'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
from sapiens_transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, ImageProjection, MultiAdapter, T2IAdapter, UNet2DConditionModel
from ...models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, replace_example_docstring, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
EXAMPLE_DOC_STRING = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> from sapiens_transformers.diffusers import T2IAdapter, StableDiffusionXLAdapterPipeline, DDPMScheduler\n        >>> from sapiens_transformers.diffusers.utils import load_image\n\n        >>> sketch_image = load_image("https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png").convert("L")\n\n        >>> model_id = "stabilityai/stable-diffusion-xl-base-1.0"\n\n        >>> adapter = T2IAdapter.from_pretrained(\n        ...     "Adapter/t2iadapter",\n        ...     subfolder="sketch_sdxl_1.0",\n        ...     torch_dtype=torch.float16,\n        ...     adapter_type="full_adapter_xl",\n        ... )\n        >>> scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")\n\n        >>> pipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n        ...     model_id, adapter=adapter, torch_dtype=torch.float16, variant="fp16", scheduler=scheduler\n        ... ).to("cuda")\n\n        >>> generator = torch.manual_seed(42)\n        >>> sketch_image_out = pipe(\n        ...     prompt="a photo of a dog in real world, high quality",\n        ...     negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality",\n        ...     image=sketch_image,\n        ...     generator=generator,\n        ...     guidance_scale=7.5,\n        ... ).images[0]\n        ```\n'
def _preprocess_adapter_image(image, height, width):
    if isinstance(image, torch.Tensor): return image
    elif isinstance(image, PIL.Image.Image): image = [image]
    if isinstance(image[0], PIL.Image.Image):
        image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION['lanczos'])) for i in image]
        image = [i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image]
        image = np.concatenate(image, axis=0)
        image = np.array(image).astype(np.float32) / 255.0
        image = image.transpose(0, 3, 1, 2)
        image = torch.from_numpy(image)
    elif isinstance(image[0], torch.Tensor):
        if image[0].ndim == 3: image = torch.stack(image, dim=0)
        elif image[0].ndim == 4: image = torch.cat(image, dim=0)
        else: raise ValueError(f'Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}')
    return image
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """Returns:"""
    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)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg
def retrieve_timesteps(scheduler, num_inference_steps: Optional[int]=None, device: Optional[Union[str, torch.device]]=None, timesteps: Optional[List[int]]=None,
sigmas: Optional[List[float]]=None, **kwargs):
    """Returns:"""
    if timesteps is not None and sigmas is not None: raise ValueError('Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values')
    if timesteps is not None:
        accepts_timesteps = 'timesteps' in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accepts_timesteps: raise ValueError(f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.")
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        accept_sigmas = 'sigmas' in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accept_sigmas: raise ValueError(f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.")
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return (timesteps, num_inference_steps)
class StableDiffusionXLAdapterPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin):
    """Args:"""
    model_cpu_offload_seq = 'text_encoder->text_encoder_2->image_encoder->unet->vae'
    _optional_components = ['tokenizer', 'tokenizer_2', 'text_encoder', 'text_encoder_2', 'feature_extractor', 'image_encoder']
    def __init__(self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer,
    unet: UNet2DConditionModel, adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]], scheduler: KarrasDiffusionSchedulers, force_zeros_for_empty_prompt: bool=True,
    feature_extractor: CLIPImageProcessor=None, image_encoder: CLIPVisionModelWithProjection=None):
        super().__init__()
        self.register_modules(vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, adapter=adapter,
        scheduler=scheduler, feature_extractor=feature_extractor, image_encoder=image_encoder)
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
        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.default_sample_size = self.unet.config.sample_size
    def encode_prompt(self, prompt: str, prompt_2: Optional[str]=None, device: Optional[torch.device]=None, num_images_per_prompt: int=1, do_classifier_free_guidance: bool=True,
    negative_prompt: Optional[str]=None, negative_prompt_2: Optional[str]=None, prompt_embeds: Optional[torch.Tensor]=None, negative_prompt_embeds: Optional[torch.Tensor]=None,
    pooled_prompt_embeds: Optional[torch.Tensor]=None, negative_pooled_prompt_embeds: Optional[torch.Tensor]=None, lora_scale: Optional[float]=None, clip_skip: Optional[int]=None):
        """Args:"""
        device = device or self._execution_device
        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
            self._lora_scale = lora_scale
            if self.text_encoder is not None:
                if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                else: scale_lora_layers(self.text_encoder, lora_scale)
            if self.text_encoder_2 is not None:
                if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
                else: scale_lora_layers(self.text_encoder_2, lora_scale)
        prompt = [prompt] if isinstance(prompt, str) else prompt
        if prompt is not None: batch_size = len(prompt)
        else: batch_size = prompt_embeds.shape[0]
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
            prompt_embeds_list = []
            prompts = [prompt, prompt_2]
            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, tokenizer)
                text_inputs = tokenizer(prompt, padding='max_length', max_length=tokenizer.model_max_length, truncation=True, return_tensors='pt')
                text_input_ids = text_inputs.input_ids
                untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1:-1])
                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
                pooled_prompt_embeds = prompt_embeds[0]
                if clip_skip is None: prompt_embeds = prompt_embeds.hidden_states[-2]
                else: prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
                prompt_embeds_list.append(prompt_embeds)
            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ''
            negative_prompt_2 = negative_prompt_2 or negative_prompt
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
            negative_prompt_2 = batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
            uncond_tokens: List[str]
            if 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)} != {type(prompt)}.')
