from typing import List, Optional, Union

import torch
from diffusers import PixArtAlphaPipeline
from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha import retrieve_timesteps


def freeze_params(params):
    for param in params:
        param.requires_grad = False


class RewardPixartPipeline(PixArtAlphaPipeline):
    def __init__(
        self, tokenizer, text_encoder, transformer, scheduler, vae, memsave=False
    ):
        super().__init__(
            tokenizer,
            text_encoder,
            vae,
            transformer,
            scheduler,
        )
        # optionally enable memsave_torch
        if memsave:
            import memsave_torch.nn

            self.vae = memsave_torch.nn.convert_to_memory_saving(self.vae)
            self.text_encoder = memsave_torch.nn.convert_to_memory_saving(
                self.text_encoder
            )
        self.text_encoder.gradient_checkpointing_enable()
        self.vae.enable_gradient_checkpointing()
        self.text_encoder.eval()
        self.vae.eval()
        freeze_params(self.vae.parameters())
        freeze_params(self.text_encoder.parameters())

    def apply(
        self,
        latents: torch.Tensor = None,
        prompt: Union[str, List[str]] = None,
        negative_prompt: str = "",
        num_inference_steps: int = 20,
        timesteps: List[int] = [400],
        sigmas: List[float] = None,
        guidance_scale: float = 1.0,
        num_images_per_prompt: Optional[int] = 1,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
        callback_steps: int = 1,
        clean_caption: bool = False,
        use_resolution_binning: bool = True,
        max_sequence_length: int = 120,
        **kwargs,
    ):
        # 1. Check inputs. Raise error if not correct
        height = height or self.transformer.config.sample_size * self.vae_scale_factor
        width = width or self.transformer.config.sample_size * self.vae_scale_factor
        if use_resolution_binning:
            if self.transformer.config.sample_size == 128:
                aspect_ratio_bin = ASPECT_RATIO_1024_BIN
            elif self.transformer.config.sample_size == 64:
                aspect_ratio_bin = ASPECT_RATIO_512_BIN
            elif self.transformer.config.sample_size == 32:
                aspect_ratio_bin = ASPECT_RATIO_256_BIN
            else:
                raise ValueError("Invalid sample size")
            orig_height, orig_width = height, width
            height, width = self.image_processor.classify_height_width_bin(
                height, width, ratios=aspect_ratio_bin
            )

        self.check_inputs(
            prompt,
            height,
            width,
            negative_prompt,
            callback_steps,
            prompt_embeds,
            negative_prompt_embeds,
            prompt_attention_mask,
            negative_prompt_attention_mask,
        )

        # 2. Default height and width to transformer
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        (
            prompt_embeds,
            prompt_attention_mask,
            negative_prompt_embeds,
            negative_prompt_attention_mask,
        ) = self.encode_prompt(
            prompt,
            do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            prompt_attention_mask=prompt_attention_mask,
            negative_prompt_attention_mask=negative_prompt_attention_mask,
            clean_caption=clean_caption,
            max_sequence_length=max_sequence_length,
        )
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            prompt_attention_mask = torch.cat(
                [negative_prompt_attention_mask, prompt_attention_mask], dim=0
            )

        # 4. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler, num_inference_steps, device, timesteps, sigmas
        )

        # 5. Prepare latents.
        latent_channels = self.transformer.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            latent_channels,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 6.1 Prepare micro-conditions.
        added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
        if self.transformer.config.sample_size == 128:
            resolution = torch.tensor([height, width]).repeat(
                batch_size * num_images_per_prompt, 1
            )
            aspect_ratio = torch.tensor([float(height / width)]).repeat(
                batch_size * num_images_per_prompt, 1
            )
            resolution = resolution.to(dtype=prompt_embeds.dtype, device=device)
            aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device)

            if do_classifier_free_guidance:
                resolution = torch.cat([resolution, resolution], dim=0)
                aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0)

            added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}

        # 7. Denoising loop
        num_warmup_steps = max(
            len(timesteps) - num_inference_steps * self.scheduler.order, 0
        )

