




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       AutoencoderKL       ,				snake_case    :       CLIPTextModel       ,				snake_case    :       CLIPTokenizer       ,				snake_case    :       UNetaDConditionModel       ,				snake_case    :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]       ,				):
          super().__init__()

          self.register_modules(vae=snake_case       ,				text_encoder=snake_case       ,				tokenizer=snake_case       ,				unet=snake_case       ,				scheduler=snake_case )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Union[str, Any] ):
          with torch.no_grad():
              __UpperCamelCase     						=  self.vae.encode(tfms.ToTensor()(snake_case ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
              __UpperCamelCase     						=  0.1_82_15 * latent.latent_dist.sample()
          return latent







      def 					snake_case		(   self    :       int       ,				snake_case    :       Optional[Any] ):
          __UpperCamelCase     						=  (1 / 0.1_82_15) * latent
          with torch.no_grad():
              __UpperCamelCase     						=  self.vae.decode(snake_case ).sample
          __UpperCamelCase     						=  (img / 2 + 0.5).clamp(0       ,				1 )
          __UpperCamelCase     						=  img.detach().cpu().permute(0       ,				2       ,				3       ,				1 ).numpy()
          __UpperCamelCase     						=  (img * 255).round().astype('''uint8''' )
          return Image.fromarray(img[0] )







      def 					snake_case		(   self    :       int       ,				snake_case    :       Any ):
          __UpperCamelCase     						=  self.tokenizer(
              snake_case       ,				padding='''max_length'''       ,				max_length=self.tokenizer.model_max_length       ,				truncation=snake_case       ,				return_tensors='''pt'''       ,				)

          __UpperCamelCase     						=  self.text_encoder(text_input.input_ids.to(self.device ) )[0]

          __UpperCamelCase     						=  self.tokenizer(
              ''''''       ,				padding='''max_length'''       ,				max_length=self.tokenizer.model_max_length       ,				truncation=snake_case       ,				return_tensors='''pt'''       ,				)

          __UpperCamelCase     						=  self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

          return torch.cat([uncond_embedding, text_embedding] )







      def __call__(   self    :       Dict       ,				snake_case    :       Image.Image       ,				snake_case    :       str       ,				snake_case    :       float = 0.3       ,				snake_case    :       float = 0.6       ,				snake_case    :       float = 0.5       ,				snake_case    :       int = 42       ,				snake_case    :       int = 50       ,				snake_case    :       float = 7.5       ,				):
          __UpperCamelCase     						=  steps - int(kmin * steps )
          __UpperCamelCase     						=  steps - int(kmax * steps )

          __UpperCamelCase     						=  self.prep_text(snake_case )

          self.scheduler.set_timesteps(snake_case )

          __UpperCamelCase						,					__UpperCamelCase     						=  img.size
          __UpperCamelCase     						=  self.encode(snake_case )

          torch.manual_seed(snake_case )
          __UpperCamelCase     						=  torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)       ,				).to(self.device )

          __UpperCamelCase     						=  self.scheduler.add_noise(
              snake_case       ,				snake_case       ,				timesteps=self.scheduler.timesteps[tmax]       ,				)

          __UpperCamelCase     						=  torch.cat([latents] * 2 )

          __UpperCamelCase     						=  self.scheduler.scale_model_input(snake_case       ,				self.scheduler.timesteps[tmax] )

          with torch.no_grad():
              __UpperCamelCase     						=  self.unet(
                  snake_case       ,				self.scheduler.timesteps[tmax]       ,				encoder_hidden_states=snake_case       ,				).sample

          __UpperCamelCase						,					__UpperCamelCase     						=  pred.chunk(2 )
          __UpperCamelCase     						=  pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __UpperCamelCase     						=  self.scheduler.step(snake_case       ,				self.scheduler.timesteps[tmax]       ,				snake_case ).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps ) ):
              if i > tmax:
                  if i < tmin:  # layout generation phase
                      __UpperCamelCase     						=  self.scheduler.add_noise(
                          snake_case       ,				snake_case       ,				timesteps=snake_case       ,				)

                      __UpperCamelCase     						=  (mix_factor * latents) + (
                          1 - mix_factor
                      ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                      __UpperCamelCase     						=  torch.cat([input] * 2 )

                  else:  # content generation phase
                      __UpperCamelCase     						=  torch.cat([latents] * 2 )

                  __UpperCamelCase     						=  self.scheduler.scale_model_input(snake_case       ,				snake_case )

                  with torch.no_grad():
                      __UpperCamelCase     						=  self.unet(
                          snake_case       ,				snake_case       ,				encoder_hidden_states=snake_case       ,				).sample

                  __UpperCamelCase						,					__UpperCamelCase     						=  pred.chunk(2 )
                  __UpperCamelCase     						=  pred_uncond + guidance_scale * (pred_text - pred_uncond)

                  __UpperCamelCase     						=  self.scheduler.step(snake_case       ,				snake_case       ,				snake_case ).prev_sample

          return self.decode(snake_case )












from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .consistency_models import ConsistencyModelPipeline
     from .dance_diffusion import DanceDiffusionPipeline
     from .ddim import DDIMPipeline
     from .ddpm import DDPMPipeline
     from .dit import DiTPipeline
     from .latent_diffusion import LDMSuperResolutionPipeline
     from .latent_diffusion_uncond import LDMPipeline
     from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
     from .pndm import PNDMPipeline
     from .repaint import RePaintPipeline
     from .score_sde_ve import ScoreSdeVePipeline
     from .stochastic_karras_ve import KarrasVePipeline

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
     from .audioldm import AudioLDMPipeline
     from .controlnet import (
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
     )
     from .deepfloyd_if import (
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
     )
     from .kandinsky import (
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
     )
     from .kandinskya_a import (
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
     )
     from .latent_diffusion import LDMTextToImagePipeline
     from .paint_by_example import PaintByExamplePipeline
     from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
     from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
     from .stable_diffusion import (
         CycleDiffusionPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
     )
     from .stable_diffusion_safe import StableDiffusionPipelineSafe
     from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
     from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
     from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
     from .versatile_diffusion import (
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
     )
     from .vq_diffusion import VQDiffusionPipeline


try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_onnx_objects import *  # noqa F403
else:
     from .onnx_utils import OnnxRuntimeModel

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .stable_diffusion import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .controlnet import FlaxStableDiffusionControlNetPipeline
     from .stable_diffusion import (
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )
try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline
