

"""simple docstring"""




from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class        __lowercase     (      lowerCamelCase__   ):

  def __init__(					self						:     Tuple   ,A						:     Optional[int]   ,A						:     Tuple   ,A						:     Optional[int]   ,A						:     Optional[int]   ,A						:     List[str]   ,A						:     List[Any] = 0.9_3   ,A						:     Optional[int] = True   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   mixing_coeff
   UpperCAmelCase__   :					Any   				=   leapfrog_steps

   super().__init__()
   self.register_modules(
       vae=lowercase_   ,text_encoder=lowercase_   ,tokenizer=lowercase_   ,unet=lowercase_   ,scheduler=lowercase_   ,)

   UpperCAmelCase__   :					int   				=   2 ** (len(self.vae.config.block_out_channels      ) - 1)
   UpperCAmelCase__   :					Tuple   				=   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor      )

  def 				__lowercase    (					self						:     str   ,A						:     Union[str, Any]   ,A						:     Union[str, Any] = None   ,A						:     Optional[Any] = False      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   self.tokenizer(
       lowercase_   ,padding="""max_length"""   ,max_length=self.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors="""pt"""   ,)

   UpperCAmelCase__   :					List[str]   				=   self.text_encoder(text_inputs.input_ids.to(self.device      )      ).last_hidden_state

   UpperCAmelCase__   :					Optional[Any]   				=   prompt_embeds.to(dtype=self.text_encoder.dtype   ,device=self.device      )

   if do_classifier_free_guidance:
    UpperCAmelCase__   :					Union[str, Any]   				=   """""" if negative_prompt is None else negative_prompt

    UpperCAmelCase__   :					Tuple   				=   self.tokenizer(
        lowercase_   ,padding="""max_length"""   ,max_length=self.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors="""pt"""   ,)

    UpperCAmelCase__   :					List[Any]   				=   self.text_encoder(uncond_input.input_ids.to(self.device      )      ).last_hidden_state

    UpperCAmelCase__   :					Tuple   				=   torch.cat([negative_prompt_embeds, prompt_embeds]      )

   return prompt_embeds

  def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     Dict   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
   UpperCAmelCase__   :					int   				=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

   return [x, y]

  def 				__lowercase    (					self						:     Optional[int]   ,A						:     Optional[Any]   ,A						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
   UpperCAmelCase__   :					Dict   				=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

   return [x, y]

  def 				__lowercase    (					self						:     Dict   ,A						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   int(lowercase_      )

   UpperCAmelCase__   :					List[Any]   				=   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

   return alpha_prod, 1 - alpha_prod

  def 				__lowercase    (					self						:     List[Any]   ,A						:     Any   ,A						:     Optional[int]   ,A						:     List[Any]   ,A						:     Tuple   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

   UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   self._get_alpha_and_beta(lowercase_      )
   UpperCAmelCase__				, UpperCAmelCase__   :					Union[str, Any]   				=   self._get_alpha_and_beta(lowercase_      )

   UpperCAmelCase__   :					Any   				=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
   UpperCAmelCase__   :					str   				=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

   UpperCAmelCase__   :					Tuple   				=   (base - b_t * model_output) / a_t

   return model_input, next_model_input.to(base.dtype      )

  def 				__lowercase    (					self						:     List[str]   ,A						:     Union[str, Any]   ,A						:     str   ,A						:     Tuple   ,A						:     int   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

   UpperCAmelCase__				, UpperCAmelCase__   :					Optional[int]   				=   self._get_alpha_and_beta(lowercase_      )
   UpperCAmelCase__				, UpperCAmelCase__   :					List[str]   				=   self._get_alpha_and_beta(lowercase_      )

   UpperCAmelCase__   :					Optional[int]   				=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
   UpperCAmelCase__   :					Any   				=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
   UpperCAmelCase__   :					Any   				=   a_t * base + b_t * model_output

   return model_input, next_model_input.to(base.dtype      )

