


'''simple docstring'''


from typing import Any

import numpy as np







class        __magic_name__  :

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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

          __A  :					List[Any]						     =       tableau
          __A     ,__A  :					Optional[Any]						     =       tableau.shape

          # Number of decision variables x1, x2, x3...
          __A  :					Optional[Any]						     =       n_vars

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

          # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
          __A  :					str						     =       (self.n_art_vars > 0) + 1

          # Number of slack variables added to make inequalities into equalities
          __A  :					List[Any]						     =       self.n_rows - self.n_stages

          # Objectives for each stage
          __A  :					Tuple						     =       ["max"]

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

          __A  :					Optional[int]						     =       [""]

          # Index of current pivot row and column
          __A  :					Optional[int]						     =       None
          __A  :					Optional[Any]						     =       None

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


     @staticmethod
     def lowerCAmelCase__     (      *lowerCamelCase		):


          '''simple docstring'''





          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
          __A  :					List[str]						     =       ["x", "s", "a"]
          __A  :					Any						     =       []
          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 lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.objectives[-1]

          # Find entries of highest magnitude in objective rows
          __A  :					List[Any]						     =       (objective == "min") - (objective == "max")
          __A  :					Optional[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:
               __A  :					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
          __A  :					Dict						     =       slice(self.n_stages ,    self.n_rows		)

          # RHS
          __A  :					Optional[Any]						     =       tableau[s, -1]

          # Elements of pivot column within slice
          __A  :					Dict						     =       tableau[s, col_idx]

          # Array filled with nans
          __A  :					str						     =       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
          __A  :					Union[str, 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
          __A  :					str						     =       np.nanargmin(__snake_case		) + self.n_stages
          return row_idx, col_idx


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       tableau[row_idx].copy()

          __A  :					Union[str, 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
          __A  :					Optional[Any]						     =       piv_row
          return tableau


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          self.objectives.pop()

          if not self.objectives:
               return tableau

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

          # Delete the artificial variable columns
          __A  :					int						     =       np.delete(__snake_case ,    __snake_case ,    axis=1		)

          # Delete the objective row of the first stage
          __A  :					List[str]						     =       np.delete(__snake_case ,    0 ,    axis=0		)

          __A  :					Any						     =       1
          self.n_rows -= 1
          __A  :					Optional[int]						     =       0
          __A  :					Union[str, Any]						     =       False
          return tableau


     def lowerCAmelCase__     (      self		):


          '''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:
                    __A  :					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		)

               __A     ,__A  :					Tuple						     =       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
                    __A  :					Any						     =       self.change_stage(self.tableau		)
               else:
                    __A  :					Optional[int]						     =       self.pivot(self.tableau ,    __snake_case ,    __snake_case		)
          return {}


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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

          for i in range(self.n_vars		):
               # Gives ids of nonzero entries in the ith column
               __A  :					List[Any]						     =       np.nonzero(tableau[:, i]		)
               __A  :					str						     =       len(nonzero[0]		)

               # First entry in the nonzero ids
               __A  :					Optional[Any]						     =       nonzero[0][0]
               __A  :					Any						     =       tableau[nonzero_rowidx, i]

               # If there is only one nonzero value in column, which is one
               if n_nonzero == nonzero_val == 1:
                    __A  :					Optional[int]						     =       tableau[nonzero_rowidx, -1]
                    __A  :					Tuple						     =       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()




'''simple docstring'''


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        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          with torch.no_grad():
               __A  :					Optional[Any]						     =       self.vae.encode(tfms.ToTensor()(lowerCamelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
               __A  :					int						     =       0.1_8215 * latent.latent_dist.sample()
          return latent


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       (1 / 0.1_8215) * latent
          with torch.no_grad():
               __A  :					List[Any]						     =       self.vae.decode(lowerCamelCase		).sample
          __A  :					int						     =       (img / 2 + 0.5).clamp(0 ,    1		)
          __A  :					List[str]						     =       img.detach().cpu().permute(0 ,    2 ,    3 ,    1		).numpy()
          __A  :					List[str]						     =       (img * 255).round().astype("uint8"		)
          return Image.fromarray(img[0]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self.tokenizer(
              lowerCamelCase ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					str						     =       self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

          __A  :					List[str]						     =       self.tokenizer(
              "" ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					Union[str, Any]						     =       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

          return torch.cat([uncond_embedding, text_embedding]		)


     def __call__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.3 ,    lowerCamelCase = 0.6 ,    lowerCamelCase = 0.5 ,    lowerCamelCase = 42 ,    lowerCamelCase = 50 ,    lowerCamelCase = 7.5 ,    ):


          '''simple docstring'''





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

          __A  :					Any						     =       self.prep_text(lowerCamelCase		)

          self.scheduler.set_timesteps(lowerCamelCase		)

          __A     ,__A  :					List[str]						     =       img.size
          __A  :					str						     =       self.encode(lowerCamelCase		)

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

          __A  :					Dict						     =       self.scheduler.add_noise(
              lowerCamelCase ,    lowerCamelCase ,    timesteps=self.scheduler.timesteps[tmax] ,    )

          __A  :					str						     =       torch.cat([latents] * 2		)

          __A  :					Optional[int]						     =       self.scheduler.scale_model_input(lowerCamelCase ,    self.scheduler.timesteps[tmax]		)

          with torch.no_grad():
               __A  :					Union[str, Any]						     =       self.unet(
                   lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    encoder_hidden_states=lowerCamelCase ,    ).sample

          __A     ,__A  :					int						     =       pred.chunk(2		)
          __A  :					int						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __A  :					Tuple						     =       self.scheduler.step(lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    lowerCamelCase		).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
               if i > tmax:
                    if i < tmin:  # layout generation phase
                         __A  :					List[Any]						     =       self.scheduler.add_noise(
                             lowerCamelCase ,    lowerCamelCase ,    timesteps=lowerCamelCase ,    )

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

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

                    __A  :					Dict						     =       self.scheduler.scale_model_input(lowerCamelCase ,    lowerCamelCase		)

                    with torch.no_grad():
                         __A  :					Dict						     =       self.unet(
                             lowerCamelCase ,    lowerCamelCase ,    encoder_hidden_states=lowerCamelCase ,    ).sample

                    __A     ,__A  :					str						     =       pred.chunk(2		)
                    __A  :					Optional[Any]						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    __A  :					Any						     =       self.scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return self.decode(lowerCamelCase		)

