'''simple docstring'''




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class 			lowerCamelCase_			(snake_case__							,				snake_case__							,				snake_case__							,				unittest.TestCase					):
   '''simple docstring'''

   __UpperCamelCase:						Tuple      			=		StableDiffusionControlNetInpaintPipeline
   __UpperCamelCase:						List[str]      			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __UpperCamelCase:						List[str]      			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCamelCase:						Optional[Any]      			=		frozenset({"control_image"}					)  # skip `image` and `mask` for now, only test for control_image
   __UpperCamelCase:						Union[str, Any]      			=		TEXT_TO_IMAGE_IMAGE_PARAMS




   def   _A			( self		:       Union[str, Any] ):
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[int]							   =					UNetaDConditionModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=9		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						List[Any]							   =					ControlNetModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[int]							   =					DDIMScheduler(
              beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=A		,      set_alpha_to_one=A		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[Any]							   =					AutoencoderKL(
              block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=4		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Tuple							   =					CLIPTextConfig(
              bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
          _UpperCAmelCase  :						Any							   =					CLIPTextModel(A )
          _UpperCAmelCase  :						Any							   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

          _UpperCAmelCase  :						Any							   =					{
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components




   def   _A			( self		:       List[Any]		,      A		:       List[str]		,      A		:       Optional[Any]=0 ):
          if str(A ).startswith("mps" ):
                 _UpperCAmelCase  :						Union[str, Any]							   =					torch.manual_seed(A )
          else:
                 _UpperCAmelCase  :						int							   =					torch.Generator(device=A ).manual_seed(A )

          _UpperCAmelCase  :						Any							   =					2
          _UpperCAmelCase  :						Any							   =					randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=A		,      device=torch.device(A )		,      )
          _UpperCAmelCase  :						Union[str, Any]							   =					floats_tensor((1, 3, 32, 32)		,      rng=random.Random(A ) ).to(A )
          _UpperCAmelCase  :						Optional[Any]							   =					init_image.cpu().permute(0		,      2		,      3		,      1 )[0]

          _UpperCAmelCase  :						Optional[int]							   =					Image.fromarray(np.uinta(A ) ).convert("RGB" ).resize((64, 64) )
          _UpperCAmelCase  :						Optional[Any]							   =					Image.fromarray(np.uinta(init_image + 4 ) ).convert("RGB" ).resize((64, 64) )

          _UpperCAmelCase  :						Optional[Any]							   =					{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
              "image": image,
              "mask_image": mask_image,
              "control_image": control_image,
          }

          return inputs




   def   _A			( self		:       str ):
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )




   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()		,      reason="XFormers attention is only available with CUDA and `xformers` installed"		,      )
   def   _A			( self		:       Any ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )





   def   _A			( self		:       List[Any] ):
          self._test_inference_batch_single_identical(expected_max_diff=2E-3 )

class 			lowerCamelCase_			(snake_case__					):
   '''simple docstring'''

   __UpperCamelCase:						int      			=		StableDiffusionControlNetInpaintPipeline
   __UpperCamelCase:						List[str]      			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __UpperCamelCase:						int      			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCamelCase:						Optional[int]      			=		frozenset([]					)




   def   _A			( self		:       int ):
          torch.manual_seed(0 )
          _UpperCAmelCase  :						str							   =					UNetaDConditionModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[int]							   =					ControlNetModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[Any]							   =					DDIMScheduler(
              beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=A		,      set_alpha_to_one=A		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[Any]							   =					AutoencoderKL(
              block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=4		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Any							   =					CLIPTextConfig(
              bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
          _UpperCAmelCase  :						Dict							   =					CLIPTextModel(A )
          _UpperCAmelCase  :						Dict							   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

          _UpperCAmelCase  :						Union[str, Any]							   =					{
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components

class 			lowerCamelCase_			(snake_case__							,				snake_case__							,				unittest.TestCase					):
   '''simple docstring'''