  @torch.no_grad()
  def 				__lowercase    (					self						:     Tuple   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   1 / self.vae.config.scaling_factor * latents
   UpperCAmelCase__   :					List[str]   				=   self.vae.decode(lowercase_      ).sample
   UpperCAmelCase__   :					List[str]   				=   (image / 2 + 0.5).clamp(0   ,1      )
   return image

  @torch.no_grad()
  def 				__lowercase    (					self						:     Optional[int]   ,A						:     str   ,A						:     Optional[int]   ,A						:     int   ,A						:     List[str]   ,A						:     Optional[int] = None   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   guidance_scale > 1.0

   UpperCAmelCase__   :					List[str]   				=   image.to(device=self.device   ,dtype=text_embeds.dtype      )
   UpperCAmelCase__   :					Dict   				=   self.vae.encode(lowercase_      ).latent_dist.sample(lowercase_      )

   UpperCAmelCase__   :					Optional[int]   				=   self.vae.config.scaling_factor * latent

   UpperCAmelCase__   :					Optional[Any]   				=   [latent.clone(), latent.clone()]

   for i, t in tqdm(enumerate(lowercase_      )   ,total=len(lowercase_      )      ):
    UpperCAmelCase__   :					Any   				=   self.noise_mixing_layer(x=coupled_latents[0]   ,y=coupled_latents[1]      )

    # j - model_input index, k - base index
    for j in range(2      ):
     UpperCAmelCase__   :					str   				=   j ^ 1

     if self.leapfrog_steps:
      if i % 2 == 0:
       UpperCAmelCase__				, UpperCAmelCase__   :					Optional[int]   				=   j, k

     UpperCAmelCase__   :					Tuple   				=   coupled_latents[j]
     UpperCAmelCase__   :					Any   				=   coupled_latents[k]

     UpperCAmelCase__   :					Optional[int]   				=   torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

     UpperCAmelCase__   :					Dict   				=   self.unet(lowercase_   ,lowercase_   ,encoder_hidden_states=lowercase_      ).sample

     if do_classifier_free_guidance:
      UpperCAmelCase__				, UpperCAmelCase__   :					Tuple   				=   noise_pred.chunk(2      )
      UpperCAmelCase__   :					Tuple   				=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

     UpperCAmelCase__				, UpperCAmelCase__   :					Any   				=   self.noise_step(
         base=lowercase_   ,model_input=lowercase_   ,model_output=lowercase_   ,timestep=lowercase_   ,)

     UpperCAmelCase__   :					Tuple   				=   model_input

   return coupled_latents




  @torch.no_grad()
  def __call__(					self						:     Tuple   ,A						:     str   ,A						:     str   ,A						:     Dict   ,A						:     Tuple = 3.0   ,A						:     Optional[Any] = 50   ,A						:     Dict = 0.8   ,A						:     List[Any] = None   ,A						:     Union[str, Any] = None   ,A						:     str = "pil"   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   guidance_scale > 1.0

   UpperCAmelCase__   :					Optional[Any]   				=   self.image_processor.preprocess(lowercase_      )

   UpperCAmelCase__   :					Union[str, Any]   				=   self._encode_prompt(lowercase_   ,lowercase_   ,lowercase_      )
   UpperCAmelCase__   :					List[Any]   				=   self._encode_prompt(lowercase_   ,lowercase_   ,lowercase_      )

   self.scheduler.set_timesteps(lowercase_   ,self.device      )

   UpperCAmelCase__   :					str   				=   num_inference_steps - int(num_inference_steps * strength      )
   UpperCAmelCase__   :					Union[str, Any]   				=   self.scheduler.timesteps[t_limit:]
   UpperCAmelCase__   :					List[str]   				=   fwd_timesteps.flip(0      )

   UpperCAmelCase__   :					Optional[Any]   				=   self.prepare_latents(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_      )

   for i, t in tqdm(enumerate(lowercase_      )   ,total=len(lowercase_      )      ):
    # j - model_input index, k - base index
    for k in range(2      ):
     UpperCAmelCase__   :					Optional[int]   				=   k ^ 1

     if self.leapfrog_steps:
      if i % 2 == 1:
       UpperCAmelCase__				, UpperCAmelCase__   :					Optional[Any]   				=   j, k