        for i, t in enumerate(timesteps):
            latent_model_input = (
                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            )
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            current_timestep = t
            if not torch.is_tensor(current_timestep):
                # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                # This would be a good case for the `match` statement (Python 3.10+)
                is_mps = latent_model_input.device.type == "mps"
                if isinstance(current_timestep, float):
                    dtype = torch.float32 if is_mps else torch.float64
                else:
                    dtype = torch.int32 if is_mps else torch.int64
                current_timestep = torch.tensor(
                    [current_timestep], dtype=dtype, device=latent_model_input.device
                )
            elif len(current_timestep.shape) == 0:
                current_timestep = current_timestep[None].to(latent_model_input.device)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            current_timestep = current_timestep.expand(latent_model_input.shape[0])

            # predict noise model_output
            noise_pred = self.transformer(
                latent_model_input,
                encoder_hidden_states=prompt_embeds,
                encoder_attention_mask=prompt_attention_mask,
                timestep=current_timestep,
                added_cond_kwargs=added_cond_kwargs,
                return_dict=False,
            )[0]

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                noise_pred = noise_pred.chunk(2, dim=1)[0]
            else:
                noise_pred = noise_pred

            # compute previous image: x_t -> x_t-1
            if num_inference_steps == 1:
                # For DMD one step sampling: https://arxiv.org/abs/2311.18828
                latents = self.scheduler.step(
                    noise_pred, t, latents, **extra_step_kwargs
                ).pred_original_sample

        image = self.vae.decode(
            latents / self.vae.config.scaling_factor, return_dict=False
        )[0]
        if use_resolution_binning:
            image = self.image_processor.resize_and_crop_tensor(
                image, orig_width, orig_height
            )

        image = (image / 2 + 0.5).clamp(0, 1)

        # Offload all models
        self.maybe_free_model_hooks()
        return image


ASPECT_RATIO_2048_BIN = {
    "0.25": [1024.0, 4096.0],
    "0.26": [1024.0, 3968.0],
    "0.27": [1024.0, 3840.0],
    "0.28": [1024.0, 3712.0],
    "0.32": [1152.0, 3584.0],
    "0.33": [1152.0, 3456.0],
    "0.35": [1152.0, 3328.0],
    "0.4": [1280.0, 3200.0],
    "0.42": [1280.0, 3072.0],
    "0.48": [1408.0, 2944.0],
    "0.5": [1408.0, 2816.0],
    "0.52": [1408.0, 2688.0],
    "0.57": [1536.0, 2688.0],
    "0.6": [1536.0, 2560.0],
    "0.68": [1664.0, 2432.0],
    "0.72": [1664.0, 2304.0],
    "0.78": [1792.0, 2304.0],
    "0.82": [1792.0, 2176.0],
    "0.88": [1920.0, 2176.0],
    "0.94": [1920.0, 2048.0],
    "1.0": [2048.0, 2048.0],
    "1.07": [2048.0, 1920.0],
    "1.13": [2176.0, 1920.0],
    "1.21": [2176.0, 1792.0],
    "1.29": [2304.0, 1792.0],
    "1.38": [2304.0, 1664.0],
    "1.46": [2432.0, 1664.0],
    "1.67": [2560.0, 1536.0],
    "1.75": [2688.0, 1536.0],
    "2.0": [2816.0, 1408.0],
    "2.09": [2944.0, 1408.0],
    "2.4": [3072.0, 1280.0],
    "2.5": [3200.0, 1280.0],
    "2.89": [3328.0, 1152.0],
    "3.0": [3456.0, 1152.0],
    "3.11": [3584.0, 1152.0],
    "3.62": [3712.0, 1024.0],
    "3.75": [3840.0, 1024.0],
    "3.88": [3968.0, 1024.0],
    "4.0": [4096.0, 1024.0],
}

