code: 
  
  
  from typing import Any
  
  import numpy as np
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case					):
           # Check if RHS is negative
           if np.any(tableau[:, -1]    ,      where=tableau[:, -1] < 0					):
                raise ValueError('''RHS must be > 0'''					)
  
           snake_case					=       tableau
           snake_case				,						snake_case					=       tableau.shape
  
           # Number of decision variables x1, x2, x3...
           snake_case					=       n_vars
  
           # Number of artificial variables to be minimised
           snake_case					=       len(np.where(tableau[self.n_vars : -1] == -1					)[0]					)
  
           # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
           snake_case					=       (self.n_art_vars > 0) + 1
  
           # Number of slack variables added to make inequalities into equalities
           snake_case					=       self.n_rows - self.n_stages
  
           # Objectives for each stage
           snake_case					=       ['''max''']
  
           # In two stage simplex, first minimise then maximise
           if self.n_art_vars:
                self.objectives.append('''min'''					)
  
           snake_case					=       ['''''']
  
           # Index of current pivot row and column
           snake_case					=       None
           snake_case					=       None
  
           # Does objective row only contain (non)-negative values?
           snake_case					=       False
      @staticmethod
      def 	a_    ( *__snake_case					):
           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
           snake_case					=       ['''x''', '''s''', '''a''']
           snake_case					=       []
           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					):
           snake_case					=       self.objectives[-1]
  
           # Find entries of highest magnitude in objective rows
           snake_case					=       (objective == '''min''') - (objective == '''max''')
           snake_case					=       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:
                snake_case					=       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
           snake_case					=       slice(self.n_stages    ,      self.n_rows					)
  
           # RHS
           snake_case					=       tableau[s, -1]
  
           # Elements of pivot column within slice
           snake_case					=       tableau[s, col_idx]
  
           # Array filled with nans
           snake_case					=       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
           snake_case					=       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
           snake_case					=       np.nanargmin(__snake_case					) + self.n_stages
           return row_idx, col_idx
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       tableau[row_idx].copy()
  
           snake_case					=       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
           snake_case					=       piv_row
           return tableau
      def 	a_    ( self    ,      __snake_case					):
           self.objectives.pop()
  
           if not self.objectives:
                return tableau
  
           # Slice containing ids for artificial columns
           snake_case					=       slice(-self.n_art_vars - 1    ,      -1					)
  
           # Delete the artificial variable columns
           snake_case					=       np.delete(__snake_case    ,      __snake_case    ,      axis=1					)
  
           # Delete the objective row of the first stage
           snake_case					=       np.delete(__snake_case    ,      0    ,      axis=0					)
  
           snake_case					=       1
           self.n_rows -= 1
           snake_case					=       0
           snake_case					=       False
           return tableau
      def 	a_    ( self					):
           # Stop simplex algorithm from cycling.
           for _ in range(1_0_0					):
                # Completion of each stage removes an objective. If both stages
                # are complete, then no objectives are left
                if not self.objectives:
                     snake_case					=       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					)
  
                snake_case				,						snake_case					=       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
                     snake_case					=       self.change_stage(self.tableau					)
                else:
                     snake_case					=       self.pivot(self.tableau    ,      __snake_case    ,      __snake_case					)
           return {}
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           snake_case					=       {'''P''': abs(tableau[0, -1]					)}
  
           for i in range(self.n_vars					):
                # Gives ids of nonzero entries in the ith column
                snake_case					=       np.nonzero(tableau[:, i]					)
                snake_case					=       len(nonzero[0]					)
  
                # First entry in the nonzero ids
                snake_case					=       nonzero[0][0]
                snake_case					=       tableau[nonzero_rowidx, i]
  
                # If there is only one nonzero value in column, which is one
                if n_nonzero == nonzero_val == 1:
                     snake_case					=       tableau[nonzero_rowidx, -1]
                     snake_case					=       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()
  
  
  
  
  
style_context: 
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMParallelScheduler,
      DDPMParallelScheduler,
      StableDiffusionParadigmsPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      require_torch_gpu,
  )
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  class 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	StableDiffusionParadigmsPipeline
      __magic_name__    						=	TEXT_TO_IMAGE_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_BATCH_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
      def 	a_    ( self					):
           torch.manual_seed(0					)
           snake_case					=       UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)    ,      layers_per_block=2    ,      sample_size=3_2    ,      in_channels=4    ,      out_channels=4    ,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,      cross_attention_dim=3_2    ,      attention_head_dim=(2, 4)    ,      use_linear_projection=__snake_case    ,      )
           snake_case					=       DDIMParallelScheduler(
               beta_start=0.0_0085    ,      beta_end=0.012    ,      beta_schedule='''scaled_linear'''    ,      clip_sample=__snake_case    ,      set_alpha_to_one=__snake_case    ,      )
           torch.manual_seed(0					)
           snake_case					=       AutoencoderKL(
               block_out_channels=[3_2, 6_4]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=4    ,      sample_size=1_2_8    ,      )
           torch.manual_seed(0					)
           snake_case					=       CLIPTextConfig(
               bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=3_2    ,      intermediate_size=3_7    ,      layer_norm_eps=1E-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1_0_0_0    ,      hidden_act='''gelu'''    ,      projection_dim=5_1_2    ,      )
           snake_case					=       CLIPTextModel(__snake_case					)
           snake_case					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
  
           snake_case					=       {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=0					):
           if str(__snake_case					).startswith('''mps'''					):
                snake_case					=       torch.manual_seed(__snake_case					)
           else:
                snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
           snake_case					=       {
               '''prompt''': '''a photograph of an astronaut riding a horse''',
               '''generator''': generator,
               '''num_inference_steps''': 1_0,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
               '''parallel''': 3,
               '''debug''': True,
           }
           return inputs
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       sd_pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           torch.manual_seed(0					)
           snake_case					=       DDPMParallelScheduler()
           torch.manual_seed(0					)
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       sd_pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      def 	a_    ( self					):
           super().test_inference_batch_consistent(batch_sizes=[1, 2]					)
      def 	a_    ( self					):
           super().test_inference_batch_single_identical(batch_size=2    ,      expected_max_diff=3E-3					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       '''french fries'''
           snake_case					=       sd_pipe(**__snake_case    ,      negative_prompt=__snake_case					)
           snake_case					=       output.images
           snake_case					=       image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 						A__      (					unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
      def 	a_    ( self    ,      __snake_case=0					):
           snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
           snake_case					=       {
               '''prompt''': '''a photograph of an astronaut riding a horse''',
               '''generator''': generator,
               '''num_inference_steps''': 1_0,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
               '''parallel''': 3,
               '''debug''': True,
           }
           return inputs
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       '''stabilityai/stable-diffusion-2-base'''
           snake_case					=       DDIMParallelScheduler.from_pretrained(__snake_case    ,      subfolder='''scheduler'''					)
           snake_case					=       StableDiffusionParadigmsPipeline.from_pretrained(__snake_case    ,      scheduler=__snake_case    ,      safety_checker=__snake_case					)
           pipe.to(__snake_case					)
           pipe.set_progress_bar_config(disable=__snake_case					)
           pipe.enable_attention_slicing()
  
           snake_case					=       self.get_inputs()
           snake_case					=       pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1].flatten()
  
           assert image.shape == (1, 5_1_2, 5_1_2, 3)
  
           snake_case					=       np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]					)
  
           assert np.abs(expected_slice - image_slice					).max() < 1E-2
  
  
  
  
  