'''
    #################################################################################################################################################################################################
    # 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, Union
import numpy as np
import PIL.Image
import torch
from packaging import version
from sapiens_transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer
from ....configuration_utils import FrozenDict
from ....image_processor import PipelineImageInput, VaeImageProcessor
from ....loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
from ....models import AutoencoderKL, ImageProjection, UNet2DConditionModel
from ....models.lora import adjust_lora_scale_text_encoder
from ....schedulers import KarrasDiffusionSchedulers
from ....utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, 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.safety_checker import StableDiffusionSafetyChecker
from .modeling_roberta_series import RobertaSeriesModelWithTransformation
from .pipeline_output import AltDiffusionPipelineOutput
EXAMPLE_DOC_STRING = '\n    Examples:\n        ```py\n        >>> import requests\n        >>> import torch\n        >>> from PIL import Image\n        >>> from io import BytesIO\n\n        >>> from sapiens_transformers.diffusers import AltDiffusionImg2ImgPipeline\n\n        >>> device = "cuda"\n        >>> model_id_or_path = "BAAI/AltDiffusion-m9"\n        >>> pipe = AltDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"\n\n        >>> response = requests.get(url)\n        >>> init_image = Image.open(BytesIO(response.content)).convert("RGB")\n        >>> init_image = init_image.resize((768, 512))\n\n        >>> # "A fantasy landscape, trending on artstation"\n        >>> prompt = "幻想风景, artstation"\n\n        >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n        >>> images[0].save("幻想风景.png")\n        ```\n'
def retrieve_latents(encoder_output: torch.Tensor, generator: Optional[torch.Generator]=None, sample_mode: str='sample'):
    if hasattr(encoder_output, 'latent_dist') and sample_mode == 'sample': return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, 'latent_dist') and sample_mode == 'argmax': return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, 'latents'): return encoder_output.latents
    else: raise AttributeError('Could not access latents of provided encoder_output')
def preprocess(image):
    deprecation_message = 'The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead'
    deprecate('preprocess', '1.0.0', deprecation_message, standard_warn=False)
    if isinstance(image, torch.Tensor): return image
    elif isinstance(image, PIL.Image.Image): image = [image]
    if isinstance(image[0], PIL.Image.Image):
        w, h = image[0].size
        w, h = (x - x % 8 for x in (w, h))
        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION['lanczos']))[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 = 2.0 * image - 1.0
        image = torch.from_numpy(image)
    elif isinstance(image[0], torch.Tensor): image = torch.cat(image, dim=0)
    return image
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 AltDiffusionImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin):
    """Args:"""
    model_cpu_offload_seq = 'text_encoder->image_encoder->unet->vae'
    _optional_components = ['safety_checker', 'feature_extractor', 'image_encoder']
    _exclude_from_cpu_offload = ['safety_checker']
    _callback_tensor_inputs = ['latents', 'prompt_embeds', 'negative_prompt_embeds']
    def __init__(self, vae: AutoencoderKL, text_encoder: RobertaSeriesModelWithTransformation, tokenizer: XLMRobertaTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers,
    safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection=None, requires_safety_checker: bool=True):
        super().__init__()
        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` 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 Sapiens 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 Sapiens 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 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 Sapiens 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, image_encoder=image_encoder)
        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)
    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.Tensor]=None,
    negative_prompt_embeds: Optional[torch.Tensor]=None, lora_scale: Optional[float]=None, **kwargs):
        deprecation_message = '`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.'
        deprecate('_encode_prompt()', '1.0.0', deprecation_message, standard_warn=False)
        prompt_embeds_tuple = self.encode_prompt(prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, **kwargs)
        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
        return prompt_embeds
    def encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.Tensor]=None, negative_prompt_embeds: Optional[torch.Tensor]=None,
    lora_scale: Optional[float]=None, clip_skip: Optional[int]=None):
        """Args:"""
        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
            self._lora_scale = lora_scale
            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 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]
        if prompt_embeds is None:
            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])
            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
            if clip_skip is None:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True)
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
        if self.text_encoder is not None: prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None: prompt_embeds_dtype = self.unet.dtype
        else: prompt_embeds_dtype = prompt_embeds.dtype
        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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 and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None: uncond_tokens = [''] * batch_size
            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)} != {type(prompt)}.')
