code: 
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
      def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      Any:
             super().__init__()
  
             self.register_modules(vae=snake_case_		,		text_encoder=snake_case_		,		tokenizer=snake_case_		,		unet=snake_case_		,		scheduler=snake_case_							)
  
  
  
  
      def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
             with torch.no_grad():
                    SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.vae.encode(tfms.ToTensor()(snake_case_							).unsqueeze(0							).to(self.device							) * 2 - 1							)
                    SCREAMING_SNAKE_CASE			: int	     =0.1_8215 * latent.latent_dist.sample()
             return latent
  
  
  
  
      def     __a		( self		,		snake_case_							)    ->      List[Any]:
             SCREAMING_SNAKE_CASE			: str	     =(1 / 0.1_8215) * latent
             with torch.no_grad():
                    SCREAMING_SNAKE_CASE			: Tuple	     =self.vae.decode(snake_case_							).sample
             SCREAMING_SNAKE_CASE			: List[Any]	     =(img / 2 + 0.5).clamp(0		,		1							)
             SCREAMING_SNAKE_CASE			: Tuple	     =img.detach().cpu().permute(0		,		2		,		3		,		1							).numpy()
             SCREAMING_SNAKE_CASE			: int	     =(img * 255).round().astype('''uint8'''							)
             return Image.fromarray(img[0]							)
  
  
  
  
      def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
             SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.tokenizer(
                 snake_case_		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=snake_case_		,		return_tensors='''pt'''		,		)
  
             SCREAMING_SNAKE_CASE			: Optional[int]	     =self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
  
             SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.tokenizer(
                 ''''''		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=snake_case_		,		return_tensors='''pt'''		,		)
  
             SCREAMING_SNAKE_CASE			: Optional[int]	     =self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
  
             return torch.cat([uncond_embedding, text_embedding]							)
  
  
  
  
  
  
  
      def __call__( self		,		snake_case_		,		snake_case_		,		snake_case_ = 0.3		,		snake_case_ = 0.6		,		snake_case_ = 0.5		,		snake_case_ = 42		,		snake_case_ = 50		,		snake_case_ = 7.5		,		)    ->      Image.Image:
             SCREAMING_SNAKE_CASE			: Optional[Any]	     =steps - int(kmin * steps							)
             SCREAMING_SNAKE_CASE			: Any	     =steps - int(kmax * steps							)
  
             SCREAMING_SNAKE_CASE			: Dict	     =self.prep_text(snake_case_							)
  
             self.scheduler.set_timesteps(snake_case_							)
  
             SCREAMING_SNAKE_CASE			: Tuple	     =img.size
             SCREAMING_SNAKE_CASE			: List[Any]	     =self.encode(snake_case_							)
  
             torch.manual_seed(snake_case_							)
             SCREAMING_SNAKE_CASE			: Any	     =torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)		,		).to(self.device							)
  
             SCREAMING_SNAKE_CASE			: str	     =self.scheduler.add_noise(
                 snake_case_		,		snake_case_		,		timesteps=self.scheduler.timesteps[tmax]		,		)
  
             SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.cat([latents] * 2							)
  
             SCREAMING_SNAKE_CASE			: Tuple	     =self.scheduler.scale_model_input(snake_case_		,		self.scheduler.timesteps[tmax]							)
  
             with torch.no_grad():
                    SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.unet(
                        snake_case_		,		self.scheduler.timesteps[tmax]		,		encoder_hidden_states=snake_case_		,		).sample
  
             SCREAMING_SNAKE_CASE			: List[Any]	     =pred.chunk(2							)
             SCREAMING_SNAKE_CASE			: Union[str, Any]	     =pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
             SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.step(snake_case_		,		self.scheduler.timesteps[tmax]		,		snake_case_							).prev_sample
  
             for i, t in enumerate(tqdm(self.scheduler.timesteps							)							):
                    if i > tmax:
                           if i < tmin:  # layout generation phase
                                  SCREAMING_SNAKE_CASE			: List[Any]	     =self.scheduler.add_noise(
                                      snake_case_		,		snake_case_		,		timesteps=snake_case_		,		)
  
                                  SCREAMING_SNAKE_CASE			: str	     =(mix_factor * latents) + (
                                      1 - mix_factor
                                  ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                  SCREAMING_SNAKE_CASE			: Any	     =torch.cat([input] * 2							)
  
                           else:  # content generation phase
                                  SCREAMING_SNAKE_CASE			: Any	     =torch.cat([latents] * 2							)
  
                           SCREAMING_SNAKE_CASE			: Optional[int]	     =self.scheduler.scale_model_input(snake_case_		,		snake_case_							)
  
                           with torch.no_grad():
                                  SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.unet(
                                      snake_case_		,		snake_case_		,		encoder_hidden_states=snake_case_		,		).sample
  
                           SCREAMING_SNAKE_CASE			: List[Any]	     =pred.chunk(2							)
                           SCREAMING_SNAKE_CASE			: Optional[Any]	     =pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                           SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.step(snake_case_		,		snake_case_		,		snake_case_							).prev_sample
  
             return self.decode(snake_case_							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_							)    ->      None:
  											# Check if RHS is negative
  											if np.any(tableau[:, -1]		,		where=tableau[:, -1] < 0							):
  																		raise ValueError('''RHS must be > 0'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape
  
