




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 			A			(		lowerCamelCase__		):
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   str	,  A__  :   Union[str, Any]	,  A__  :   Dict	,  A__  :   Tuple	,  A__  :   int	,  A__  :   List[Any] = 0.93	,  A__  :   str = True	,  )				->       Any:
           lowerCamelCase__        =				mixing_coeff
           lowerCamelCase__        =				leapfrog_steps

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

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




     def  __snake_case   (    self  :   str	,  A__  :   List[Any]	,  A__  :   Optional[int] = None	,  A__  :   Optional[Any] = False			)				->       Union[str, Any]:
           lowerCamelCase__        =				self.tokenizer(
               lowercase_	,  padding='max_length'	,  max_length=self.tokenizer.model_max_length	,  truncation=lowercase_	,  return_tensors='pt'	,  )

           lowerCamelCase__        =				self.text_encoder(text_inputs.input_ids.to(self.device			)			).last_hidden_state

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

           if do_classifier_free_guidance:
                 lowerCamelCase__        =				'' if negative_prompt is None else negative_prompt

                 lowerCamelCase__        =				self.tokenizer(
                     lowercase_	,  padding='max_length'	,  max_length=self.tokenizer.model_max_length	,  truncation=lowercase_	,  return_tensors='pt'	,  )

                 lowerCamelCase__        =				self.text_encoder(uncond_input.input_ids.to(self.device			)			).last_hidden_state

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

           return prompt_embeds




     def  __snake_case   (    self  :   Optional[Any]	,  A__  :   List[str]	,  A__  :   Optional[Any]			)				->       Optional[int]:
           lowerCamelCase__        =				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           lowerCamelCase__        =				self.mixing_coeff * y + (1 - self.mixing_coeff) * x

           return [x, y]




     def  __snake_case   (    self  :   List[str]	,  A__  :   int	,  A__  :   str			)				->       Tuple:
           lowerCamelCase__        =				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           lowerCamelCase__        =				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

           return [x, y]




     def  __snake_case   (    self  :   List[Any]	,  A__  :   Dict			)				->       Any:
           lowerCamelCase__        =				int(lowercase_			)

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

           return alpha_prod, 1 - alpha_prod




     def  __snake_case   (    self  :   List[Any]	,  A__  :   str	,  A__  :   List[str]	,  A__  :   List[Any]	,  A__  :   Optional[int]	,  )				->       Dict:
           lowerCamelCase__        =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           lowerCamelCase__  ,		lowerCamelCase__        =				self._get_alpha_and_beta(lowercase_			)
           lowerCamelCase__  ,		lowerCamelCase__        =				self._get_alpha_and_beta(lowercase_			)

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

           lowerCamelCase__        =				(base - b_t * model_output) / a_t

           return model_input, next_model_input.to(base.dtype			)




     def  __snake_case   (    self  :   Any	,  A__  :   str	,  A__  :   List[Any]	,  A__  :   List[str]	,  A__  :   Dict	,  )				->       Optional[int]:
           lowerCamelCase__        =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           lowerCamelCase__  ,		lowerCamelCase__        =				self._get_alpha_and_beta(lowercase_			)
           lowerCamelCase__  ,		lowerCamelCase__        =				self._get_alpha_and_beta(lowercase_			)

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

           return model_input, next_model_input.to(base.dtype			)




     @torch.no_grad()
     def  __snake_case   (    self  :   List[Any]	,  A__  :   Any			)				->       Dict:
           lowerCamelCase__        =				1 / self.vae.config.scaling_factor * latents
           lowerCamelCase__        =				self.vae.decode(lowercase_			).sample
           lowerCamelCase__        =				(image / 2 + 0.5).clamp(0	,  1			)
           return image




     @torch.no_grad()
     def  __snake_case   (    self  :   Tuple	,  A__  :   List[str]	,  A__  :   Any	,  A__  :   Union[str, Any]	,  A__  :   List[str]	,  A__  :   Dict = None	,  )				->       str:
           lowerCamelCase__        =				guidance_scale > 1.0

           lowerCamelCase__        =				image.to(device=self.device	,  dtype=text_embeds.dtype			)
           lowerCamelCase__        =				self.vae.encode(lowercase_			).latent_dist.sample(lowercase_			)

           lowerCamelCase__        =				self.vae.config.scaling_factor * latent

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

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

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

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

                       lowerCamelCase__        =				coupled_latents[j]
                       lowerCamelCase__        =				coupled_latents[k]

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

                       lowerCamelCase__        =				self.unet(lowercase_	,  lowercase_	,  encoder_hidden_states=lowercase_			).sample

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

                       lowerCamelCase__  ,		lowerCamelCase__        =				self.noise_step(
                           base=lowercase_	,  model_input=lowercase_	,  model_output=lowercase_	,  timestep=lowercase_	,  )

                       lowerCamelCase__        =				model_input

           return coupled_latents




     @torch.no_grad()
     def __call__(    self  :   List[str]	,  A__  :   Optional[Any]	,  A__  :   int	,  A__  :   Tuple	,  A__  :   Any = 3.0	,  A__  :   int = 5_0	,  A__  :   List[str] = 0.8	,  A__  :   Any = None	,  A__  :   Optional[int] = None	,  A__  :   Union[str, Any] = "pil"	,  )				->       Tuple:
           lowerCamelCase__        =				guidance_scale > 1.0

           lowerCamelCase__        =				self.image_processor.preprocess(lowercase_			)

           lowerCamelCase__        =				self._encode_prompt(lowercase_	,  lowercase_	,  lowercase_			)
           lowerCamelCase__        =				self._encode_prompt(lowercase_	,  lowercase_	,  lowercase_			)

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

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

           lowerCamelCase__        =				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			):
                       lowerCamelCase__        =				k ^ 1

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

                       lowerCamelCase__        =				coupled_latents[j]
                       lowerCamelCase__        =				coupled_latents[k]

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

                       lowerCamelCase__        =				self.unet(lowercase_	,  lowercase_	,  encoder_hidden_states=lowercase_			).sample

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

                       lowerCamelCase__  ,		lowerCamelCase__        =				self.denoise_step(
                           base=lowercase_	,  model_input=lowercase_	,  model_output=lowercase_	,  timestep=lowercase_	,  )

                       lowerCamelCase__        =				model_input

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

           # either one is fine
           lowerCamelCase__        =				coupled_latents[0]

           if output_type not in ["latent", "pt", "np", "pil"]:
                 lowerCamelCase__        =				(
                     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_			)
                 lowerCamelCase__        =				'np'

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

           return image





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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["pixel_values"]




     def __init__(    self  :   Optional[Any]	,  A__  :   bool = True	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = PILImageResampling.BILINEAR	,  A__  :   bool = True	,  A__  :   bool = False	,  A__  :   bool = True	,  A__  :   bool = True	,  A__  :   Union[int, float] = 1 / 2_5_5	,  A__  :   bool = True	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  **A__  :   List[str]	,  )				->       None:
           super().__init__(**A__			)

           lowerCamelCase__        =				size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
           if isinstance(A__	,  (tuple, list)			):
                 # The previous feature extractor size parameter was in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)

           lowerCamelCase__        =				do_resize
           lowerCamelCase__        =				size
           lowerCamelCase__        =				resample
           lowerCamelCase__        =				do_thumbnail
           lowerCamelCase__        =				do_align_long_axis
           lowerCamelCase__        =				do_pad
           lowerCamelCase__        =				do_rescale
           lowerCamelCase__        =				rescale_factor
           lowerCamelCase__        =				do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase__        =				image_std if image_std is not None else IMAGENET_STANDARD_STD




     def  __snake_case   (    self  :   str	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   Optional[Union[str, ChannelDimension]] = None			)				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

           if (output_width < output_height and input_width > input_height) or (
               output_width > output_height and input_width < input_height
           ):
                 lowerCamelCase__        =				np.rotaa(A__	,  3			)

           if data_format is not None:
                 lowerCamelCase__        =				to_channel_dimension_format(A__	,  A__			)

           return image




     def  __snake_case   (    self  :   Tuple	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   bool = False	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)

