





'''simple docstring'''

import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class 			SCREAMING_SNAKE_CASE__	(					unittest.TestCase						):







					"""simple docstring"""






					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
					@property
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Dict					   =			1
							__UpperCAmelCase     :				Union[str, Any]					   =			3
							__UpperCAmelCase     :				int					   =			(32, 32)

							__UpperCAmelCase     :				Optional[Any]					   =			floats_tensor((batch_size, num_channels) + sizes						,     rng=random.Random(0						)						).to(_A						)
							return image
					@property
					def 						lowerCamelCase_					(					self    :	List[str]						):







							"""simple docstring"""
							torch.manual_seed(0						)
							__UpperCAmelCase     :				Optional[int]					   =			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						,     )
							return model
					@property
					def 						lowerCamelCase_					(					self    :	List[str]						):







							"""simple docstring"""
							torch.manual_seed(0						)
							__UpperCAmelCase     :				Dict					   =			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						,     )
							return model
					@property
					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""
							torch.manual_seed(0						)
							__UpperCAmelCase     :				List[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=1_000						,     )
							return CLIPTextModel(_A						)
					@property
					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""
							def extract(*UpperCAmelCase_    :	List[Any]						,     **UpperCAmelCase_    :	List[str]						):


									class 			SCREAMING_SNAKE_CASE__	:







														"""simple docstring"""






														def __init__(					self    :	Optional[int]						):







																"""simple docstring"""
																__UpperCAmelCase     :				Optional[Any]					   =			torch.ones([0]						)






														def 						lowerCamelCase_					(					self    :	Dict						,     UpperCAmelCase_    :	Any						):







																"""simple docstring"""
																self.pixel_values.to(_A						)
																return self






									return Out()

							return extract
					def 						lowerCamelCase_					(					self    :	str						):







							"""simple docstring"""
							__UpperCAmelCase     :				str					   =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__UpperCAmelCase     :				List[Any]					   =			self.dummy_cond_unet
							__UpperCAmelCase     :				int					   =			DDIMScheduler(
							    beta_start=0.00085						,     beta_end=0.012						,     beta_schedule="scaled_linear"						,     clip_sample=_A						,     set_alpha_to_one=_A						,     )

							__UpperCAmelCase     :				int					   =			self.dummy_vae
							__UpperCAmelCase     :				List[str]					   =			self.dummy_text_encoder
							__UpperCAmelCase     :				Tuple					   =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

							# make sure here that pndm scheduler skips prk
							__UpperCAmelCase     :				str					   =			StableDiffusionPipeline(
							    unet=_A						,     scheduler=_A						,     vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     safety_checker=_A						,     feature_extractor=self.dummy_extractor						,     )
							__UpperCAmelCase     :				Optional[Any]					   =			sd_pipe.to(_A						)
							sd_pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Tuple					   =			"A painting of a squirrel eating a burger"

							__UpperCAmelCase     :				Tuple					   =			torch.Generator(device=_A						).manual_seed(0						)
							__UpperCAmelCase     :				int					   =			sd_pipe([prompt]						,     generator=_A						,     guidance_scale=6.0						,     num_inference_steps=2						,     output_type="np"						)
							__UpperCAmelCase     :				str					   =			output.images

							__UpperCAmelCase     :				Dict					   =			torch.Generator(device=_A						).manual_seed(0						)
							__UpperCAmelCase     :				Optional[int]					   =			sd_pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=6.0						,     num_inference_steps=2						,     output_type="np"						,     return_dict=_A						,     )[0]

							__UpperCAmelCase     :				List[Any]					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				Dict					   =			image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__UpperCAmelCase     :				Tuple					   =			np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
					def 						lowerCamelCase_					(					self    :	Optional[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				List[str]					   =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__UpperCAmelCase     :				Dict					   =			self.dummy_cond_unet
							__UpperCAmelCase     :				str					   =			PNDMScheduler(skip_prk_steps=_A						)
							__UpperCAmelCase     :				Union[str, Any]					   =			self.dummy_vae
							__UpperCAmelCase     :				str					   =			self.dummy_text_encoder
							__UpperCAmelCase     :				str					   =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

							# make sure here that pndm scheduler skips prk
							__UpperCAmelCase     :				str					   =			StableDiffusionPipeline(
							    unet=_A						,     scheduler=_A						,     vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     safety_checker=_A						,     feature_extractor=self.dummy_extractor						,     )
							__UpperCAmelCase     :				int					   =			sd_pipe.to(_A						)
							sd_pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Tuple					   =			"A painting of a squirrel eating a burger"
							__UpperCAmelCase     :				List[Any]					   =			torch.Generator(device=_A						).manual_seed(0						)
							__UpperCAmelCase     :				Union[str, Any]					   =			sd_pipe([prompt]						,     generator=_A						,     guidance_scale=6.0						,     num_inference_steps=2						,     output_type="np"						)

