

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     (		UpperCAmelCase ):


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_ = 0.93  ,    A_ = True  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			mixing_coeff
										_UpperCAmelCase				:						List[Any]       						=			leapfrog_steps

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

										_UpperCAmelCase				:						Optional[Any]       						=			2 ** (len(self.vae.config.block_out_channels		) - 1)
										_UpperCAmelCase				:						List[str]       						=			VaeImageProcessor(vae_scale_factor=self.vae_scale_factor		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.tokenizer(
										    A_  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=A_  ,    return_tensors="pt"  ,    )

										_UpperCAmelCase				:						Optional[int]       						=			self.text_encoder(text_inputs.input_ids.to(self.device		)		).last_hidden_state

										_UpperCAmelCase				:						Any       						=			prompt_embeds.to(dtype=self.text_encoder.dtype  ,    device=self.device		)

										if do_classifier_free_guidance:
														_UpperCAmelCase				:						Any       						=			"" if negative_prompt is None else negative_prompt

														_UpperCAmelCase				:						Any       						=			self.tokenizer(
														    A_  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=A_  ,    return_tensors="pt"  ,    )

														_UpperCAmelCase				:						Tuple       						=			self.text_encoder(uncond_input.input_ids.to(self.device		)		).last_hidden_state

														_UpperCAmelCase				:						Any       						=			torch.cat([negative_prompt_embeds, prompt_embeds]		)

										return prompt_embeds


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			self.mixing_coeff * x + (1 - self.mixing_coeff) * y
										_UpperCAmelCase				:						Optional[int]       						=			self.mixing_coeff * y + (1 - self.mixing_coeff) * x

										return [x, y]


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
										_UpperCAmelCase				:						Union[str, Any]       						=			(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

										return [x, y]


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			int(A_		)

										_UpperCAmelCase				:						Union[str, Any]       						=			self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

										return alpha_prod, 1 - alpha_prod


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			self._get_alpha_and_beta(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			self._get_alpha_and_beta(A_		)

										_UpperCAmelCase				:						Dict       						=			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
										_UpperCAmelCase				:						Dict       						=			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

										_UpperCAmelCase				:						Any       						=			(base - b_t * model_output) / a_t

										return model_input, next_model_input.to(base.dtype		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			self._get_alpha_and_beta(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Tuple       						=			self._get_alpha_and_beta(A_		)

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

										return model_input, next_model_input.to(base.dtype		)


						@torch.no_grad()
						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			1 / self.vae.config.scaling_factor * latents
										_UpperCAmelCase				:						Optional[int]       						=			self.vae.decode(A_		).sample
										_UpperCAmelCase				:						int       						=			(image / 2 + 0.5).clamp(0  ,    1		)
										return image


						@torch.no_grad()
						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			guidance_scale > 1.0

										_UpperCAmelCase				:						int       						=			image.to(device=self.device  ,    dtype=text_embeds.dtype		)
										_UpperCAmelCase				:						Tuple       						=			self.vae.encode(A_		).latent_dist.sample(A_		)

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

										_UpperCAmelCase				:						int       						=			[latent.clone(), latent.clone()]

										for i, t in tqdm(enumerate(A_		)  ,    total=len(A_		)		):
														_UpperCAmelCase				:						int       						=			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				:						Any       						=			j, k

																		_UpperCAmelCase				:						str       						=			coupled_latents[j]
																		_UpperCAmelCase				:						List[str]       						=			coupled_latents[k]

																		_UpperCAmelCase				:						Any       						=			torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																		_UpperCAmelCase				:						Optional[Any]       						=			self.unet(A_  ,    A_  ,    encoder_hidden_states=A_		).sample

																		if do_classifier_free_guidance:
																						_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			noise_pred.chunk(2		)
																						_UpperCAmelCase				:						Tuple       						=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			self.noise_step(
																		    base=A_  ,    model_input=A_  ,    model_output=A_  ,    timestep=A_  ,    )

																		_UpperCAmelCase				:						Optional[Any]       						=			model_input

										return coupled_latents





						@torch.no_grad()
						def __call__(				self  ,    A_  ,    A_  ,    A_  ,    A_ = 3.0  ,    A_ = 50  ,    A_ = 0.8  ,    A_ = None  ,    A_ = None  ,    A_ = "pil"  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			guidance_scale > 1.0

										_UpperCAmelCase				:						Optional[int]       						=			self.image_processor.preprocess(A_		)

										_UpperCAmelCase				:						List[Any]       						=			self._encode_prompt(A_  ,    A_  ,    A_		)
										_UpperCAmelCase				:						Optional[int]       						=			self._encode_prompt(A_  ,    A_  ,    A_		)

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

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

										_UpperCAmelCase				:						Optional[int]       						=			self.prepare_latents(A_  ,    A_  ,    A_  ,    A_  ,    A_		)

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

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

																		_UpperCAmelCase				:						Optional[Any]       						=			coupled_latents[j]
																		_UpperCAmelCase				:						List[str]       						=			coupled_latents[k]

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

																		_UpperCAmelCase				:						List[str]       						=			self.unet(A_  ,    A_  ,    encoder_hidden_states=A_		).sample

																		if do_classifier_free_guidance:
																						_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			noise_pred.chunk(2		)
																						_UpperCAmelCase				:						List[str]       						=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			self.denoise_step(
																		    base=A_  ,    model_input=A_  ,    model_output=A_  ,    timestep=A_  ,    )

