accelerator = Accelerator(
            log_with=self.config.log_with,
            mixed_precision=self.config.mixed_precision,
            project_config=accelerator_project_config,
            # we always accumulate gradients across timesteps; we want config.train.gradient_accumulation_steps to be the
            # number of *samples* we accumulate across, so we need to multiply by the number of training timesteps to get
            # the total number of optimizer steps to accumulate across.
            gradient_accumulation_steps=self.config.train_gradient_accumulation_steps,
            **self.config.accelerator_kwargs,
        )

        is_using_tensorboard = config.log_with is not None and config.log_with == "tensorboard"

        if self.accelerator.is_main_process:
            self.accelerator.init_trackers(
                self.config.tracker_project_name,
                config=dict(alignprop_trainer_config=config.to_dict())
                if not is_using_tensorboard
                else config.to_dict(),
                init_kwargs=self.config.tracker_kwargs,
            )

        logger.info(f"\n{config}")

        set_seed(self.config.seed, device_specific=True)

        self.sd_pipeline = sd_pipeline

        self.sd_pipeline.set_progress_bar_config(
            position=1,
            disable=not self.accelerator.is_local_main_process,
            leave=False,
            desc="Timestep",
            dynamic_ncols=True,
        )

        # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
        # as these weights are only used for inference, keeping weights in full precision is not required.
        if self.accelerator.mixed_precision == "fp16":
            inference_dtype = torch.float16
        elif self.accelerator.mixed_precision == "bf16":
            inference_dtype = torch.bfloat16
        else:
            inference_dtype = torch.float32

        self.sd_pipeline.vae.to(self.accelerator.device, dtype=inference_dtype)
        self.sd_pipeline.text_encoder.to(self.accelerator.device, dtype=inference_dtype)
        self.sd_pipeline.unet.to(self.accelerator.device, dtype=inference_dtype)

        trainable_layers = self.sd_pipeline.get_trainable_layers()

        self.accelerator.register_save_state_pre_hook(self._save_model_hook)
        self.accelerator.register_load_state_pre_hook(self._load_model_hook)

        # Enable TF32 for faster training on Ampere GPUs,
        # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
        if self.config.allow_tf32:
            torch.backends.cuda.matmul.allow_tf32 = True

        self.optimizer = self._setup_optimizer(
            trainable_layers.parameters() if not isinstance(trainable_layers, list) else trainable_layers
        )

        self.neg_prompt_embed = self.sd_pipeline.text_encoder(
            self.sd_pipeline.tokenizer(
                [""] if self.config.negative_prompts is None else self.config.negative_prompts,
                return_tensors="pt",
                padding="max_length",
                truncation=True,
                max_length=self.sd_pipeline.tokenizer.model_max_length,
            ).input_ids.to(self.accelerator.device)
        )[0]

        # NOTE: for some reason, autocast is necessary for non-lora training but for lora training it isn't necessary and it uses
        # more memory
        self.autocast = self.sd_pipeline.autocast or self.accelerator.autocast

        if hasattr(self.sd_pipeline, "use_lora") and self.sd_pipeline.use_lora:
            unet, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer)
            self.trainable_layers = list(filter(lambda p: p.requires_grad, unet.parameters()))
        else:
            self.trainable_layers, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer)

        if config.resume_from:
            logger.info(f"Resuming from {config.resume_from}")
            self.accelerator.load_state(config.resume_from)
            self.first_epoch = int(config.resume_from.split("_")[-1]) + 1
        else:
            self.first_epoch = 0

    def compute_rewards(self, prompt_image_pairs):
        reward, reward_metadata = self.reward_fn(
            prompt_image_pairs["images"], prompt_image_pairs["prompts"], prompt_image_pairs["prompt_metadata"]
        )
        return reward

    def step(self, epoch: int, global_step: int):
        """
        Perform a single step of training.

        Args:
            epoch (int): The current epoch.
            global_step (int): The current global step.