							__UpperCAmelCase     :				List[Any]					   =			output.images

							__UpperCAmelCase     :				int					   =			torch.Generator(device=_A						).manual_seed(0						)
							__UpperCAmelCase     :				Optional[int]					   =			sd_pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=6.0						,     num_inference_steps=2						,     output_type="np"						,     return_dict=_A						,     )[0]

							__UpperCAmelCase     :				Tuple					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				List[Any]					   =			image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__UpperCAmelCase     :				Union[str, Any]					   =			np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Tuple					   =			StableDiffusionPipeline.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"						,     safety_checker=_A						)
							assert isinstance(_A						,     _A						)
							assert isinstance(pipe.scheduler						,     _A						)
							assert pipe.safety_checker is None

							__UpperCAmelCase     :				int					   =			pipe("example prompt"						,     num_inference_steps=2						).images[0]
							assert image is not None

							# check that there's no error when saving a pipeline with one of the models being None
							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_A						)
									__UpperCAmelCase     :				Any					   =			StableDiffusionPipeline.from_pretrained(_A						)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							__UpperCAmelCase     :				List[Any]					   =			pipe("example prompt"						,     num_inference_steps=2						).images[0]
							assert image is not None






					@unittest.skipIf(torch_device != "cuda"						,     "This test requires a GPU"						)
					def 						lowerCamelCase_					(					self    :	Tuple						):







							"""simple docstring"""
							__UpperCAmelCase     :				int					   =			self.dummy_cond_unet
							__UpperCAmelCase     :				List[str]					   =			PNDMScheduler(skip_prk_steps=_A						)
							__UpperCAmelCase     :				Union[str, Any]					   =			self.dummy_vae
							__UpperCAmelCase     :				List[str]					   =			self.dummy_text_encoder
							__UpperCAmelCase     :				List[str]					   =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

							# put models in fp16
							__UpperCAmelCase     :				Any					   =			unet.half()
							__UpperCAmelCase     :				Any					   =			vae.half()
							__UpperCAmelCase     :				Optional[Any]					   =			bert.half()

							# make sure here that pndm scheduler skips prk
							__UpperCAmelCase     :				Any					   =			StableDiffusionPipeline(
							    unet=_A						,     scheduler=_A						,     vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     safety_checker=_A						,     feature_extractor=self.dummy_extractor						,     )
							__UpperCAmelCase     :				List[Any]					   =			sd_pipe.to(_A						)
							sd_pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Union[str, Any]					   =			"A painting of a squirrel eating a burger"
							__UpperCAmelCase     :				List[str]					   =			sd_pipe([prompt]						,     num_inference_steps=2						,     output_type="np"						).images

							assert image.shape == (1, 64, 64, 3)






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







					"""simple docstring"""






					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[int]					   =			"cuda"
							__UpperCAmelCase     :				List[str]					   =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
							__UpperCAmelCase     :				Any					   =			pipe.to(_A						)
							pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Optional[int]					   =			"a photo of a cat"
							__UpperCAmelCase     :				List[str]					   =			{
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 10,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.95,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCAmelCase     :				Optional[int]					   =			3
							__UpperCAmelCase     :				int					   =			7

							# no sega enabled
							__UpperCAmelCase     :				str					   =			torch.Generator(_A						)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Union[str, Any]					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     )

							__UpperCAmelCase     :				List[Any]					   =			output.images
							__UpperCAmelCase     :				Union[str, Any]					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				Union[str, Any]					   =			[
							    0.34673113,
							    0.38492733,
							    0.37597352,
							    0.34086335,
							    0.35650748,
							    0.35579205,
							    0.3384763,
							    0.34340236,
							    0.3573271,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Any					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     **_A						,     )

							__UpperCAmelCase     :				Dict					   =			output.images
							__UpperCAmelCase     :				Union[str, Any]					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				Optional[Any]					   =			[
							    0.41887826,
							    0.37728766,
							    0.30138272,
							    0.41416335,
							    0.41664985,
							    0.36283392,
							    0.36191246,
							    0.43364465,
							    0.43001732,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
					def 						lowerCamelCase_					(					self    :	List[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				str					   =			"cuda"
							__UpperCAmelCase     :				List[str]					   =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
							__UpperCAmelCase     :				List[str]					   =			pipe.to(_A						)
							pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Tuple					   =			"an image of a crowded boulevard, realistic, 4k"
							__UpperCAmelCase     :				Dict					   =			{
							    "editing_prompt": "crowd, crowded, people",
							    "reverse_editing_direction": True,
							    "edit_warmup_steps": 10,
							    "edit_guidance_scale": 8.3,
							    "edit_threshold": 0.9,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCAmelCase     :				str					   =			9
							__UpperCAmelCase     :				Optional[int]					   =			7

							# no sega enabled
							__UpperCAmelCase     :				Optional[int]					   =			torch.Generator(_A						)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Any					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     )

							__UpperCAmelCase     :				str					   =			output.images
							__UpperCAmelCase     :				int					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				int					   =			[
							    0.43497998,
							    0.91814065,
							    0.7540739,
							    0.55580205,
							    0.8467265,
							    0.5389691,
							    0.62574506,
							    0.58897763,
							    0.50926757,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				List[Any]					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     **_A						,     )