ASPECT_RATIO_256_BIN = {
    "0.25": [128.0, 512.0],
    "0.28": [128.0, 464.0],
    "0.32": [144.0, 448.0],
    "0.33": [144.0, 432.0],
    "0.35": [144.0, 416.0],
    "0.4": [160.0, 400.0],
    "0.42": [160.0, 384.0],
    "0.48": [176.0, 368.0],
    "0.5": [176.0, 352.0],
    "0.52": [176.0, 336.0],
    "0.57": [192.0, 336.0],
    "0.6": [192.0, 320.0],
    "0.68": [208.0, 304.0],
    "0.72": [208.0, 288.0],
    "0.78": [224.0, 288.0],
    "0.82": [224.0, 272.0],
    "0.88": [240.0, 272.0],
    "0.94": [240.0, 256.0],
    "1.0": [256.0, 256.0],
    "1.07": [256.0, 240.0],
    "1.13": [272.0, 240.0],
    "1.21": [272.0, 224.0],
    "1.29": [288.0, 224.0],
    "1.38": [288.0, 208.0],
    "1.46": [304.0, 208.0],
    "1.67": [320.0, 192.0],
    "1.75": [336.0, 192.0],
    "2.0": [352.0, 176.0],
    "2.09": [368.0, 176.0],
    "2.4": [384.0, 160.0],
    "2.5": [400.0, 160.0],
    "3.0": [432.0, 144.0],
    "4.0": [512.0, 128.0],
}

ASPECT_RATIO_1024_BIN = {
    "0.25": [512.0, 2048.0],
    "0.28": [512.0, 1856.0],
    "0.32": [576.0, 1792.0],
    "0.33": [576.0, 1728.0],
    "0.35": [576.0, 1664.0],
    "0.4": [640.0, 1600.0],
    "0.42": [640.0, 1536.0],
    "0.48": [704.0, 1472.0],
    "0.5": [704.0, 1408.0],
    "0.52": [704.0, 1344.0],
    "0.57": [768.0, 1344.0],
    "0.6": [768.0, 1280.0],
    "0.68": [832.0, 1216.0],
    "0.72": [832.0, 1152.0],
    "0.78": [896.0, 1152.0],
    "0.82": [896.0, 1088.0],
    "0.88": [960.0, 1088.0],
    "0.94": [960.0, 1024.0],
    "1.0": [1024.0, 1024.0],
    "1.07": [1024.0, 960.0],
    "1.13": [1088.0, 960.0],
    "1.21": [1088.0, 896.0],
    "1.29": [1152.0, 896.0],
    "1.38": [1152.0, 832.0],
    "1.46": [1216.0, 832.0],
    "1.67": [1280.0, 768.0],
    "1.75": [1344.0, 768.0],
    "2.0": [1408.0, 704.0],
    "2.09": [1472.0, 704.0],
    "2.4": [1536.0, 640.0],
    "2.5": [1600.0, 640.0],
    "3.0": [1728.0, 576.0],
    "4.0": [2048.0, 512.0],
}

ASPECT_RATIO_512_BIN = {
    "0.25": [256.0, 1024.0],
    "0.28": [256.0, 928.0],
    "0.32": [288.0, 896.0],
    "0.33": [288.0, 864.0],
    "0.35": [288.0, 832.0],
    "0.4": [320.0, 800.0],
    "0.42": [320.0, 768.0],
    "0.48": [352.0, 736.0],
    "0.5": [352.0, 704.0],
    "0.52": [352.0, 672.0],
    "0.57": [384.0, 672.0],
    "0.6": [384.0, 640.0],
    "0.68": [416.0, 608.0],
    "0.72": [416.0, 576.0],
    "0.78": [448.0, 576.0],
    "0.82": [448.0, 544.0],
    "0.88": [480.0, 544.0],
    "0.94": [480.0, 512.0],
    "1.0": [512.0, 512.0],
    "1.07": [512.0, 480.0],
    "1.13": [544.0, 480.0],
    "1.21": [544.0, 448.0],
    "1.29": [576.0, 448.0],
    "1.38": [576.0, 416.0],
    "1.46": [608.0, 416.0],
    "1.67": [640.0, 384.0],
    "1.75": [672.0, 384.0],
    "2.0": [704.0, 352.0],
    "2.09": [736.0, 352.0],
    "2.4": [768.0, 320.0],
    "2.5": [800.0, 320.0],
    "3.0": [864.0, 288.0],
    "4.0": [1024.0, 256.0],
}