   __UpperCamelCase:						Optional[Any]      			=		StableDiffusionControlNetInpaintPipeline
   __UpperCamelCase:						Dict      			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __UpperCamelCase:						int      			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




   def   _A			( self		:       Any ):
          torch.manual_seed(0 )
          _UpperCAmelCase  :						List[str]							   =					UNetaDConditionModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=9		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
          torch.manual_seed(0 )

          def init_weights(A		:       Union[str, Any] ):
                 if isinstance(A		,      torch.nn.Convad ):
                        torch.nn.init.normal(m.weight )
                        m.bias.data.fill_(1.0 )

          _UpperCAmelCase  :						Optional[Any]							   =					ControlNetModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
          controlneta.controlnet_down_blocks.apply(A )

          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[int]							   =					ControlNetModel(
              block_out_channels=(32, 64)		,      layers_per_block=2		,      in_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      cross_attention_dim=32		,      conditioning_embedding_out_channels=(16, 32)		,      )
          controlneta.controlnet_down_blocks.apply(A )

          torch.manual_seed(0 )
          _UpperCAmelCase  :						Tuple							   =					DDIMScheduler(
              beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=A		,      set_alpha_to_one=A		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Union[str, Any]							   =					AutoencoderKL(
              block_out_channels=[32, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,      latent_channels=4		,      )
          torch.manual_seed(0 )
          _UpperCAmelCase  :						Optional[Any]							   =					CLIPTextConfig(
              bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
          _UpperCAmelCase  :						str							   =					CLIPTextModel(A )
          _UpperCAmelCase  :						Optional[int]							   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

          _UpperCAmelCase  :						List[Any]							   =					MultiControlNetModel([controlneta, controlneta] )

          _UpperCAmelCase  :						Optional[int]							   =					{
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components




   def   _A			( self		:       Dict		,      A		:       int		,      A		:       Tuple=0 ):
          if str(A ).startswith("mps" ):
                 _UpperCAmelCase  :						Tuple							   =					torch.manual_seed(A )
          else:
                 _UpperCAmelCase  :						Optional[Any]							   =					torch.Generator(device=A ).manual_seed(A )

          _UpperCAmelCase  :						Tuple							   =					2

          _UpperCAmelCase  :						Tuple							   =					[
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=A		,      device=torch.device(A )		,      ),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,      generator=A		,      device=torch.device(A )		,      ),
          ]
          _UpperCAmelCase  :						List[str]							   =					floats_tensor((1, 3, 32, 32)		,      rng=random.Random(A ) ).to(A )
          _UpperCAmelCase  :						Optional[Any]							   =					init_image.cpu().permute(0		,      2		,      3		,      1 )[0]

          _UpperCAmelCase  :						int							   =					Image.fromarray(np.uinta(A ) ).convert("RGB" ).resize((64, 64) )
          _UpperCAmelCase  :						str							   =					Image.fromarray(np.uinta(init_image + 4 ) ).convert("RGB" ).resize((64, 64) )

          _UpperCAmelCase  :						Optional[Any]							   =					{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
              "image": image,
              "mask_image": mask_image,
              "control_image": control_image,
          }

          return inputs




   def   _A			( self		:       int ):
          _UpperCAmelCase  :						Optional[int]							   =					self.get_dummy_components()
          _UpperCAmelCase  :						Union[str, Any]							   =					self.pipeline_class(**A )
          pipe.to(A )

          _UpperCAmelCase  :						str							   =					10.0
          _UpperCAmelCase  :						str							   =					4

          _UpperCAmelCase  :						Any							   =					self.get_dummy_inputs(A )
          _UpperCAmelCase  :						Optional[Any]							   =					steps
          _UpperCAmelCase  :						Optional[Any]							   =					scale
          _UpperCAmelCase  :						List[str]							   =					pipe(**A )[0]

          _UpperCAmelCase  :						Dict							   =					self.get_dummy_inputs(A )
          _UpperCAmelCase  :						Tuple							   =					steps
          _UpperCAmelCase  :						int							   =					scale
          _UpperCAmelCase  :						Any							   =					pipe(**A		,      control_guidance_start=0.1		,      control_guidance_end=0.2 )[0]

          _UpperCAmelCase  :						Tuple							   =					self.get_dummy_inputs(A )
          _UpperCAmelCase  :						Any							   =					steps
          _UpperCAmelCase  :						Tuple							   =					scale
          _UpperCAmelCase  :						Union[str, Any]							   =					pipe(**A		,      control_guidance_start=[0.1, 0.3]		,      control_guidance_end=[0.2, 0.7] )[0]