        Side Effects:
            - Model weights are updated
            - Logs the statistics to the accelerator trackers.
            - If `self.image_samples_callback` is not None, it will be called with the prompt_image_pairs, global_step, and the accelerator tracker.

        Returns:
            global_step (int): The updated global step.
        """
        info = defaultdict(list)

        self.sd_pipeline.unet.train()

        for _ in range(self.config.train_gradient_accumulation_steps):
            with self.accelerator.accumulate(self.sd_pipeline.unet), self.autocast(), torch.enable_grad():
                prompt_image_pairs = self._generate_samples(
                    batch_size=self.config.train_batch_size,
                )

                rewards = self.compute_rewards(prompt_image_pairs)

                prompt_image_pairs["rewards"] = rewards

                rewards_vis = self.accelerator.gather(rewards).detach().cpu().numpy()

                loss = self.calculate_loss(rewards)

                self.accelerator.backward(loss)

                if self.accelerator.sync_gradients:
                    self.accelerator.clip_grad_norm_(
                        self.trainable_layers.parameters()
                        if not isinstance(self.trainable_layers, list)
                        else self.trainable_layers,
                        self.config.train_max_grad_norm,
                    )

                self.optimizer.step()
                self.optimizer.zero_grad()

            info["reward_mean"].append(rewards_vis.mean())
            info["reward_std"].append(rewards_vis.std())
            info["loss"].append(loss.item())

        # Checks if the accelerator has performed an optimization step behind the scenes
        if self.accelerator.sync_gradients:
            # log training-related stuff
            info = {k: torch.mean(torch.tensor(v)) for k, v in info.items()}
            info = self.accelerator.reduce(info, reduction="mean")
            info.update({"epoch": epoch})
            self.accelerator.log(info, step=global_step)
            global_step += 1
            info = defaultdict(list)
        else:
            raise ValueError(
                "Optimization step should have been performed by this point. Please check calculated gradient accumulation settings."
            )
        # Logs generated images
        if self.image_samples_callback is not None and global_step % self.config.log_image_freq == 0:
            self.image_samples_callback(prompt_image_pairs, global_step, self.accelerator.trackers[0])

        if epoch != 0 and epoch % self.config.save_freq == 0 and self.accelerator.is_main_process:
            self.accelerator.save_state()

        return global_step

    def calculate_loss(self, rewards):
        """
        Calculate the loss for a batch of an unpacked sample

        Args:
            rewards (torch.Tensor):
                Differentiable reward scalars for each generated image, shape: [batch_size]

        Returns:
            loss (torch.Tensor)
            (all of these are of shape (1,))
        """
        #  Loss is specific to Aesthetic Reward function used in AlignProp (https://huggingface.co/papers/2310.03739)
        loss = 10.0 - (rewards).mean()
        return loss

    def loss(
        self,
        advantages: torch.Tensor,
        clip_range: float,
        ratio: torch.Tensor,
    ):
        unclipped_loss = -advantages * ratio
        clipped_loss = -advantages * torch.clamp(
            ratio,
            1.0 - clip_range,
            1.0 + clip_range,
        )
        return torch.mean(torch.maximum(unclipped_loss, clipped_loss))

    def _setup_optimizer(self, trainable_layers_parameters):
        if self.config.train_use_8bit_adam:
            import bitsandbytes

            optimizer_cls = bitsandbytes.optim.AdamW8bit
        else:
            optimizer_cls = torch.optim.AdamW

        return optimizer_cls(
            trainable_layers_parameters,
            lr=self.config.train_learning_rate,
            betas=(self.config.train_adam_beta1, self.config.train_adam_beta2),
            weight_decay=self.config.train_adam_weight_decay,
            eps=self.config.train_adam_epsilon,
        )

    def _save_model_hook(self, models, weights, output_dir):
        self.sd_pipeline.save_checkpoint(models, weights, output_dir)
        weights.pop()  # ensures that accelerate doesn't try to handle saving of the model

    def _load_model_hook(self, models, input_dir):
        self.sd_pipeline.load_checkpoint(models, input_dir)
        models.pop()  # ensures that accelerate doesn't try to handle loading of the model

    def _generate_samples(self, batch_size, with_grad=True, prompts=None):
        """
        Generate samples from the model

        Args:
            batch_size (int): Batch size to use for sampling
            with_grad (bool): Whether the generated RGBs should have gradients attached to it.

