






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 							__snake_case(       __UpperCamelCase   ):



 def __init__(						self : List[str]			,		UpperCAmelCase__ : AutoencoderKL			,		UpperCAmelCase__ : CLIPTextModel			,		UpperCAmelCase__ : CLIPTokenizer			,		UpperCAmelCase__ : UNetaDConditionModel			,		UpperCAmelCase__ : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,		):

    """simple docstring"""






    super().__init__()

    self.register_modules(vae=UpperCAmelCase__			,		text_encoder=UpperCAmelCase__			,		tokenizer=UpperCAmelCase__			,		unet=UpperCAmelCase__			,		scheduler=UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Optional[int]			,		UpperCAmelCase__ : Tuple						):

    """simple docstring"""






    with torch.no_grad():
       lowercase__    :						Dict       =			self.vae.encode(tfms.ToTensor()(UpperCAmelCase__						).unsqueeze(0						).to(self.device						) * 2 - 1						)
       lowercase__    :						str       =			0.18_215 * latent.latent_dist.sample()
    return latent



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : List[Any]						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			(1 / 0.18_215) * latent
    with torch.no_grad():
       lowercase__    :						Any       =			self.vae.decode(UpperCAmelCase__						).sample
    lowercase__    :						Union[str, Any]       =			(img / 2 + 0.5).clamp(0			,		1						)
    lowercase__    :						Optional[Any]       =			img.detach().cpu().permute(0			,		2			,		3			,		1						).numpy()
    lowercase__    :						Dict       =			(img * 255).round().astype("""uint8"""						)
    return Image.fromarray(img[0]						)



 def     UpperCAmelCase     (						self : Optional[int]			,		UpperCAmelCase__ : List[Any]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			self.tokenizer(
        UpperCAmelCase__			,		padding="""max_length"""			,		max_length=self.tokenizer.model_max_length			,		truncation=UpperCAmelCase__			,		return_tensors="""pt"""			,		)

    lowercase__    :						Dict       =			self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

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

    lowercase__    :						str       =			self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

    return torch.cat([uncond_embedding, text_embedding]						)







 def __call__(						self : str			,		UpperCAmelCase__ : Image.Image			,		UpperCAmelCase__ : str			,		UpperCAmelCase__ : float = 0.3			,		UpperCAmelCase__ : float = 0.6			,		UpperCAmelCase__ : float = 0.5			,		UpperCAmelCase__ : int = 42			,		UpperCAmelCase__ : int = 50			,		UpperCAmelCase__ : float = 7.5			,		):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			steps - int(kmin * steps						)
    lowercase__    :						Optional[int]       =			steps - int(kmax * steps						)

    lowercase__    :						List[str]       =			self.prep_text(UpperCAmelCase__						)

    self.scheduler.set_timesteps(UpperCAmelCase__						)

    lowercase__  ,       lowercase__    :						Any       =			img.size
    lowercase__    :						List[Any]       =			self.encode(UpperCAmelCase__						)

    torch.manual_seed(UpperCAmelCase__						)
    lowercase__    :						Union[str, Any]       =			torch.randn(
        (1, self.unet.config.in_channels, height // 8, width // 8)			,		).to(self.device						)

    lowercase__    :						Tuple       =			self.scheduler.add_noise(
        UpperCAmelCase__			,		UpperCAmelCase__			,		timesteps=self.scheduler.timesteps[tmax]			,		)

    lowercase__    :						Optional[Any]       =			torch.cat([latents] * 2						)

    lowercase__    :						Tuple       =			self.scheduler.scale_model_input(UpperCAmelCase__			,		self.scheduler.timesteps[tmax]						)

    with torch.no_grad():
       lowercase__    :						Optional[Any]       =			self.unet(
           UpperCAmelCase__			,		self.scheduler.timesteps[tmax]			,		encoder_hidden_states=UpperCAmelCase__			,		).sample

    lowercase__  ,       lowercase__    :						Optional[int]       =			pred.chunk(2						)
    lowercase__    :						Tuple       =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

    lowercase__    :						Optional[int]       =			self.scheduler.step(UpperCAmelCase__			,		self.scheduler.timesteps[tmax]			,		UpperCAmelCase__						).prev_sample

    for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
       if i > tmax:
          if i < tmin:  # layout generation phase
             lowercase__    :						Any       =			self.scheduler.add_noise(
                 UpperCAmelCase__			,		UpperCAmelCase__			,		timesteps=UpperCAmelCase__			,		)

             lowercase__    :						int       =			(mix_factor * latents) + (
                 1 - mix_factor
             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
             lowercase__    :						Any       =			torch.cat([input] * 2						)