          _UpperCAmelCase  :						Dict							   =					self.get_dummy_inputs(A )
          _UpperCAmelCase  :						str							   =					steps
          _UpperCAmelCase  :						Optional[int]							   =					scale
          _UpperCAmelCase  :						Union[str, Any]							   =					pipe(**A		,      control_guidance_start=0.4		,      control_guidance_end=[0.5, 0.8] )[0]

          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
          assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
          assert np.sum(np.abs(output_a - output_a ) ) > 1E-3




   def   _A			( self		:       Union[str, Any] ):
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )




   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()		,      reason="XFormers attention is only available with CUDA and `xformers` installed"		,      )
   def   _A			( self		:       List[str] ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )




   def   _A			( self		:       Optional[Any] ):
          self._test_inference_batch_single_identical(expected_max_diff=2E-3 )





   def   _A			( self		:       int ):
          _UpperCAmelCase  :						Dict							   =					self.get_dummy_components()
          _UpperCAmelCase  :						int							   =					self.pipeline_class(**A )
          pipe.to(A )
          pipe.set_progress_bar_config(disable=A )
          with tempfile.TemporaryDirectory() as tmpdir:
                 try:
                        # save_pretrained is not implemented for Multi-ControlNet
                        pipe.save_pretrained(A )
                 except NotImplementedError:
                        pass

@slow
@require_torch_gpu
class 			lowerCamelCase_			(unittest.TestCase					):
   '''simple docstring'''

   def   _A			( self		:       str ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




   def   _A			( self		:       str ):
          _UpperCAmelCase  :						Dict							   =					ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny" )

          _UpperCAmelCase  :						int							   =					StableDiffusionControlNetInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting"		,      safety_checker=A		,      controlnet=A )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A )

          _UpperCAmelCase  :						int							   =					torch.Generator(device="cpu" ).manual_seed(0 )
          _UpperCAmelCase  :						Union[str, Any]							   =					load_image(
              "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png" ).resize((512, 512) )

          _UpperCAmelCase  :						str							   =					load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_inpaint/input_bench_mask.png" ).resize((512, 512) )

          _UpperCAmelCase  :						Any							   =					"pitch black hole"

          _UpperCAmelCase  :						Optional[int]							   =					load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ).resize((512, 512) )

          _UpperCAmelCase  :						Optional[Any]							   =					pipe(
              A		,      image=A		,      mask_image=A		,      control_image=A		,      generator=A		,      output_type="np"		,      num_inference_steps=3		,      )

          _UpperCAmelCase  :						Union[str, Any]							   =					output.images[0]

          assert image.shape == (512, 512, 3)

          _UpperCAmelCase  :						int							   =					load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy" )

          assert np.abs(expected_image - image ).max() < 9E-2





   def   _A			( self		:       int ):
          _UpperCAmelCase  :						Optional[Any]							   =					ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint" )

          _UpperCAmelCase  :						Any							   =					StableDiffusionControlNetInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"		,      safety_checker=A		,      controlnet=A )
          _UpperCAmelCase  :						Union[str, Any]							   =					DDIMScheduler.from_config(pipe.scheduler.config )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A )

          _UpperCAmelCase  :						Optional[Any]							   =					torch.Generator(device="cpu" ).manual_seed(33 )

          _UpperCAmelCase  :						Any							   =					load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png" )
          _UpperCAmelCase  :						Dict							   =					init_image.resize((512, 512) )

          _UpperCAmelCase  :						Dict							   =					load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png" )
          _UpperCAmelCase  :						List[str]							   =					mask_image.resize((512, 512) )

          _UpperCAmelCase  :						Tuple							   =					"a handsome man with ray-ban sunglasses"

          def make_inpaint_condition(A		:       Optional[Any]		,      A		:       List[str] ):
                 _UpperCAmelCase  :						int							   =					np.array(image.convert("RGB" ) ).astype(np.floataa ) / 255.0
                 _UpperCAmelCase  :						Dict							   =					np.array(image_mask.convert("L" ) ).astype(np.floataa ) / 255.0

                 assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
                 _UpperCAmelCase  :						int							   =					-1.0  # set as masked pixel
                 _UpperCAmelCase  :						Optional[int]							   =					np.expand_dims(A		,      0 ).transpose(0		,      3		,      1		,      2 )
                 _UpperCAmelCase  :						List[str]							   =					torch.from_numpy(A )
                 return image

