# coding=utf-8
# Copyright 2022 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 inspect
import time
import warnings
from dataclasses import dataclass
from math import ceil
from typing import Any, Callable, Dict, List, Optional, Union

import numpy as np
import PIL
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import BaseOutput, deprecate
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from optimum.utils import logging

from ....transformers.gaudi_configuration import GaudiConfig
from ....utils import speed_metrics
from ..pipeline_utils import GaudiDiffusionPipeline


logger = logging.get_logger(__name__)


@dataclass
class GaudiStableDiffusionPipelineOutput(BaseOutput):
    images: Union[List[PIL.Image.Image], np.ndarray]
    nsfw_content_detected: Optional[List[bool]]
    throughput: float


def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


class GaudiStableDiffusionPipeline(
    GaudiDiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
    """
    Extends the [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline) class:
    - Generation is performed by batches
    - Two `mark_step()` were added to add support for lazy mode
    - Added support for HPU graphs

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
        use_habana (bool, defaults to `False`):
            Whether to use Gaudi (`True`) or CPU (`False`).
        use_hpu_graphs (bool, defaults to `False`):
            Whether to use HPU graphs or not.
        gaudi_config (Union[str, [`GaudiConfig`]], defaults to `None`):
            Gaudi configuration to use. Can be a string to download it from the Hub.
            Or a previously initialized config can be passed.
        bf16_full_eval (bool, defaults to `False`):
            Whether to use full bfloat16 evaluation instead of 32-bit.
            This will be faster and save memory compared to fp32/mixed precision but can harm generated images.
    """

    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        requires_safety_checker: bool = True,
        use_habana: bool = False,
        use_hpu_graphs: bool = False,
        gaudi_config: Union[str, GaudiConfig] = None,
        bf16_full_eval: bool = False,
    ):
        super().__init__(
            use_habana,
            use_hpu_graphs,
            gaudi_config,
            bf16_full_eval,
        )

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
            )
            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["clip_sample"] = False
            scheduler._internal_dict = FrozenDict(new_config)

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
            version.parse(unet.config._diffusers_version).base_version
        ) < version.parse("0.9.0.dev0")
        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
                " the `unet/config.json` file"
            )
            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(unet.config)
            new_config["sample_size"] = 64
            unet._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

        self.to(self._device)

    @property
    def _execution_device(self):
        r"""
        Returns the device on which the pipeline's models will be executed. After calling
        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
        hooks.
        """
        if not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(module, "_hf_hook")
                and hasattr(module._hf_hook, "execution_device")
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
        """
        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

        if prompt is not None and isinstance(prompt, str):
            num_prompts = 1
        elif prompt is not None and isinstance(prompt, list):
            num_prompts = len(prompt)
        else:
            num_prompts = prompt_embeds.shape[0]

        if prompt_embeds is None:
            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * num_prompts
            elif prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif num_prompts != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has {len(negative_prompt)} elements, but `prompt`:"
                    f" {prompt} has {num_prompts}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(num_prompts * num_images_per_prompt, seq_len, -1)

        return prompt_embeds, negative_prompt_embeds

    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is None:
            has_nsfw_concept = None
        else:
            if torch.is_tensor(image):
                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
            else:
                feature_extractor_input = self.image_processor.numpy_to_pil(image)
            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
            )
        return image, has_nsfw_concept

    def decode_latents(self, latents):
        warnings.warn(
            "The decode_latents method is deprecated and will be removed in a future version. Please"
            " use VaeImageProcessor instead",
            FutureWarning,
        )
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents, return_dict=False)[0]
        image = (image / 2 + 0.5).clamp(0, 1)
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        prompt,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    def prepare_latents(self, num_images, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (num_images, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != num_images:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective number"
                f" of images of {num_images}. Make sure the number of images matches the length of the generators."
            )

        if latents is None:
            # torch.randn is broken on HPU so running it on CPU
            rand_device = "cpu" if device.type == "hpu" else device
            if isinstance(generator, list):
                shape = (1,) + shape[1:]
                latents = [
                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
                    for i in range(num_images)
                ]
                latents = torch.cat(latents, dim=0).to(device)
            else:
                latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
        else:
            if latents.shape != shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    @classmethod
    def _split_inputs_into_batches(cls, batch_size, latents, text_embeddings, uncond_embeddings):
        # Use torch.split to generate num_batches batches of size batch_size
        latents_batches = list(torch.split(latents, batch_size))
        text_embeddings_batches = list(torch.split(text_embeddings, batch_size))
        if uncond_embeddings is not None:
            uncond_embeddings_batches = list(torch.split(uncond_embeddings, batch_size))