            elif batch_size != len(negative_prompt): raise ValueError(f'`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`: {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches the batch size of `prompt`.')
            else: uncond_tokens = [negative_prompt, negative_prompt_2]
            negative_prompt_embeds_list = []
            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin): negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(negative_prompt, padding='max_length', max_length=max_length, truncation=True, return_tensors='pt')
                negative_prompt_embeds = text_encoder(uncond_input.input_ids.to(device), output_hidden_states=True)
                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
                negative_prompt_embeds_list.append(negative_prompt_embeds)
            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
        if self.text_encoder_2 is not None: prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
        else: prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
        bs_embed, seq_len, _ = prompt_embeds.shape
        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)
        if do_classifier_free_guidance:
            seq_len = negative_prompt_embeds.shape[1]
            if self.text_encoder_2 is not None: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
            else: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(bs_embed * num_images_per_prompt, -1)
        if do_classifier_free_guidance: negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(bs_embed * num_images_per_prompt, -1)
        if self.text_encoder is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: unscale_lora_layers(self.text_encoder, lora_scale)
        if self.text_encoder_2 is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: unscale_lora_layers(self.text_encoder_2, lora_scale)
        return (prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
        dtype = next(self.image_encoder.parameters()).dtype
        if not isinstance(image, torch.Tensor): image = self.feature_extractor(image, return_tensors='pt').pixel_values
        image = image.to(device=device, dtype=dtype)
        if output_hidden_states:
            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
            uncond_image_enc_hidden_states = self.image_encoder(torch.zeros_like(image), output_hidden_states=True).hidden_states[-2]
            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
            return (image_enc_hidden_states, uncond_image_enc_hidden_states)
        else:
            image_embeds = self.image_encoder(image).image_embeds
            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
            uncond_image_embeds = torch.zeros_like(image_embeds)
            return (image_embeds, uncond_image_embeds)
    def prepare_ip_adapter_image_embeds(self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance):
        image_embeds = []
        if do_classifier_free_guidance: negative_image_embeds = []
        if ip_adapter_image_embeds is None:
            if not isinstance(ip_adapter_image, list): ip_adapter_image = [ip_adapter_image]
            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): raise ValueError(f'`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.')
            for single_ip_adapter_image, image_proj_layer in zip(ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers):
                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
                single_image_embeds, single_negative_image_embeds = self.encode_image(single_ip_adapter_image, device, 1, output_hidden_state)
                image_embeds.append(single_image_embeds[None, :])
                if do_classifier_free_guidance: negative_image_embeds.append(single_negative_image_embeds[None, :])
        else:
            for single_image_embeds in ip_adapter_image_embeds:
                if do_classifier_free_guidance:
                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
                    negative_image_embeds.append(single_negative_image_embeds)
                image_embeds.append(single_image_embeds)
        ip_adapter_image_embeds = []
        for i, single_image_embeds in enumerate(image_embeds):
            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
            if do_classifier_free_guidance:
                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
            single_image_embeds = single_image_embeds.to(device=device)
            ip_adapter_image_embeds.append(single_image_embeds)
        return ip_adapter_image_embeds
    def prepare_extra_step_kwargs(self, generator, eta):
        accepts_eta = 'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta: extra_step_kwargs['eta'] = eta
        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, prompt_2, height, width, callback_steps, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None,
    pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, ip_adapter_image=None, ip_adapter_image_embeds=None, callback_on_step_end_tensor_inputs=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 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 {type(callback_steps)}.')
        if callback_on_step_end_tensor_inputs is not None and (not all((k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs))): raise ValueError(f'`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}')
        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_2 is not None and prompt_embeds is not None: raise ValueError(f'Cannot forward both `prompt_2`: {prompt_2} 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)}')
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and (not isinstance(prompt_2, list))): raise ValueError(f'`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}')
        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`: {negative_prompt_embeds}. Please make sure to only forward one of the two.')