        Returns:
            prompt_image_pairs (dict[Any])
        """
        prompt_image_pairs = {}

        sample_neg_prompt_embeds = self.neg_prompt_embed.repeat(batch_size, 1, 1)

        if prompts is None:
            prompts, prompt_metadata = zip(*[self.prompt_fn() for _ in range(batch_size)])
        else:
            prompt_metadata = [{} for _ in range(batch_size)]

        prompt_ids = self.sd_pipeline.tokenizer(
            prompts,
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=self.sd_pipeline.tokenizer.model_max_length,
        ).input_ids.to(self.accelerator.device)

        prompt_embeds = self.sd_pipeline.text_encoder(prompt_ids)[0]

        if with_grad:
            sd_output = self.sd_pipeline.rgb_with_grad(
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=sample_neg_prompt_embeds,
                num_inference_steps=self.config.sample_num_steps,
                guidance_scale=self.config.sample_guidance_scale,
                eta=self.config.sample_eta,
                truncated_backprop_rand=self.config.truncated_backprop_rand,
                truncated_backprop_timestep=self.config.truncated_backprop_timestep,
                truncated_rand_backprop_minmax=self.config.truncated_rand_backprop_minmax,
                output_type="pt",
            )
        else:
            sd_output = self.sd_pipeline(
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=sample_neg_prompt_embeds,
                num_inference_steps=self.config.sample_num_steps,
                guidance_scale=self.config.sample_guidance_scale,
                eta=self.config.sample_eta,
                output_type="pt",
            )

        images = sd_output.images

        prompt_image_pairs["images"] = images
        prompt_image_pairs["prompts"] = prompts
        prompt_image_pairs["prompt_metadata"] = prompt_metadata

        return prompt_image_pairs

    def train(self, epochs: Optional[int] = None):
        """
        Train the model for a given number of epochs
        """
        global_step = 0
        if epochs is None:
            epochs = self.config.num_epochs
        for epoch in range(self.first_epoch, epochs):
            global_step = self.step(epoch, global_step)

    def _save_pretrained(self, save_directory):
        self.sd_pipeline.save_pretrained(save_directory)
        self.create_model_card()

    def create_model_card(
        self,
        model_name: Optional[str] = None,
        dataset_name: Optional[str] = None,
        tags: Union[str, list[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            model_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the model.
            dataset_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the dataset used for training.
            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
                Tags to be associated with the model card.
        """
        if not self.is_world_process_zero():
            return

        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
            base_model = self.model.config._name_or_path
        else:
            base_model = None

        tags = tags or []
        if isinstance(tags, str):
            tags = [tags]

        if hasattr(self.model.config, "unsloth_version"):
            tags.append("unsloth")

        citation = textwrap.dedent("""\
        @article{prabhudesai2024aligning,
            title        = {{Aligning Text-to-Image Diffusion Models with Reward Backpropagation}},
            author       = {Mihir Prabhudesai and Anirudh Goyal and Deepak Pathak and Katerina Fragkiadaki},
            year         = 2024,
            eprint       = {arXiv:2310.03739}
        }""")

        model_card = generate_model_card(
            base_model=base_model,
            model_name=model_name,
            hub_model_id=self.hub_model_id,
            dataset_name=dataset_name,
            tags=tags,
            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="AlignProp",
            trainer_citation=citation,
            paper_title="Aligning Text-to-Image Diffusion Models with Reward Backpropagation",
            paper_id="2310.03739",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))