     UpperCAmelCase__   :					List[str]   				=   coupled_latents[j]
     UpperCAmelCase__   :					Tuple   				=   coupled_latents[k]

     UpperCAmelCase__   :					str   				=   torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

     UpperCAmelCase__   :					str   				=   self.unet(lowercase_   ,lowercase_   ,encoder_hidden_states=lowercase_      ).sample

     if do_classifier_free_guidance:
      UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   noise_pred.chunk(2      )
      UpperCAmelCase__   :					Any   				=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

     UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   self.denoise_step(
         base=lowercase_   ,model_input=lowercase_   ,model_output=lowercase_   ,timestep=lowercase_   ,)

     UpperCAmelCase__   :					Optional[int]   				=   model_input

    UpperCAmelCase__   :					Any   				=   self.denoise_mixing_layer(x=coupled_latents[0]   ,y=coupled_latents[1]      )

   # either one is fine
   UpperCAmelCase__   :					Dict   				=   coupled_latents[0]

   if output_type not in ["latent", "pt", "np", "pil"]:
    UpperCAmelCase__   :					List[str]   				=   (
        f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
        """`pil`, `np`, `pt`, `latent`"""
    )
    deprecate("""Unsupported output_type"""   ,"""1.0.0"""   ,lowercase_   ,standard_warn=lowercase_      )
    UpperCAmelCase__   :					str   				=   """np"""

   if output_type == "latent":
    UpperCAmelCase__   :					Optional[int]   				=   final_latent
   else:
    UpperCAmelCase__   :					List[Any]   				=   self.decode_latents(lowercase_      )
    UpperCAmelCase__   :					List[str]   				=   self.image_processor.postprocess(lowercase_   ,output_type=lowercase_      )

   return image








"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


__UpperCAmelCase	=			logging.get_logger(__name__)







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    ["""pixel_values"""]

  def __init__(					self						:     Tuple   ,A						:     bool = True   ,A						:     Dict[str, int] = None   ,A						:     PILImageResampling = PIL.Image.NEAREST   ,A						:     bool = False   ,A						:     Dict[str, int] = None   ,A						:     Union[int, float] = 1 / 255   ,A						:     bool = False   ,A						:     bool = True   ,A						:     bool = True   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     bool = True   ,**A						:     List[str]   ,):






   '''simple docstring'''
   super().__init__(**A      )
   UpperCAmelCase__   :					Optional[Any]   				=   size if size is not None else {"""height""": 346, """width""": 346}
   UpperCAmelCase__   :					Union[str, Any]   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Optional[int]   				=   do_resize
   UpperCAmelCase__   :					Optional[int]   				=   size
   UpperCAmelCase__   :					Optional[int]   				=   resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop
   UpperCAmelCase__   :					int   				=   crop_size
   UpperCAmelCase__   :					List[Any]   				=   do_rescale
   UpperCAmelCase__   :					List[str]   				=   rescale_factor
   UpperCAmelCase__   :					List[str]   				=   rescale_offset
   UpperCAmelCase__   :					Any   				=   do_normalize
   UpperCAmelCase__   :					Tuple   				=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase__   :					int   				=   image_std if image_std is not None else IMAGENET_STANDARD_STD
   UpperCAmelCase__   :					Optional[int]   				=   include_top

  def 				__lowercase    (					self						:     Tuple   ,A						:     np.ndarray   ,A						:     Dict[str, int]   ,A						:     PILImageResampling = PIL.Image.NEAREST   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   get_size_dict(A      )
   if "height" not in size or "width" not in size:
    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
   return resize(
       A   ,size=(size["""height"""], size["""width"""])   ,resample=A   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     np.ndarray   ,A						:     Dict[str, int]   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Union[str, Any]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   get_size_dict(A      )
   if "height" not in size or "width" not in size:
    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
   return center_crop(A   ,size=(size["""height"""], size["""width"""])   ,data_format=A   ,**A      )

  def 				__lowercase    (					self						:     List[Any]   ,A						:     np.ndarray   ,A						:     Union[int, float]   ,A						:     bool = True   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     Any   ,):