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None: raise ValueError(f'Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`: {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(f'`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds` {negative_prompt_embeds.shape}.')
        if prompt_embeds is not None and pooled_prompt_embeds is None: raise ValueError('If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.')
        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: raise ValueError('If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.')
        if ip_adapter_image is not None and ip_adapter_image_embeds is not None: raise ValueError('Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.')
        if ip_adapter_image_embeds is not None:
            if not isinstance(ip_adapter_image_embeds, list): raise ValueError(f'`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}')
            elif ip_adapter_image_embeds[0].ndim not in [3, 4]: raise ValueError(f'`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D')
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(width) // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size: raise ValueError(f'You have passed a list of generators of length {len(generator)}, but requested an effective batch size of {batch_size}. Make sure the batch size matches the length of the generators.')
        if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else: latents = latents.to(device)
        latents = latents * self.scheduler.init_noise_sigma
        return latents
    def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None):
        add_time_ids = list(original_size + crops_coords_top_left + target_size)
        passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
        if expected_add_embed_dim != passed_add_embed_dim: raise ValueError(f'Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.')
        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        return add_time_ids
    def upcast_vae(self):
        dtype = self.vae.dtype
        self.vae.to(dtype=torch.float32)
        use_torch_2_0_or_xformers = isinstance(self.vae.decoder.mid_block.attentions[0].processor, (AttnProcessor2_0, XFormersAttnProcessor))
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)
    def _default_height_width(self, height, width, image):
        while isinstance(image, list): image = image[0]
        if height is None:
            if isinstance(image, PIL.Image.Image): height = image.height
            elif isinstance(image, torch.Tensor): height = image.shape[-2]
            height = height // self.adapter.downscale_factor * self.adapter.downscale_factor
        if width is None:
            if isinstance(image, PIL.Image.Image): width = image.width
            elif isinstance(image, torch.Tensor): width = image.shape[-1]
            width = width // self.adapter.downscale_factor * self.adapter.downscale_factor
        return (height, width)
    def get_guidance_scale_embedding(self, w: torch.Tensor, embedding_dim: int=512, dtype: torch.dtype=torch.float32) -> torch.Tensor:
        """Returns:"""
        assert len(w.shape) == 1
        w = w * 1000.0
        half_dim = embedding_dim // 2
        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
        emb = w.to(dtype)[:, None] * emb[None, :]
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
        if embedding_dim % 2 == 1: emb = torch.nn.functional.pad(emb, (0, 1))
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb
    @property
    def guidance_scale(self): return self._guidance_scale
    @property
    def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(self, prompt: Union[str, List[str]]=None, prompt_2: Optional[Union[str, List[str]]]=None, image: PipelineImageInput=None, height: Optional[int]=None,
    width: Optional[int]=None, num_inference_steps: int=50, timesteps: List[int]=None, sigmas: List[float]=None, denoising_end: Optional[float]=None,
    guidance_scale: float=5.0, negative_prompt: Optional[Union[str, List[str]]]=None, negative_prompt_2: Optional[Union[str, List[str]]]=None,
    num_images_per_prompt: Optional[int]=1, eta: float=0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]]=None, latents: Optional[torch.Tensor]=None,
    prompt_embeds: Optional[torch.Tensor]=None, negative_prompt_embeds: Optional[torch.Tensor]=None, pooled_prompt_embeds: Optional[torch.Tensor]=None,
    negative_pooled_prompt_embeds: Optional[torch.Tensor]=None, ip_adapter_image: Optional[PipelineImageInput]=None,
    ip_adapter_image_embeds: Optional[List[torch.Tensor]]=None, output_type: Optional[str]='pil', return_dict: bool=True, callback: Optional[Callable[[int, int,
    torch.Tensor], None]]=None, callback_steps: int=1, cross_attention_kwargs: Optional[Dict[str, Any]]=None, guidance_rescale: float=0.0, original_size: Optional[Tuple[int,
    int]]=None, crops_coords_top_left: Tuple[int, int]=(0, 0), target_size: Optional[Tuple[int, int]]=None, negative_original_size: Optional[Tuple[int, int]]=None,
    negative_crops_coords_top_left: Tuple[int, int]=(0, 0), negative_target_size: Optional[Tuple[int, int]]=None, adapter_conditioning_scale: Union[float, List[float]]=1.0,
    adapter_conditioning_factor: float=1.0, clip_skip: Optional[int]=None):
        """Examples:"""
