




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					(				_A       ):







							def __init__(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	AutoencoderKL   ,     __UpperCAmelCase   :	CLIPTextModel   ,     __UpperCAmelCase   :	CLIPTokenizer   ,     __UpperCAmelCase   :	UNetaDConditionModel   ,     __UpperCAmelCase   :	DDIMScheduler   ,     __UpperCAmelCase   :	float = 0.93   ,     __UpperCAmelCase   :	bool = True   ,     )		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= mixing_coeff
										UpperCamelCase_     				= leapfrog_steps

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

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







							def        lowercase__							(    self   :	Optional[int]   ,     __UpperCAmelCase   :	str   ,     __UpperCAmelCase   :	Optional[str] = None   ,     __UpperCAmelCase   :	bool = False				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= self.tokenizer(
										    __A   ,     padding='max_length'   ,     max_length=self.tokenizer.model_max_length   ,     truncation=__A   ,     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(
													    __A   ,     padding='max_length'   ,     max_length=self.tokenizer.model_max_length   ,     truncation=__A   ,     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        lowercase__							(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor				)		->					Tuple:

										"""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        lowercase__							(    self   :	Dict   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor				)		->					Union[str, Any]:

										"""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        lowercase__							(    self   :	List[str]   ,     __UpperCAmelCase   :	torch.Tensor				)		->					Dict:

										"""simple docstring"""



										UpperCamelCase_     				= int(__A				)

										UpperCamelCase_     				= 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]   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     )		->					int:

										"""simple docstring"""



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

										UpperCamelCase_				,   UpperCamelCase_     				= self._get_alpha_and_beta(__A				)
										UpperCamelCase_				,   UpperCamelCase_     				= self._get_alpha_and_beta(__A				)

										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        lowercase__							(    self   :	Tuple   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     )		->					List[str]:

										"""simple docstring"""



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

										UpperCamelCase_				,   UpperCamelCase_     				= self._get_alpha_and_beta(__A				)
										UpperCamelCase_				,   UpperCamelCase_     				= self._get_alpha_and_beta(__A				)

										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        lowercase__							(    self   :	int   ,     __UpperCAmelCase   :	torch.Tensor				)		->					Union[str, Any]:

										"""simple docstring"""



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







							@torch.no_grad()
							def        lowercase__							(    self   :	List[str]   ,     __UpperCAmelCase   :	Image.Image   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	torch.Tensor   ,     __UpperCAmelCase   :	float   ,     __UpperCAmelCase   :	Optional[torch.Generator] = None   ,     )		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= guidance_scale > 1.0

										UpperCamelCase_     				= image.to(device=self.device   ,     dtype=text_embeds.dtype				)
										UpperCamelCase_     				= self.vae.encode(__A				).latent_dist.sample(__A				)

										UpperCamelCase_     				= self.vae.config.scaling_factor * latent

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

										for i, t in tqdm(enumerate(__A				)   ,     total=len(__A				)				):
													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(__A   ,     __A   ,     encoder_hidden_states=__A				).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=__A   ,     model_input=__A   ,     model_output=__A   ,     timestep=__A   ,     )

																UpperCamelCase_     				= model_input

										return coupled_latents







							@torch.no_grad()
							def __call__(    self   :	List[Any]   ,     __UpperCAmelCase   :	str   ,     __UpperCAmelCase   :	str   ,     __UpperCAmelCase   :	Image.Image   ,     __UpperCAmelCase   :	float = 3.0   ,     __UpperCAmelCase   :	int = 50   ,     __UpperCAmelCase   :	float = 0.8   ,     __UpperCAmelCase   :	Optional[str] = None   ,     __UpperCAmelCase   :	Optional[torch.Generator] = None   ,     __UpperCAmelCase   :	Optional[str] = "pil"   ,     )		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= guidance_scale > 1.0

										UpperCamelCase_     				= self.image_processor.preprocess(__A				)

										UpperCamelCase_     				= self._encode_prompt(__A   ,     __A   ,     __A				)
										UpperCamelCase_     				= self._encode_prompt(__A   ,     __A   ,     __A				)

										self.scheduler.set_timesteps(__A   ,     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(__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_     				= 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(__A   ,     __A   ,     encoder_hidden_states=__A				).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=__A   ,     model_input=__A   ,     model_output=__A   ,     timestep=__A   ,     )

																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'   ,     __A   ,     standard_warn=__A				)
													UpperCamelCase_     				= 'np'

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

										return image





'''simple docstring'''







_UpperCamelCase  						=			{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

_UpperCamelCase  						=			{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}
def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	str					, UpperCAmelCase_      :	float						) ->							float:
							if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
														lowerCAmelCase__  =   (
														    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
														    F'''Supported values are: {", ".join(UpperCAmelCase_						)}'''
														)
														raise ValueError(UpperCAmelCase_						)
							return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()