        # If the last batch has less samples than batch_size, pad it with dummy samples
        num_dummy_samples = 0
        if latents_batches[-1].shape[0] < batch_size:
            num_dummy_samples = batch_size - latents_batches[-1].shape[0]
            # Pad latents_batches
            sequence_to_stack = (latents_batches[-1],) + tuple(
                torch.zeros_like(latents_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
            )
            latents_batches[-1] = torch.vstack(sequence_to_stack)
            # Pad text_embeddings_batches
            sequence_to_stack = (text_embeddings_batches[-1],) + tuple(
                torch.zeros_like(text_embeddings_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
            )
            text_embeddings_batches[-1] = torch.vstack(sequence_to_stack)
            # Pad uncond_embeddings_batches if necessary
            if uncond_embeddings is not None:
                sequence_to_stack = (uncond_embeddings_batches[-1],) + tuple(
                    torch.zeros_like(uncond_embeddings_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
                )
                uncond_embeddings_batches[-1] = torch.vstack(sequence_to_stack)

        # Stack batches in the same tensor
        latents_batches = torch.stack(latents_batches)
        if uncond_embeddings is not None:
            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            for i, (uncond_embeddings_batch, text_embeddings_batch) in enumerate(
                zip(uncond_embeddings_batches, text_embeddings_batches[:])
            ):
                text_embeddings_batches[i] = torch.cat([uncond_embeddings_batch, text_embeddings_batch])
        text_embeddings_batches = torch.stack(text_embeddings_batches)

        return latents_batches, text_embeddings_batches, num_dummy_samples

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        batch_size: int = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated images.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated images.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            batch_size (`int`, *optional*, defaults to 1):
                The number of images in a batch.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated randomly.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
            guidance_rescale (`float`, *optional*, defaults to 0.7):
                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                Guidance rescale factor should fix overexposure when using zero terminal SNR.

        Returns:
            [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] or `tuple`:
            [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple`.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        with torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=self.gaudi_config.use_torch_autocast):
            # 0. Default height and width to unet
            height = height or self.unet.config.sample_size * self.vae_scale_factor
            width = width or self.unet.config.sample_size * self.vae_scale_factor

            # 1. Check inputs. Raise error if not correct
            self.check_inputs(
                prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
            )

            # 2. Define call parameters
            if prompt is not None and isinstance(prompt, str):
                num_prompts = 1
            elif prompt is not None and isinstance(prompt, list):
                num_prompts = len(prompt)
            else:
                num_prompts = prompt_embeds.shape[0]
            num_batches = ceil((num_images_per_prompt * num_prompts) / batch_size)
            logger.info(
                f"{num_prompts} prompt(s) received, {num_images_per_prompt} generation(s) per prompt,"
                f" {batch_size} sample(s) per batch, {num_batches} total batch(es)."
            )
            if num_batches < 3:
                logger.warning("The first two iterations are slower so it is recommended to feed more batches.")
            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
            text_encoder_lora_scale = (
                cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
            )
            prompt_embeds, negative_prompt_embeds = self._encode_prompt(
                prompt,
                device,
                num_images_per_prompt,
                do_classifier_free_guidance,
                negative_prompt,
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=negative_prompt_embeds,
                lora_scale=text_encoder_lora_scale,
            )

            # 4. Prepare timesteps
            self.scheduler.set_timesteps(num_inference_steps, device="cpu")
            timesteps = self.scheduler.timesteps.to(device)

            # 5. Prepare latent variables
            num_channels_latents = self.unet.config.in_channels
            latents = self.prepare_latents(
                num_prompts * num_images_per_prompt,
                num_channels_latents,
                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)

            # 7. Split into batches (HPU-specific step)
            latents_batches, text_embeddings_batches, num_dummy_samples = self._split_inputs_into_batches(
                batch_size,
                latents,
                prompt_embeds,
                negative_prompt_embeds,
            )

            outputs = {
                "images": [],
                "has_nsfw_concept": [],
            }
            t0 = time.time()
            t1 = t0

