'''
    #################################################################################################################################################################################################
    # 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 Callable, List, Optional, Union
import PIL.Image
import torch
from packaging import version
from sapiens_transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import deprecate
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
class StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMixin):
    """Args:"""
    _optional_components = ['safety_checker']
    model_cpu_offload_seq = 'image_encoder->unet->vae'
    _exclude_from_cpu_offload = ['safety_checker']
    def __init__(self, vae: AutoencoderKL, image_encoder: CLIPVisionModelWithProjection, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker,
    feature_extractor: CLIPImageProcessor, requires_safety_checker: bool=True):
        super().__init__()
        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 you're 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, image_encoder=image_encoder, 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)
    def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
        dtype = next(self.image_encoder.parameters()).dtype
        if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors='pt').pixel_values
        image = image.to(device=device, dtype=dtype)
        image_embeddings = self.image_encoder(image).image_embeds
        image_embeddings = image_embeddings.unsqueeze(1)
        bs_embed, seq_len, _ = image_embeddings.shape
        image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
        image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
        if do_classifier_free_guidance:
            negative_prompt_embeds = torch.zeros_like(image_embeddings)
            image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
        return image_embeddings
    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, image, height, width, callback_steps):
        if not isinstance(image, torch.Tensor) and (not isinstance(image, PIL.Image.Image)) and (not isinstance(image, list)): raise ValueError(f'`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is {type(image)}')
        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 {type(callback_steps)}.')
    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
    @torch.no_grad()
    def __call__(self, image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor], height: Optional[int]=None, width: Optional[int]=None, num_inference_steps: int=50,
    guidance_scale: float=7.5, 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, output_type: Optional[str]='pil', return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.Tensor], None]]=None, callback_steps: int=1):
        """Examples:"""
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor
        self.check_inputs(image, height, width, callback_steps)
        if isinstance(image, PIL.Image.Image): batch_size = 1
        elif isinstance(image, list): batch_size = len(image)
        else: batch_size = image.shape[0]
        device = self._execution_device
        do_classifier_free_guidance = guidance_scale > 1.0
        image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(batch_size * num_images_per_prompt, num_channels_latents, height, width, image_embeddings.dtype, device, generator, latents)
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        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 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=image_embeddings).sample
                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)
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
                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)
        self.maybe_free_model_hooks()
        if not output_type == 'latent':
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.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 StableDiffusionPipelineOutput(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.                                                                 #
    #################################################################################################################################################################################################
'''