        height, width = self._default_height_width(height, width, image)
        device = self._execution_device
        if isinstance(self.adapter, MultiAdapter):
            adapter_input = []
            for one_image in image:
                one_image = _preprocess_adapter_image(one_image, height, width)
                one_image = one_image.to(device=device, dtype=self.adapter.dtype)
                adapter_input.append(one_image)
        else:
            adapter_input = _preprocess_adapter_image(image, height, width)
            adapter_input = adapter_input.to(device=device, dtype=self.adapter.dtype)
        original_size = original_size or (height, width)
        target_size = target_size or (height, width)
        self.check_inputs(prompt, prompt_2, height, width, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds,
        negative_pooled_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds)
        self._guidance_scale = guidance_scale
        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
        prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = self.encode_prompt(prompt=prompt, prompt_2=prompt_2, device=device,
        num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, negative_prompt=negative_prompt,
        negative_prompt_2=negative_prompt_2, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, clip_skip=clip_skip)
        if ip_adapter_image is not None or ip_adapter_image_embeds is not None: image_embeds = self.prepare_ip_adapter_image_embeds(ip_adapter_image, ip_adapter_image_embeds,
        device, batch_size * num_images_per_prompt, self.do_classifier_free_guidance)
        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps, sigmas)
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents)
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim).to(device=device, dtype=latents.dtype)
        if isinstance(self.adapter, MultiAdapter):
            adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)
            for k, v in enumerate(adapter_state): adapter_state[k] = v
        else:
            adapter_state = self.adapter(adapter_input)
            for k, v in enumerate(adapter_state): adapter_state[k] = v * adapter_conditioning_scale
        if num_images_per_prompt > 1:
            for k, v in enumerate(adapter_state): adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)
        if self.do_classifier_free_guidance:
            for k, v in enumerate(adapter_state): adapter_state[k] = torch.cat([v] * 2, dim=0)
        add_text_embeds = pooled_prompt_embeds
        if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
        else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
        add_time_ids = self._get_add_time_ids(original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim)
        if negative_original_size is not None and negative_target_size is not None: negative_add_time_ids = self._get_add_time_ids(negative_original_size,
        negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim)
        else: negative_add_time_ids = add_time_ids
        if self.do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
        prompt_embeds = prompt_embeds.to(device)
        add_text_embeds = add_text_embeds.to(device)
        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        if denoising_end is not None and isinstance(denoising_end, float) and (denoising_end > 0) and (denoising_end < 1):
            discrete_timestep_cutoff = int(round(self.scheduler.config.num_train_timesteps - denoising_end * self.scheduler.config.num_train_timesteps))
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                added_cond_kwargs = {'text_embeds': add_text_embeds, 'time_ids': add_time_ids}
                if ip_adapter_image is not None or ip_adapter_image_embeds is not None: added_cond_kwargs['image_embeds'] = image_embeds
                if i < int(num_inference_steps * adapter_conditioning_factor): down_intrablock_additional_residuals = [state.clone() for state in adapter_state]
                else: down_intrablock_additional_residuals = None
                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=cross_attention_kwargs,
                down_intrablock_additional_residuals=down_intrablock_additional_residuals, added_cond_kwargs=added_cond_kwargs, return_dict=False)[0]
                if self.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 self.do_classifier_free_guidance and guidance_rescale > 0.0: noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                if i == len(timesteps) - 1 or (i + 1 > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, 'order', 1)
                        callback(step_idx, t, latents)
        if not output_type == 'latent':
            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
            if needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            if needs_upcasting: self.vae.to(dtype=torch.float16)
        else:
            image = latents
            return StableDiffusionXLPipelineOutput(images=image)
        image = self.image_processor.postprocess(image, output_type=output_type)
        self.maybe_free_model_hooks()
        if not return_dict: return (image,)
        return StableDiffusionXLPipelineOutput(images=image)
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