          else:  # content generation phase
             lowercase__    :						Dict       =			torch.cat([latents] * 2						)

          lowercase__    :						Tuple       =			self.scheduler.scale_model_input(UpperCAmelCase__			,		UpperCAmelCase__						)

          with torch.no_grad():
             lowercase__    :						List[Any]       =			self.unet(
                 UpperCAmelCase__			,		UpperCAmelCase__			,		encoder_hidden_states=UpperCAmelCase__			,		).sample

          lowercase__  ,       lowercase__    :						str       =			pred.chunk(2						)
          lowercase__    :						str       =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

          lowercase__    :						Any       =			self.scheduler.step(UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__						).prev_sample

    return self.decode(UpperCAmelCase__						)










from typing import Any

import numpy as np
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if np.any(tableau[:, -1]			,		where=tableau[:, -1] < 0						):
       raise ValueError("""RHS must be > 0"""						)

    lowercase__    :						List[str]       =			tableau
    lowercase__  ,       lowercase__    :						Optional[Any]       =			tableau.shape

    # Number of decision variables x1, x2, x3...
    lowercase__    :						List[str]       =			n_vars

    # Number of artificial variables to be minimised
    lowercase__    :						str       =			len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    lowercase__    :						Optional[Any]       =			(self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    lowercase__    :						Optional[int]       =			self.n_rows - self.n_stages

    # Objectives for each stage
    lowercase__    :						Any       =			["""max"""]

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append("""min"""						)

    lowercase__    :						Union[str, Any]       =			[""""""]

    # Index of current pivot row and column
    lowercase__    :						List[str]       =			None
    lowercase__    :						List[str]       =			None

    # Does objective row only contain (non)-negative values?
    lowercase__    :						str       =			False



 @staticmethod
 def     UpperCAmelCase     (						*UpperCAmelCase__ : int						):

    """simple docstring"""






    if len(UpperCAmelCase__						) != 3:
       raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

    if not all(x >= 0 and isinstance(UpperCAmelCase__			,		UpperCAmelCase__						) for x in args						):
       raise ValueError("""All arguments must be non-negative integers"""						)

    # decision | slack | artificial
    lowercase__    :						Optional[int]       =			["""x""", """s""", """a"""]
    lowercase__    :						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     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    lowercase__    :						Union[str, Any]       =			(objective == """min""") - (objective == """max""")
    lowercase__    :						Optional[int]       =			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:
       lowercase__    :						Optional[int]       =			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
    lowercase__    :						Any       =			slice(self.n_stages			,		self.n_rows						)

    # RHS
    lowercase__    :						Union[str, Any]       =			tableau[s, -1]

    # Elements of pivot column within slice
    lowercase__    :						Optional[int]       =			tableau[s, col_idx]

    # Array filled with nans
    lowercase__    :						Optional[int]       =			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
    lowercase__    :						Optional[int]       =			np.divide(UpperCAmelCase__			,		UpperCAmelCase__			,		out=UpperCAmelCase__			,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    lowercase__    :						Dict       =			np.nanargmin(UpperCAmelCase__						) + self.n_stages
    return row_idx, col_idx



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						int       =			tableau[row_idx].copy()

    lowercase__    :						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
    lowercase__    :						Any       =			piv_row
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    lowercase__    :						Optional[int]       =			slice(-self.n_art_vars - 1			,		-1						)

    # Delete the artificial variable columns
    lowercase__    :						Dict       =			np.delete(UpperCAmelCase__			,		UpperCAmelCase__			,		axis=1						)

    # Delete the objective row of the first stage
    lowercase__    :						Optional[Any]       =			np.delete(UpperCAmelCase__			,		0			,		axis=0						)

    lowercase__    :						str       =			1
    self.n_rows -= 1
    lowercase__    :						List[str]       =			0
    lowercase__    :						List[Any]       =			False
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    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:
          lowercase__    :						List[Any]       =			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						)

       lowercase__  ,       lowercase__    :						Optional[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
          lowercase__    :						Any       =			self.change_stage(self.tableau						)
       else:
          lowercase__    :						str       =			self.pivot(self.tableau			,		UpperCAmelCase__			,		UpperCAmelCase__						)
    return {}







 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : list[str]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{"""P""": abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       lowercase__    :						int       =			np.nonzero(tableau[:, i]						)
       lowercase__    :						Tuple       =			len(nonzero[0]						)

       # First entry in the nonzero ids
       lowercase__    :						int       =			nonzero[0][0]
       lowercase__    :						Tuple       =			tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          lowercase__    :						Tuple       =			tableau[nonzero_rowidx, -1]
          lowercase__    :						Union[str, 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(UpperCAmelCase__			,		0						)
    return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()


