



"""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  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






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

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

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






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None  ,    lowercase_					:bool = False   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.tokenizer(
												    lowercase_  ,    padding='max_length'  ,    max_length=self.tokenizer.model_max_length  ,    truncation=lowercase_  ,    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(
																		    lowercase_  ,    padding='max_length'  ,    max_length=self.tokenizer.model_max_length  ,    truncation=lowercase_  ,    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					:List[Any]  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor   )							->     Dict:
												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					:Union[str, Any]  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor   )							->     Tuple:
												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					:Optional[Any]  ,    lowercase_					:torch.Tensor   )							->     Any:
												# as self.alphas_cumprod is always in cpu
												UpperCAmelCase			 =					int(lowercase_   )

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

												return alpha_prod, 1 - alpha_prod






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    )							->     List[Any]:
												UpperCAmelCase			 =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

												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					:List[str]  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    )							->     Optional[int]:
												UpperCAmelCase			 =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

												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					:Optional[int]  ,    lowercase_					:torch.Tensor   )							->     str:
												UpperCAmelCase			 =					1 / self.vae.config.scaling_factor * latents
												UpperCAmelCase			 =					self.vae.decode(lowercase_   ).sample
												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												return image






						@torch.no_grad()
						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Image.Image  ,    lowercase_					:torch.Tensor  ,    lowercase_					:torch.Tensor  ,    lowercase_					:float  ,    lowercase_					:Optional[torch.Generator] = None  ,    )							->     Optional[int]:
												UpperCAmelCase			 =					guidance_scale > 1.0

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

												UpperCAmelCase			 =					self.vae.config.scaling_factor * latent

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

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

																								UpperCAmelCase			 =					model_input

												return coupled_latents






						@torch.no_grad()
						def __call__(   self					:List[Any]  ,    lowercase_					:str  ,    lowercase_					:str  ,    lowercase_					:Image.Image  ,    lowercase_					:float = 3.0  ,    lowercase_					:int = 50  ,    lowercase_					:float = 0.8  ,    lowercase_					:Optional[str] = None  ,    lowercase_					:Optional[torch.Generator] = None  ,    lowercase_					:Optional[str] = "pil"  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					guidance_scale > 1.0

												UpperCAmelCase			 =					self.image_processor.preprocess(lowercase_   )

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

												self.scheduler.set_timesteps(lowercase_  ,    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(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			 =					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(lowercase_  ,    lowercase_  ,    encoder_hidden_states=lowercase_   ).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=lowercase_  ,    model_input=lowercase_  ,    model_output=lowercase_  ,    timestep=lowercase_  ,    )

																								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'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

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

												return image











"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]						)    ->       int:
 return EnvironmentCommand()







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 @staticmethod
 def lowercase_      (lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  parser.add_parser("env"					,     help="Print relevant system environment info."   )
  download_parser.set_defaults(func=lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Any					      =  {
      "`datasets` version": version,
      "Platform": platform.platform(),
      "Python version": platform.python_version(),
      "Huggingface_hub version": huggingface_hub.__version__,
      "PyArrow version": pyarrow.__version__,
      "Pandas version": pandas.__version__,
  }

  print("\nCopy-and-paste the text below in your GitHub issue.\n"   )
  print(self.format_dict(lowerCAmelCase__   )   )

  return info






 @staticmethod
 def lowercase_      (lowerCAmelCase__   ):

  '''simple docstring'''







  return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"



