






"""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


lowerCAmelCase_		:			Union[str, Any]				   =					logging.get_logger(__name__)





class      UpperCamelCase_       (				a_     ):
  _A				:    Dict =		['pixel_values']



  def __init__(					self       ,						snake_case__ = True       ,						snake_case__ = None       ,						snake_case__ = PIL.Image.NEAREST       ,						snake_case__ = False       ,						snake_case__ = None       ,						snake_case__ = 1 / 2_55       ,						snake_case__ = False       ,						snake_case__ = True       ,						snake_case__ = True       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = True       ,						**snake_case__       ,						)	->	None:



         """simple docstring"""





         super().__init__(**snake_case__ )
         UpperCAmelCase					=	size if size is not None else {"""height""": 3_46, """width""": 3_46}
         UpperCAmelCase					=	get_size_dict(snake_case__ )
         UpperCAmelCase					=	crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         UpperCAmelCase					=	get_size_dict(snake_case__       ,						param_name="""crop_size""" )

         UpperCAmelCase					=	do_resize
         UpperCAmelCase					=	size
         UpperCAmelCase					=	resample
         UpperCAmelCase					=	do_center_crop
         UpperCAmelCase					=	crop_size
         UpperCAmelCase					=	do_rescale
         UpperCAmelCase					=	rescale_factor
         UpperCAmelCase					=	rescale_offset
         UpperCAmelCase					=	do_normalize
         UpperCAmelCase					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
         UpperCAmelCase					=	include_top



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = PIL.Image.NEAREST       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         UpperCAmelCase					=	get_size_dict(snake_case__ )
         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(
             snake_case__       ,						size=(size["""height"""], size["""width"""])       ,						resample=snake_case__       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         UpperCAmelCase					=	get_size_dict(snake_case__ )
         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(snake_case__       ,						size=(size["""height"""], size["""width"""])       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = True       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	Optional[Any]:



         """simple docstring"""





         if offset:
                UpperCAmelCase					=	(image - 127.5) * scale
                if data_format is not None:
                       UpperCAmelCase					=	to_channel_dimension_format(snake_case__       ,						snake_case__ )
                UpperCAmelCase					=	rescaled_image.astype(np.floataa )
         else:
                UpperCAmelCase					=	rescale(snake_case__       ,						scale=snake_case__       ,						data_format=snake_case__       ,						**snake_case__ )
         return rescaled_image



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         return normalize(snake_case__       ,						mean=snake_case__       ,						std=snake_case__       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__=None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = ChannelDimension.FIRST       ,						**snake_case__       ,						)	->	PIL.Image.Image:



         """simple docstring"""





         UpperCAmelCase					=	do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase					=	resample if resample is not None else self.resample
         UpperCAmelCase					=	do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase					=	do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase					=	rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase					=	rescale_offset if rescale_offset is not None else self.rescale_offset
         UpperCAmelCase					=	do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase					=	image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase					=	image_std if image_std is not None else self.image_std
         UpperCAmelCase					=	include_top if include_top is not None else self.include_top

         UpperCAmelCase					=	size if size is not None else self.size
         UpperCAmelCase					=	get_size_dict(snake_case__ )
         UpperCAmelCase					=	crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase					=	get_size_dict(snake_case__       ,						param_name="""crop_size""" )

         UpperCAmelCase					=	make_list_of_images(snake_case__ )

         if not valid_images(snake_case__ ):
                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					=	[to_numpy_array(snake_case__ ) for image in images]

         if do_resize:
                UpperCAmelCase					=	[self.resize(image=snake_case__       ,						size=snake_case__       ,						resample=snake_case__ ) for image in images]

         if do_center_crop:
                UpperCAmelCase					=	[self.center_crop(image=snake_case__       ,						size=snake_case__ ) for image in images]

         if do_rescale:
                UpperCAmelCase					=	[self.rescale(image=snake_case__       ,						scale=snake_case__       ,						offset=snake_case__ ) for image in images]

         if do_normalize:
                UpperCAmelCase					=	[self.normalize(image=snake_case__       ,						mean=snake_case__       ,						std=snake_case__ ) for image in images]

         if include_top:
                UpperCAmelCase					=	[self.normalize(image=snake_case__       ,						mean=[0, 0, 0]       ,						std=snake_case__ ) for image in images]

         UpperCAmelCase					=	[to_channel_dimension_format(snake_case__       ,						snake_case__ ) for image in images]

         UpperCAmelCase					=	{"""pixel_values""": images}
         return BatchFeature(data=snake_case__       ,						tensor_type=snake_case__ )








"""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      UpperCamelCase_       (				a_     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.93       ,						snake_case__ = True       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	mixing_coeff
         UpperCAmelCase					=	leapfrog_steps

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

         UpperCAmelCase					=	2 ** (len(self.vae.config.block_out_channels ) - 1)
         UpperCAmelCase					=	VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False )	->	Dict:



         """simple docstring"""





         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_inputs.input_ids.to(self.device ) ).last_hidden_state

         UpperCAmelCase					=	prompt_embeds.to(dtype=self.text_encoder.dtype       ,						device=self.device )

         if do_classifier_free_guidance:
                UpperCAmelCase					=	"""""" if negative_prompt is None else negative_prompt

                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(uncond_input.input_ids.to(self.device ) ).last_hidden_state

                UpperCAmelCase					=	torch.cat([negative_prompt_embeds, prompt_embeds] )

         return prompt_embeds



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         UpperCAmelCase					=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         UpperCAmelCase					=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	int(snake_case__ )

         UpperCAmelCase					=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         UpperCAmelCase					=	(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         UpperCAmelCase					=	a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype )



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	1 / self.vae.config.scaling_factor * latents
         UpperCAmelCase					=	self.vae.decode(snake_case__ ).sample
         UpperCAmelCase					=	(image / 2 + 0.5).clamp(0       ,						1 )
         return image



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	image.to(device=self.device       ,						dtype=text_embeds.dtype )
         UpperCAmelCase					=	self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )

         UpperCAmelCase					=	self.vae.config.scaling_factor * latent

         UpperCAmelCase					=	[latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                UpperCAmelCase					=	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					=	j ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 0:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

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

                       UpperCAmelCase			,				UpperCAmelCase					=	self.noise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

         return coupled_latents



  @torch.no_grad()
  def __call__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 3.0       ,						snake_case__ = 50       ,						snake_case__ = 0.8       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = "pil"       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	self.image_processor.preprocess(snake_case__ )

         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )
         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )

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

         UpperCAmelCase					=	num_inference_steps - int(num_inference_steps * strength )
         UpperCAmelCase					=	self.scheduler.timesteps[t_limit:]
         UpperCAmelCase					=	fwd_timesteps.flip(0 )

         UpperCAmelCase					=	self.prepare_latents(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                # j - model_input index, k - base index
                for k in range(2 ):
                       UpperCAmelCase					=	k ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 1:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

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

                       UpperCAmelCase			,				UpperCAmelCase					=	self.denoise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

                UpperCAmelCase					=	self.denoise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

         # either one is fine
         UpperCAmelCase					=	coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
                UpperCAmelCase					=	(
                    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"""       ,						snake_case__       ,						standard_warn=snake_case__ )
                UpperCAmelCase					=	"""np"""

         if output_type == "latent":
                UpperCAmelCase					=	final_latent
         else:
                UpperCAmelCase					=	self.decode_latents(snake_case__ )
                UpperCAmelCase					=	self.image_processor.postprocess(snake_case__       ,						output_type=snake_case__ )

         return image