            elif isinstance(negative_prompt, str): uncond_tokens = [negative_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
            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:
            seq_len = negative_prompt_embeds.shape[1]
            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_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)
        if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND: unscale_lora_layers(self.text_encoder, lora_scale)
        return (prompt_embeds, negative_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 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):
        deprecation_message = 'The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead'
        deprecate('decode_latents', '1.0.0', deprecation_message, standard_warn=False)
        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)
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image
    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, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, callback_on_step_end_tensor_inputs=None):
        if strength < 0 or strength > 1: raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')
        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 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`: {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}.')
    def get_timesteps(self, num_inference_steps, strength, device):
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order:]
        return (timesteps, num_inference_steps - t_start)
    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError(f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}')
        image = image.to(device=device, dtype=dtype)
        batch_size = batch_size * num_images_per_prompt
        if image.shape[1] == 4: init_latents = image
        else:
            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.')
            elif isinstance(generator, list):
                init_latents = [retrieve_latents(self.vae.encode(image[i:i + 1]), generator=generator[i]) for i in range(batch_size)]
                init_latents = torch.cat(init_latents, dim=0)
            else: init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
            init_latents = self.vae.config.scaling_factor * init_latents
        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
            deprecation_message = f'You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial images (`image`). Initial images are now duplicating to match the number of text prompts. Note that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update your script to pass as many initial images as text prompts to suppress this warning.'
            deprecate('len(prompt) != len(image)', '1.0.0', deprecation_message, standard_warn=False)
            additional_image_per_prompt = batch_size // init_latents.shape[0]
            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError(f'Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.')
        else: init_latents = torch.cat([init_latents], dim=0)
        shape = init_latents.shape
        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
        latents = init_latents
        return latents
    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
        """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 clip_skip(self): return self._clip_skip
    @property
    def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
    @property
    def cross_attention_kwargs(self): return self._cross_attention_kwargs
    @property
    def num_timesteps(self): return self._num_timesteps
    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(self, prompt: Union[str, List[str]]=None, image: PipelineImageInput=None, strength: float=0.8, num_inference_steps: Optional[int]=50, timesteps: List[int]=None, sigmas: List[float]=None,
    guidance_scale: Optional[float]=7.5, negative_prompt: Optional[Union[str, List[str]]]=None, num_images_per_prompt: Optional[int]=1, eta: Optional[float]=0.0, generator: Optional[Union[torch.Generator,
    List[torch.Generator]]]=None, prompt_embeds: Optional[torch.Tensor]=None, negative_prompt_embeds: Optional[torch.Tensor]=None, ip_adapter_image: Optional[PipelineImageInput]=None,
    output_type: Optional[str]='pil', return_dict: bool=True, cross_attention_kwargs: Optional[Dict[str, Any]]=None, clip_skip: int=None, callback_on_step_end: Optional[Callable[[int,
    int, Dict], None]]=None, callback_on_step_end_tensor_inputs: List[str]=['latents'], **kwargs):
        """Examples:"""
        callback = kwargs.pop('callback', None)
        callback_steps = kwargs.pop('callback_steps', None)
        if callback is not None: deprecate('callback', '1.0.0', 'Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`')
        if callback_steps is not None: deprecate('callback_steps', '1.0.0', 'Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`')
        self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, callback_on_step_end_tensor_inputs)
        self._guidance_scale = guidance_scale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs
        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
        text_encoder_lora_scale = self.cross_attention_kwargs.get('scale', None) if self.cross_attention_kwargs is not None else None
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(prompt, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip)
        if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
        if ip_adapter_image is not None:
            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt, output_hidden_state)
            if self.do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds])
        image = self.image_processor.preprocess(image)
        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps, sigmas)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
        latents = self.prepare_latents(image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator)
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        added_cond_kwargs = {'image_embeds': image_embeds} if ip_adapter_image is not None else None
        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)
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        self._num_timesteps = len(timesteps)
        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)
                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond,
                cross_attention_kwargs=self.cross_attention_kwargs, 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 + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
                    latents = callback_outputs.pop('latents', latents)
                    prompt_embeds = callback_outputs.pop('prompt_embeds', prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop('negative_prompt_embeds', negative_prompt_embeds)
                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':
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None
        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)
        self.maybe_free_model_hooks()
        if not return_dict: return (image, has_nsfw_concept)
        return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
'''
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
'''