   '''simple docstring'''
   if offset:
    UpperCAmelCase__   :					Tuple   				=   (image - 1_2_7.5) * scale
    if data_format is not None:
     UpperCAmelCase__   :					Any   				=   to_channel_dimension_format(A   ,A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   rescaled_image.astype(np.floataa      )
   else:
    UpperCAmelCase__   :					List[str]   				=   rescale(A   ,scale=A   ,data_format=A   ,**A      )
   return rescaled_image

  def 				__lowercase    (					self						:     Optional[Any]   ,A						:     np.ndarray   ,A						:     Union[float, List[float]]   ,A						:     Union[float, List[float]]   ,A						:     Optional[Union[str, ChannelDimension]] = None   ,**A						:     List[Any]   ,):






   '''simple docstring'''
   return normalize(A   ,mean=A   ,std=A   ,data_format=A   ,**A      )




  def 				__lowercase    (					self						:     Optional[int]   ,A						:     ImageInput   ,A						:     bool = None   ,A						:     Dict[str, int] = None   ,A						:     str=None   ,A						:     bool = None   ,A						:     Dict[str, int] = None   ,A						:     bool = None   ,A						:     float = None   ,A						:     bool = None   ,A						:     bool = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     Optional[Union[float, List[float]]] = None   ,A						:     bool = None   ,A						:     Optional[Union[str, TensorType]] = None   ,A						:     ChannelDimension = ChannelDimension.FIRST   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__   :					Optional[int]   				=   resample if resample is not None else self.resample
   UpperCAmelCase__   :					List[str]   				=   do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__   :					List[str]   				=   do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__   :					Any   				=   rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__   :					str   				=   rescale_offset if rescale_offset is not None else self.rescale_offset
   UpperCAmelCase__   :					Union[str, Any]   				=   do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase__   :					int   				=   image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase__   :					Any   				=   image_std if image_std is not None else self.image_std
   UpperCAmelCase__   :					List[Any]   				=   include_top if include_top is not None else self.include_top

   UpperCAmelCase__   :					List[Any]   				=   size if size is not None else self.size
   UpperCAmelCase__   :					int   				=   get_size_dict(A      )
   UpperCAmelCase__   :					Any   				=   crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__   :					str   				=   get_size_dict(A   ,param_name="""crop_size"""      )

   UpperCAmelCase__   :					Any   				=   make_list_of_images(A      )

   if not valid_images(A      ):
    raise ValueError(
        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
        """torch.Tensor, tf.Tensor or jax.ndarray."""      )

   if do_resize and size is None or resample is None:
    raise ValueError("""Size and resample must be specified if do_resize is True."""      )

   if do_center_crop and crop_size is None:
    raise ValueError("""Crop size must be specified if do_center_crop is True."""      )

   if do_rescale and rescale_factor is None:
    raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

   # All transformations expect numpy arrays.
   UpperCAmelCase__   :					List[str]   				=   [to_numpy_array(A      ) for image in images]

   if do_resize:
    UpperCAmelCase__   :					str   				=   [self.resize(image=A   ,size=A   ,resample=A      ) for image in images]

   if do_center_crop:
    UpperCAmelCase__   :					Tuple   				=   [self.center_crop(image=A   ,size=A      ) for image in images]

   if do_rescale:
    UpperCAmelCase__   :					int   				=   [self.rescale(image=A   ,scale=A   ,offset=A      ) for image in images]

   if do_normalize:
    UpperCAmelCase__   :					int   				=   [self.normalize(image=A   ,mean=A   ,std=A      ) for image in images]

   if include_top:
    UpperCAmelCase__   :					Dict   				=   [self.normalize(image=A   ,mean=[0, 0, 0]   ,std=A      ) for image in images]

   UpperCAmelCase__   :					Optional[Any]   				=   [to_channel_dimension_format(A   ,A      ) for image in images]

   UpperCAmelCase__   :					Dict   				=   {"""pixel_values""": images}
   return BatchFeature(data=A   ,tensor_type=A      )