  											# Number of decision variables x1, x2, x3...
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars
  
  											# Number of artificial variables to be minimised
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)
  
  											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =(self.n_art_vars > 0) + 1
  
  											# Number of slack variables added to make inequalities into equalities
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.n_rows - self.n_stages
  
  											# Objectives for each stage
  											SCREAMING_SNAKE_CASE			: str	     =['''max''']
  
  											# In two stage simplex, first minimise then maximise
  											if self.n_art_vars:
  																		self.objectives.append('''min'''							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =['''''']
  
  											# Index of current pivot row and column
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											# Does objective row only contain (non)-negative values?
  											SCREAMING_SNAKE_CASE			: Dict	     =False
  
  
  
  
  				@staticmethod
  				def     __a		( *snake_case_							)    ->      list[str]:
  											if len(snake_case_							) != 3:
  																		raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''							)
  
  											if not all(x >= 0 and isinstance(snake_case_		,		snake_case_							) for x in args							):
  																		raise ValueError('''All arguments must be non-negative integers'''							)
  
  											# decision | slack | artificial
  											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
  											SCREAMING_SNAKE_CASE			: int	     =[]
  											for i in range(3							):
  																		for j in range(args[i]							):
  																									titles.append(string_starts[i] + str(j + 1							)							)
  											titles.append('''RHS'''							)
  											return titles
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]
  
  											# Find entries of highest magnitude in objective rows
  											SCREAMING_SNAKE_CASE			: str	     =(objective == '''min''') - (objective == '''max''')
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.argmax(sign * tableau[0, : self.n_vars]							)
  
  											# Choice is only valid if below 0 for maximise, and above for minimise
  											if sign * self.tableau[0, col_idx] <= 0:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =True
  																		return 0, 0
  
  											# Pivot row is chosen as having the lowest quotient when elements of
  											# the pivot column divide the right-hand side
  
  											# Slice excluding the objective rows
  											SCREAMING_SNAKE_CASE			: Tuple	     =slice(self.n_stages		,		self.n_rows							)
  
  											# RHS
  											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]
  
  											# Elements of pivot column within slice
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[s, col_idx]
  
  											# Array filled with nans
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.full(self.n_rows - self.n_stages		,		np.nan							)
  
  											# If element in pivot column is greater than zeron_stages, return
  											# quotient or nan otherwise
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =np.divide(snake_case_		,		snake_case_		,		out=snake_case_		,		where=divisor > 0							)
  
  											# Arg of minimum quotient excluding the nan values. n_stages is added
  											# to compensate for earlier exclusion of objective columns
  											SCREAMING_SNAKE_CASE			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
  											return row_idx, col_idx
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      np.ndarray:
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[row_idx].copy()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =piv_row[col_idx]
  
  											# Entry becomes 1
  											piv_row *= 1 / piv_val
  
  											# Variable in pivot column becomes basic, ie the only non-zero entry
  											for idx, coeff in enumerate(tableau[:, col_idx]							):
  																		tableau[idx] += -coeff * piv_row
  											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
  											return tableau
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
  											self.objectives.pop()
  
  											if not self.objectives:
  																		return tableau
  
  											# Slice containing ids for artificial columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =slice(-self.n_art_vars - 1		,		-1							)
  
  											# Delete the artificial variable columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)
  
  											# Delete the objective row of the first stage
  											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =1
  											self.n_rows -= 1
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  											SCREAMING_SNAKE_CASE			: List[str]	     =False
  											return tableau
  
  
  
  
  				def     __a		( self							)    ->      dict[Any, Any]:
  											# Stop simplex algorithm from cycling.
  											for _ in range(100							):
  																		# Completion of each stage removes an objective. If both stages
  																		# are complete, then no objectives are left
  																		if not self.objectives:
  																									SCREAMING_SNAKE_CASE			: int	     =self.generate_col_titles(
  																									    self.n_vars		,		self.n_slack		,		self.n_art_vars							)
  
  																									# Find the values of each variable at optimal solution
  																									return self.interpret_tableau(self.tableau		,		self.col_titles							)
  
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =self.find_pivot(self.tableau							)
  
  																		# If there are no more negative values in objective row
  																		if self.stop_iter:
  																									# Delete artificial variable columns and rows. Update attributes
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.change_stage(self.tableau							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
  											return {}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      dict[str, float]:
  											SCREAMING_SNAKE_CASE			: Any	     ={'''P''': abs(tableau[0, -1]							)}
  
  											for i in range(self.n_vars							):
  																		# Gives ids of nonzero entries in the ith column
  																		SCREAMING_SNAKE_CASE			: Any	     =np.nonzero(tableau[:, i]							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(nonzero[0]							)
  
  																		# First entry in the nonzero ids
  																		SCREAMING_SNAKE_CASE			: List[str]	     =nonzero[0][0]
  																		SCREAMING_SNAKE_CASE			: Any	     =tableau[nonzero_rowidx, i]
  
  																		# If there is only one nonzero value in column, which is one
  																		if n_nonzero == nonzero_val == 1:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tableau[nonzero_rowidx, -1]
  																									SCREAMING_SNAKE_CASE			: Any	     =rhs_val
  
          # Check for basic variables
  											for title in col_titles:
  																		# Don't add RHS or slack variables to output dict
  																		if title[0] not in "R-s-a":
  																									output_dict.setdefault(snake_case_		,		0							)
  											return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  
  
  