           lowerCamelCase__        =				output_width - input_width
           lowerCamelCase__        =				output_height - input_height

           if random_padding:
                 lowerCamelCase__        =				np.random.randint(low=0	,  high=delta_height + 1			)
                 lowerCamelCase__        =				np.random.randint(low=0	,  high=delta_width + 1			)
           else:
                 lowerCamelCase__        =				delta_height // 2
                 lowerCamelCase__        =				delta_width // 2

           lowerCamelCase__        =				delta_height - pad_top
           lowerCamelCase__        =				delta_width - pad_left

           lowerCamelCase__        =				((pad_top, pad_bottom), (pad_left, pad_right))
           return pad(A__	,  A__	,  data_format=A__			)




     def  __snake_case   (    self  :   int	,  *A__  :   Tuple	,  **A__  :   Dict			)				->       List[Any]:
           logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
           return self.pad_image(*A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

           # We always resize to the smallest of either the input or output size.
           lowerCamelCase__        =				min(A__	,  A__			)
           lowerCamelCase__        =				min(A__	,  A__			)

           if height == input_height and width == input_width:
                 return image

           if input_height > input_width:
                 lowerCamelCase__        =				int(input_width * height / input_height			)
           elif input_width > input_height:
                 lowerCamelCase__        =				int(input_height * width / input_width			)

           return resize(
               A__	,  size=(height, width)	,  resample=A__	,  reducing_gap=2.0	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   List[str]	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				min(size['height']	,  size['width']			)
           lowerCamelCase__        =				get_resize_output_image_size(A__	,  size=A__	,  default_to_square=A__			)
           lowerCamelCase__        =				resize(A__	,  size=A__	,  resample=A__	,  data_format=A__	,  **A__			)
           return resized_image




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   np.ndarray	,  A__  :   Union[int, float]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Optional[int]	,  )				->       Tuple:
           return rescale(A__	,  scale=A__	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Union[float, List[float]]	,  A__  :   Union[float, List[float]]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Union[str, Any]	,  )				->       np.ndarray:
           return normalize(A__	,  mean=A__	,  std=A__	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   ImageInput	,  A__  :   bool = None	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = False	,  A__  :   bool = None	,  A__  :   float = None	,  A__  :   bool = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[str, TensorType]] = None	,  A__  :   Optional[ChannelDimension] = ChannelDimension.FIRST	,  **A__  :   Any	,  )				->       PIL.Image.Image:
           lowerCamelCase__        =				do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__        =				size if size is not None else self.size
           if isinstance(A__	,  (tuple, list)			):
                 # Previous feature extractor had size in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				resample if resample is not None else self.resample
           lowerCamelCase__        =				do_thumbnail if do_thumbnail is not None else self.do_thumbnail
           lowerCamelCase__        =				do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
           lowerCamelCase__        =				do_pad if do_pad is not None else self.do_pad
           lowerCamelCase__        =				do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__        =				rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__        =				do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__        =				image_std if image_std is not None else self.image_std

           lowerCamelCase__        =				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:
                 raise ValueError('Size must be specified if do_resize is True.'			)

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

           if do_pad and size is None:
                 raise ValueError('Size must be specified if do_pad 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.
           lowerCamelCase__        =				[to_numpy_array(A__			) for image in images]

           if do_align_long_axis:
                 lowerCamelCase__        =				[self.align_long_axis(A__	,  size=A__			) for image in images]

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

           if do_thumbnail:
                 lowerCamelCase__        =				[self.thumbnail(image=A__	,  size=A__			) for image in images]

           if do_pad:
                 lowerCamelCase__        =				[self.pad_image(image=A__	,  size=A__	,  random_padding=A__			) for image in images]

           if do_rescale:
                 lowerCamelCase__        =				[self.rescale(image=A__	,  scale=A__			) for image in images]

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

           lowerCamelCase__        =				[to_channel_dimension_format(A__	,  A__			) for image in images]

           lowerCamelCase__        =				{'pixel_values': images}
           return BatchFeature(data=A__	,  tensor_type=A__			)