							__UpperCAmelCase     :				Optional[int]					   =			output.images
							__UpperCAmelCase     :				int					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				Dict					   =			[
							    0.3089719,
							    0.30500144,
							    0.29016042,
							    0.30630964,
							    0.325687,
							    0.29419225,
							    0.2908091,
							    0.28723598,
							    0.27696294,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
					def 						lowerCamelCase_					(					self    :	Tuple						):







							"""simple docstring"""
							__UpperCAmelCase     :				List[Any]					   =			"cuda"
							__UpperCAmelCase     :				List[str]					   =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
							__UpperCAmelCase     :				List[Any]					   =			pipe.to(_A						)
							pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Tuple					   =			"a castle next to a river"
							__UpperCAmelCase     :				Optional[int]					   =			{
							    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
							    "reverse_editing_direction": False,
							    "edit_warmup_steps": [15, 18],
							    "edit_guidance_scale": 6,
							    "edit_threshold": [0.9, 0.8],
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCAmelCase     :				Optional[int]					   =			48
							__UpperCAmelCase     :				int					   =			7

							# no sega enabled
							__UpperCAmelCase     :				Any					   =			torch.Generator(_A						)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Union[str, Any]					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     )

							__UpperCAmelCase     :				str					   =			output.images
							__UpperCAmelCase     :				Optional[Any]					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				int					   =			[
							    0.75163555,
							    0.76037145,
							    0.61785,
							    0.9189673,
							    0.8627701,
							    0.85189694,
							    0.8512813,
							    0.87012076,
							    0.8312857,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Tuple					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     **_A						,     )

							__UpperCAmelCase     :				Optional[Any]					   =			output.images
							__UpperCAmelCase     :				str					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				str					   =			[
							    0.73553365,
							    0.7537271,
							    0.74341905,
							    0.66480356,
							    0.6472925,
							    0.63039416,
							    0.64812905,
							    0.6749717,
							    0.6517102,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2






					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""
							__UpperCAmelCase     :				List[str]					   =			"cuda"
							__UpperCAmelCase     :				Any					   =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						,     torch_dtype=torch.floataa						)
							__UpperCAmelCase     :				Optional[Any]					   =			pipe.to(_A						)
							pipe.set_progress_bar_config(disable=_A						)

							__UpperCAmelCase     :				Optional[int]					   =			"a photo of a cat"
							__UpperCAmelCase     :				Tuple					   =			{
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 10,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.95,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							__UpperCAmelCase     :				Any					   =			3
							__UpperCAmelCase     :				Optional[Any]					   =			7

							# no sega enabled
							__UpperCAmelCase     :				List[Any]					   =			torch.Generator(_A						)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Tuple					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     )

							__UpperCAmelCase     :				str					   =			output.images
							__UpperCAmelCase     :				Optional[int]					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				int					   =			[
							    0.34887695,
							    0.3876953,
							    0.375,
							    0.34423828,
							    0.3581543,
							    0.35717773,
							    0.3383789,
							    0.34570312,
							    0.359375,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(_A						)
							__UpperCAmelCase     :				Optional[Any]					   =			pipe(
							    [prompt]						,     generator=_A						,     guidance_scale=_A						,     num_inference_steps=50						,     output_type="np"						,     width=512						,     height=512						,     **_A						,     )

							__UpperCAmelCase     :				Optional[Any]					   =			output.images
							__UpperCAmelCase     :				Optional[Any]					   =			image[0, -3:, -3:, -1]
							__UpperCAmelCase     :				List[str]					   =			[
							    0.42285156,
							    0.36914062,
							    0.29077148,
							    0.42041016,
							    0.41918945,
							    0.35498047,
							    0.3618164,
							    0.4423828,
							    0.43115234,
							]

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2






from typing import Any

import numpy as np







class      lowerCamelCase		:





     def __init__(self      :    Optional[Any]				,    _A      :    np.ndarray				,    _A      :    int   )						->						None:
         # 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 		UpperCAmelCase(*_A      :    int   )						->						list[str]:
         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
         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 		UpperCAmelCase(self      :    int				,    _A      :    np.ndarray   )						->						tuple[Any, Any]:
         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(_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
         snake_case      =					np.nanargmin(_A   ) + self.n_stages
         return row_idx, col_idx





     def 		UpperCAmelCase(self      :    int				,    _A      :    np.ndarray				,    _A      :    int				,    _A      :    int   )						->						np.ndarray:
         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 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    np.ndarray   )						->						np.ndarray:
         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(_A				,    _A				,    axis=1   )

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

         snake_case      =					1
         self.n_rows -= 1
         snake_case      =					0
         snake_case      =					False
         return tableau





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						dict[Any, Any]:
         # 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				,    _A				,    _A   )
         return {}





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    np.ndarray				,    _A      :    list[str]   )						->						dict[str, float]:
         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(_A				,    0   )
         return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()