            # 8. Denoising loop
            for j in self.progress_bar(range(num_batches)):
                # The throughput is calculated from the 3rd iteration
                # because compilation occurs in the first two iterations
                if j == 2:
                    t1 = time.time()

                latents_batch = latents_batches[0]
                latents_batches = torch.roll(latents_batches, shifts=-1, dims=0)
                text_embeddings_batch = text_embeddings_batches[0]
                text_embeddings_batches = torch.roll(text_embeddings_batches, shifts=-1, dims=0)

                for i in range(num_inference_steps):
                    timestep = timesteps[0]
                    timesteps = torch.roll(timesteps, shifts=-1, dims=0)

                    capture = True if self.use_hpu_graphs and i < 2 else False

                    # expand the latents if we are doing classifier free guidance
                    latent_model_input = (
                        torch.cat([latents_batch] * 2) if do_classifier_free_guidance else latents_batch
                    )
                    # latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)

                    # predict the noise residual
                    noise_pred = self.unet_hpu(
                        latent_model_input,
                        timestep,
                        text_embeddings_batch,
                        cross_attention_kwargs,
                        capture,
                    )

                    # 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)

                    if do_classifier_free_guidance and guidance_rescale > 0.0:
                        # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                        noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                    # compute the previous noisy sample x_t -> x_t-1
                    latents_batch = self.scheduler.step(
                        noise_pred, latents_batch, **extra_step_kwargs, return_dict=False
                    )[0]

                    if not self.use_hpu_graphs:
                        self.htcore.mark_step()

                    # call the callback, if provided
                    if callback is not None and i % callback_steps == 0:
                        callback(i, timestep, latents_batch)

                if not output_type == "latent":
                    # 8. Post-processing
                    image = self.vae.decode(latents_batch / self.vae.config.scaling_factor, return_dict=False)[0]
                else:
                    image = latents_batch
                outputs["images"].append(image)

                self.scheduler.reset_timestep_dependent_params()

                if not self.use_hpu_graphs:
                    self.htcore.mark_step()

            speed_metrics_prefix = "generation"
            speed_measures = speed_metrics(
                split=speed_metrics_prefix,
                start_time=t0,
                num_samples=num_batches * batch_size if t1 == t0 else (num_batches - 2) * batch_size,
                num_steps=num_batches,
                start_time_after_warmup=t1,
            )
            logger.info(f"Speed metrics: {speed_measures}")

            # Remove dummy generations if needed
            if num_dummy_samples > 0:
                outputs["images"][-1] = outputs["images"][-1][:-num_dummy_samples]

            # Process generated images
            for i, image in enumerate(outputs["images"][:]):
                if i == 0:
                    outputs["images"].clear()

                if output_type == "latent":
                    has_nsfw_concept = None
                else:
                    image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

                if has_nsfw_concept is None:
                    do_denormalize = [True] * image.shape[0]
                else:
                    do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

                image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

                if output_type == "pil":
                    outputs["images"] += image
                else:
                    outputs["images"] += [*image]

                if has_nsfw_concept is not None:
                    outputs["has_nsfw_concept"] += has_nsfw_concept
                else:
                    outputs["has_nsfw_concept"] = None

            if not return_dict:
                return (outputs["images"], outputs["has_nsfw_concept"])

            return GaudiStableDiffusionPipelineOutput(
                images=outputs["images"],
                nsfw_content_detected=outputs["has_nsfw_concept"],
                throughput=speed_measures[f"{speed_metrics_prefix}_samples_per_second"],
            )

    @torch.no_grad()
    def unet_hpu(self, latent_model_input, timestep, encoder_hidden_states, cross_attention_kwargs, capture):
        if self.use_hpu_graphs:
            return self.capture_replay(latent_model_input, timestep, encoder_hidden_states, capture)
        else:
            return self.unet(
                latent_model_input,
                timestep,
                encoder_hidden_states=encoder_hidden_states,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]

    @torch.no_grad()
    def capture_replay(self, latent_model_input, timestep, encoder_hidden_states, capture):
        inputs = [latent_model_input, timestep, encoder_hidden_states, False]
        h = self.ht.hpu.graphs.input_hash(inputs)
        cached = self.cache.get(h)

        if capture:
            # Capture the graph and cache it
            with self.ht.hpu.stream(self.hpu_stream):
                graph = self.ht.hpu.HPUGraph()
                graph.capture_begin()
                outputs = self.unet(inputs[0], inputs[1], inputs[2], inputs[3])[0]
                graph.capture_end()
                graph_inputs = inputs
                graph_outputs = outputs
                self.cache[h] = self.ht.hpu.graphs.CachedParams(graph_inputs, graph_outputs, graph)
            return outputs

        # Replay the cached graph with updated inputs
        self.ht.hpu.graphs.copy_to(cached.graph_inputs, inputs)
        cached.graph.replay()
        self.ht.core.hpu.default_stream().synchronize()

        return cached.graph_outputs
