


'''simple docstring'''

from typing import Any

import numpy as np





class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     None:
          # Check if RHS is negative
          if np.any(tableau[:, -1]  ,				where=tableau[:, -1] < 0		):
               raise ValueError("RHS must be > 0"		)

          _a            =		tableau
          _a      ,      _a            =		tableau.shape

          # Number of decision variables x1, x2, x3...
          _a            =		n_vars

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

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

          # Number of slack variables added to make inequalities into equalities
          _a            =		self.n_rows - self.n_stages

          # Objectives for each stage
          _a            =		["max"]

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

          _a            =		[""]

          # Index of current pivot row and column
          _a            =		None
          _a            =		None

          # Does objective row only contain (non)-negative values?
          _a            =		False






     @staticmethod
     def 			a_       (      *__UpperCamelCase		)							->     list[str]:
          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
          _a            =		["x", "s", "a"]
          _a            =		[]
          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  ,				__UpperCamelCase		)							->     tuple[Any, Any]:
          _a            =		self.objectives[-1]

          # Find entries of highest magnitude in objective rows
          _a            =		(objective == "min") - (objective == "max")
          _a            =		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            =		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            =		slice(self.n_stages  ,				self.n_rows		)

          # RHS
          _a            =		tableau[s, -1]

          # Elements of pivot column within slice
          _a            =		tableau[s, col_idx]

          # Array filled with nans
          _a            =		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            =		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
          _a            =		np.nanargmin(__UpperCamelCase		) + self.n_stages
          return row_idx, col_idx






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     np.ndarray:
          _a            =		tableau[row_idx].copy()

          _a            =		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            =		piv_row
          return tableau






     def 			a_       (      self  ,				__UpperCamelCase		)							->     np.ndarray:
          self.objectives.pop()

          if not self.objectives:
               return tableau

          # Slice containing ids for artificial columns
          _a            =		slice(-self.n_art_vars - 1  ,				-1		)

          # Delete the artificial variable columns
          _a            =		np.delete(__UpperCamelCase  ,				__UpperCamelCase  ,				axis=1		)

          # Delete the objective row of the first stage
          _a            =		np.delete(__UpperCamelCase  ,				0  ,				axis=0		)

          _a            =		1
          self.n_rows -= 1
          _a            =		0
          _a            =		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:
                    _a            =		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            =		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            =		self.change_stage(self.tableau		)
               else:
                    _a            =		self.pivot(self.tableau  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          return {}






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     dict[str, float]:
          _a            =		{"P": abs(tableau[0, -1]		)}

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

               # First entry in the nonzero ids
               _a            =		nonzero[0][0]
               _a            =		tableau[nonzero_rowidx, i]

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





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					RePaintPipeline
     UpperCAmelCase			       =					IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     UpperCAmelCase			       =					IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase			       =					False






     def 			a_       (      self		)							->     int:
          torch.manual_seed(0		)
          torch.manual_seed(0		)
          _a            =		UNetaDModel(
              block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,				up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,				)
          _a            =		RePaintScheduler()
          _a            =		{"unet": unet, "scheduler": scheduler}
          return components






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Tuple:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		np.random.RandomState(__UpperCamelCase		).standard_normal((1, 3, 32, 32)		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		(image > 0).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		{
              "image": image,
              "mask_image": mask,
              "generator": generator,
              "num_inference_steps": 5,
              "eta": 0.0,
              "jump_length": 2,
              "jump_n_sample": 2,
              "output_type": "numpy",
          }
          return inputs






     def 			a_       (      self		)							->     Optional[Any]:
          _a            =		"cpu"  # ensure determinism for the device-dependent torch.Generator
          _a            =		self.get_dummy_components()
          _a            =		RePaintPipeline(**__UpperCamelCase		)
          _a            =		sd_pipe.to(__UpperCamelCase		)
          sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		self.get_dummy_inputs(__UpperCamelCase		)
          _a            =		sd_pipe(**__UpperCamelCase		).images
          _a            =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _a            =		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3






     @skip_mps
     def 			a_       (      self		)							->     Union[str, Any]:
          return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"		)
     def 			a_       (      self		)							->     int:
          return super().test_inference_batch_single_identical()






     @skip_mps
     def 			a_       (      self		)							->     str:
          return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 			a_       (      self		)							->     int:
          return super().test_save_load_optional_components()






     @skip_mps
     def 			a_       (      self		)							->     Optional[int]:
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Union[str, Any]:
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
          _a            =		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"		)

          _a            =		"google/ddpm-ema-celebahq-256"
          _a            =		UNetaDModel.from_pretrained(__UpperCamelCase		)
          _a            =		RePaintScheduler.from_pretrained(__UpperCamelCase		)

          _a            =		RePaintPipeline(unet=__UpperCamelCase  ,				scheduler=__UpperCamelCase		).to(__UpperCamelCase		)
          repaint.set_progress_bar_config(disable=__UpperCamelCase		)
          repaint.enable_attention_slicing()

          _a            =		torch.manual_seed(0		)
          _a            =		repaint(
              __UpperCamelCase  ,				__UpperCamelCase  ,				num_inference_steps=250  ,				eta=0.0  ,				jump_length=10  ,				jump_n_sample=10  ,				generator=__UpperCamelCase  ,				output_type="np"  ,				)
          _a            =		output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image		).mean() < 1e-2