          _UpperCAmelCase  :						List[Any]							   =					make_inpaint_condition(A		,      A )

          _UpperCAmelCase  :						Optional[int]							   =					pipe(
              A		,      image=A		,      mask_image=A		,      control_image=A		,      guidance_scale=9.0		,      eta=1.0		,      generator=A		,      num_inference_steps=20		,      output_type="np"		,      )
          _UpperCAmelCase  :						Dict							   =					output.images[0]

          assert image.shape == (512, 512, 3)

          _UpperCAmelCase  :						List[str]							   =					load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy" )

          assert np.abs(expected_image - image ).max() < 9E-2


'''simple docstring'''




from typing import Any

import numpy as np

class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       np.ndarray		,      A		:       int ):
             # Check if RHS is negative
             if np.any(tableau[:, -1]		,      where=tableau[:, -1] < 0 ):
                    raise ValueError("RHS must be > 0" )

             _UpperCAmelCase  :						Union[str, Any]							   =					tableau
             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					tableau.shape

             # Number of decision variables x1, x2, x3...
             _UpperCAmelCase  :						Any							   =					n_vars

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

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

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

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

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

             _UpperCAmelCase  :						Any							   =					[""]

             # Index of current pivot row and column
             _UpperCAmelCase  :						Optional[Any]							   =					None
             _UpperCAmelCase  :						Dict							   =					None

             # Does objective row only contain (non)-negative values?
             _UpperCAmelCase  :						List[str]							   =					False




      @staticmethod
      def   _A			( *A		:       int ):
             if len(A ) != 3:
                    raise ValueError("Must provide n_vars, n_slack, and n_art_vars" )

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

             # decision | slack | artificial
             _UpperCAmelCase  :						Optional[Any]							   =					["x", "s", "a"]
             _UpperCAmelCase  :						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		:       List[str]		,      A		:       np.ndarray ):
             _UpperCAmelCase  :						str							   =					self.objectives[-1]

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

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

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

             # Array filled with nans
             _UpperCAmelCase  :						List[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
             _UpperCAmelCase  :						str							   =					np.divide(A		,      A		,      out=A		,      where=divisor > 0 )

             # Arg of minimum quotient excluding the nan values. n_stages is added
             # to compensate for earlier exclusion of objective columns
             _UpperCAmelCase  :						Optional[Any]							   =					np.nanargmin(A ) + self.n_stages
             return row_idx, col_idx




      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tableau[row_idx].copy()

             _UpperCAmelCase  :						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
             _UpperCAmelCase  :						Optional[Any]							   =					piv_row
             return tableau




      def   _A			( self		:       Optional[Any]		,      A		:       np.ndarray ):
             self.objectives.pop()

             if not self.objectives:
                    return tableau

             # Slice containing ids for artificial columns
             _UpperCAmelCase  :						Union[str, Any]							   =					slice(-self.n_art_vars - 1		,      -1 )

             # Delete the artificial variable columns
             _UpperCAmelCase  :						List[Any]							   =					np.delete(A		,      A		,      axis=1 )

             # Delete the objective row of the first stage
             _UpperCAmelCase  :						Union[str, Any]							   =					np.delete(A		,      0		,      axis=0 )

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




      def   _A			( self		:       Optional[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:
                           _UpperCAmelCase  :						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 )

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					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
                           _UpperCAmelCase  :						int							   =					self.change_stage(self.tableau )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					self.pivot(self.tableau		,      A		,      A )
             return {}





      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       list[str] ):
             _UpperCAmelCase  :						str							   =					{"P": abs(tableau[0, -1] )}

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

                    # First entry in the nonzero ids
                    _UpperCAmelCase  :						str							   =					nonzero[0][0]
                    _UpperCAmelCase  :						str							   =					tableau[nonzero_rowidx, i]

                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           _UpperCAmelCase  :						Optional[Any]							   =					tableau[nonzero_rowidx, -1]
                           _UpperCAmelCase  :						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(A		,      0 )
             return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()