																		_UpperCAmelCase				:						Union[str, Any]       						=			model_input

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

										# either one is fine
										_UpperCAmelCase				:						Tuple       						=			coupled_latents[0]

										if output_type not in ["latent", "pt", "np", "pil"]:
														_UpperCAmelCase				:						Any       						=			(
														    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"  ,    A_  ,    standard_warn=A_		)
														_UpperCAmelCase				:						Union[str, Any]       						=			"np"

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

										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_						=					logging.get_logger(__name__)


if is_vision_available():
					import PIL

class  a     (		UpperCAmelCase ):
						_lowercase			  =	["pixel_values"]


						def __init__(				self  ,    A_ = True  ,    A_ = None  ,    A_ = PILImageResampling.BILINEAR  ,    A_ = True  ,    A_ = False  ,    A_ = True  ,    A_ = True  ,    A_ = 1 / 255  ,    A_ = True  ,    A_ = None  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										super().__init__(**A_		)

										_UpperCAmelCase				:						str       						=			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(A_  ,    (tuple, list)		):
														# The previous feature extractor size parameter was in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Dict       						=			get_size_dict(A_		)

										_UpperCAmelCase				:						int       						=			do_resize
										_UpperCAmelCase				:						Dict       						=			size
										_UpperCAmelCase				:						List[Any]       						=			resample
										_UpperCAmelCase				:						Union[str, Any]       						=			do_thumbnail
										_UpperCAmelCase				:						Optional[int]       						=			do_align_long_axis
										_UpperCAmelCase				:						Optional[Any]       						=			do_pad
										_UpperCAmelCase				:						int       						=			do_rescale
										_UpperCAmelCase				:						List[Any]       						=			rescale_factor
										_UpperCAmelCase				:						Union[str, Any]       						=			do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase				:						Dict       						=			image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						List[str]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														_UpperCAmelCase				:						Optional[int]       						=			np.rotaa(A_  ,    3		)

										if data_format is not None:
														_UpperCAmelCase				:						List[str]       						=			to_channel_dimension_format(A_  ,    A_		)

										return image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = False  ,    A_ = None  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			size["height"], size["width"]
										_UpperCAmelCase						,							_UpperCAmelCase				:						List[Any]       						=			get_image_size(A_		)

										_UpperCAmelCase				:						Optional[Any]       						=			output_width - input_width
										_UpperCAmelCase				:						Any       						=			output_height - input_height

										if random_padding:
														_UpperCAmelCase				:						List[str]       						=			np.random.randint(low=0  ,    high=delta_height + 1		)
														_UpperCAmelCase				:						Optional[int]       						=			np.random.randint(low=0  ,    high=delta_width + 1		)
										else:
														_UpperCAmelCase				:						Union[str, Any]       						=			delta_height // 2
														_UpperCAmelCase				:						str       						=			delta_width // 2

										_UpperCAmelCase				:						Optional[Any]       						=			delta_height - pad_top
										_UpperCAmelCase				:						List[Any]       						=			delta_width - pad_left

										_UpperCAmelCase				:						Optional[Any]       						=			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(A_  ,    A_  ,    data_format=A_		)


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										_UpperCAmelCase				:						Dict       						=			min(A_  ,    A_		)
										_UpperCAmelCase				:						Tuple       						=			min(A_  ,    A_		)

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

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

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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						List[str]       						=			min(size["height"]  ,    size["width"]		)
										_UpperCAmelCase				:						Optional[Any]       						=			get_resize_output_image_size(A_  ,    size=A_  ,    default_to_square=A_		)
										_UpperCAmelCase				:						str       						=			resize(A_  ,    size=A_  ,    resample=A_  ,    data_format=A_  ,    **A_		)
										return resized_image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										return rescale(A_  ,    scale=A_  ,    data_format=A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


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





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = ChannelDimension.FIRST  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase				:						str       						=			size if size is not None else self.size
										if isinstance(A_  ,    (tuple, list)		):
														# Previous feature extractor had size in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Union[str, Any]       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						Dict       						=			resample if resample is not None else self.resample
										_UpperCAmelCase				:						str       						=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										_UpperCAmelCase				:						Optional[Any]       						=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										_UpperCAmelCase				:						List[str]       						=			do_pad if do_pad is not None else self.do_pad
										_UpperCAmelCase				:						Optional[Any]       						=			do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase				:						int       						=			rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase				:						List[str]       						=			do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase				:						List[Any]       						=			image_std if image_std is not None else self.image_std

										_UpperCAmelCase				:						str       						=			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.
										_UpperCAmelCase				:						Union[str, Any]       						=			[to_numpy_array(A_		) for image in images]

										if do_align_long_axis:
														_UpperCAmelCase				:						Tuple       						=			[self.align_long_axis(A_  ,    size=A_		) for image in images]

										if do_resize:
														_UpperCAmelCase				:						Optional[int]       						=			[self.resize(image=A_  ,    size=A_  ,    resample=A_		) for image in images]

										if do_thumbnail:
														_UpperCAmelCase				:						Optional[Any]       						=			[self.thumbnail(image=A_  ,    size=A_		) for image in images]

										if do_pad:
														_UpperCAmelCase				:						List[Any]       						=			[self.pad_image(image=A_  ,    size=A_  ,    random_padding=A_		) for image in images]

										if do_rescale:
														_UpperCAmelCase				:						Tuple       						=			[self.rescale(image=A_  ,    scale=A_		) for image in images]

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

										_UpperCAmelCase				:						str       						=			[to_channel_dimension_format(A_  ,    A_		) for image in images]

										_UpperCAmelCase				:						Dict       						=			{"pixel_values": images}
										return BatchFeature(data=A_  ,    tensor_type=A_		)


