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 _a       (	unittest.TestCase ):

	'''simple docstring'''



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

	@property
	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				1
			__A							:   Optional[int]	  =				3
			__A							:   List[Any]	  =				(32, 32)

			__A							:   Union[str, Any]	  =				floats_tensor((batch_size, num_channels) + sizes							,			rng=random.Random(0   )   ).to(lowerCAmelCase__   )
			return image

	@property
	def 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   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 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   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							,			)
			return model

	@property
	def 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   List[str]	  =				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(lowerCAmelCase__   )

	@property
	def 					__UpperCAmelCase(       self   ):


			def extract(*__UpperCAmelCase							,			**__UpperCAmelCase   ):


					class _a       :

						'''simple docstring'''



						def __init__(       self   ):
								__A							:   List[str]	  =				torch.ones([0]   )







						def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
								self.pixel_values.to(lowerCAmelCase__   )
								return self


					return Out()

			return extract

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
			__A							:   str	  =				self.dummy_cond_unet
			__A							:   Optional[Any]	  =				DDIMScheduler(
			    beta_start=0.0_00_85							,			beta_end=0.0_12							,			beta_schedule="scaled_linear"							,			clip_sample=lowerCAmelCase__							,			set_alpha_to_one=lowerCAmelCase__							,			)

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

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

			__A							:   List[Any]	  =				"A painting of a squirrel eating a burger"

			__A							:   Any	  =				torch.Generator(device=lowerCAmelCase__   ).manual_seed(0   )
			__A							:   Tuple	  =				sd_pipe([prompt]							,			generator=lowerCAmelCase__							,			guidance_scale=6.0							,			num_inference_steps=2							,			output_type="np"   )
			__A							:   Tuple	  =				output.images

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

			__A							:   Optional[int]	  =				image[0, -3:, -3:, -1]
			__A							:   Optional[int]	  =				image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__A							:   Union[str, Any]	  =				np.array([0.57_53, 0.61_14, 0.50_01, 0.50_34, 0.54_70, 0.47_29, 0.49_71, 0.48_67, 0.48_67]   )

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

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

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

			__A							:   List[str]	  =				output.images

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

			__A							:   List[Any]	  =				image[0, -3:, -3:, -1]
			__A							:   Union[str, Any]	  =				image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__A							:   int	  =				np.array([0.51_22, 0.57_12, 0.48_25, 0.50_53, 0.56_46, 0.47_69, 0.51_79, 0.48_94, 0.49_94]   )

			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 					__UpperCAmelCase(       self   ):
			__A							:   Optional[int]	  =				StableDiffusionPipeline.from_pretrained(
			    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"							,			safety_checker=lowerCAmelCase__   )
			assert isinstance(lowerCAmelCase__							,			lowerCAmelCase__   )
			assert isinstance(pipe.scheduler							,			lowerCAmelCase__   )
			assert pipe.safety_checker is None

			__A							:   List[str]	  =				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(lowerCAmelCase__   )
					__A							:   Union[str, Any]	  =				StableDiffusionPipeline.from_pretrained(lowerCAmelCase__   )

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			__A							:   str	  =				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 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				self.dummy_cond_unet
			__A							:   str	  =				PNDMScheduler(skip_prk_steps=lowerCAmelCase__   )
			__A							:   Dict	  =				self.dummy_vae
			__A							:   Optional[Any]	  =				self.dummy_text_encoder
			__A							:   List[str]	  =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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

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

			__A							:   Optional[int]	  =				"A painting of a squirrel eating a burger"
			__A							:   Optional[Any]	  =				sd_pipe([prompt]							,			num_inference_steps=2							,			output_type="np"   ).images

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


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

	'''simple docstring'''



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

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[Any]	  =				"cuda"
			__A							:   List[Any]	  =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   )
			__A							:   int	  =				pipe.to(lowerCAmelCase__   )
			pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

			__A							:   Any	  =				"a photo of a cat"
			__A							:   Optional[int]	  =				{
			    "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,
			}

			__A							:   str	  =				3
			__A							:   Union[str, Any]	  =				7

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

			__A							:   Any	  =				output.images
			__A							:   Dict	  =				image[0, -3:, -3:, -1]
			__A							:   Optional[int]	  =				[
			    0.34_67_31_13,
			    0.38_49_27_33,
			    0.37_59_73_52,
			    0.34_08_63_35,
			    0.35_65_07_48,
			    0.35_57_92_05,
			    0.3_38_47_63,
			    0.34_34_02_36,
			    0.3_57_32_71,
			]

			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(lowerCAmelCase__   )
			__A							:   Any	  =				pipe(
			    [prompt]							,			generator=lowerCAmelCase__							,			guidance_scale=lowerCAmelCase__							,			num_inference_steps=50							,			output_type="np"							,			width=512							,			height=512							,			**lowerCAmelCase__							,			)

			__A							:   Optional[int]	  =				output.images
			__A							:   str	  =				image[0, -3:, -3:, -1]
			__A							:   List[Any]	  =				[
			    0.41_88_78_26,
			    0.37_72_87_66,
			    0.30_13_82_72,
			    0.41_41_63_35,
			    0.41_66_49_85,
			    0.36_28_33_92,
			    0.36_19_12_46,
			    0.43_36_44_65,
			    0.43_00_17_32,
			]

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

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

	def 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				"cuda"
			__A							:   List[Any]	  =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   )
			__A							:   int	  =				pipe.to(lowerCAmelCase__   )
			pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

			__A							:   Optional[Any]	  =				"an image of a crowded boulevard, realistic, 4k"
			__A							:   Optional[Any]	  =				{
			    "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,
			}

			__A							:   Optional[int]	  =				9
			__A							:   int	  =				7

			# no sega enabled
			__A							:   int	  =				torch.Generator(lowerCAmelCase__   )
			generator.manual_seed(lowerCAmelCase__   )
			__A							:   List[str]	  =				pipe(
			    [prompt]							,			generator=lowerCAmelCase__							,			guidance_scale=lowerCAmelCase__							,			num_inference_steps=50							,			output_type="np"							,			width=512							,			height=512							,			)

			__A							:   str	  =				output.images
			__A							:   Union[str, Any]	  =				image[0, -3:, -3:, -1]
			__A							:   Any	  =				[
			    0.43_49_79_98,
			    0.91_81_40_65,
			    0.7_54_07_39,
			    0.55_58_02_05,
			    0.8_46_72_65,
			    0.5_38_96_91,
			    0.62_57_45_06,
			    0.58_89_77_63,
			    0.50_92_67_57,
			]

			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(lowerCAmelCase__   )
			__A							:   Union[str, Any]	  =				pipe(
			    [prompt]							,			generator=lowerCAmelCase__							,			guidance_scale=lowerCAmelCase__							,			num_inference_steps=50							,			output_type="np"							,			width=512							,			height=512							,			**lowerCAmelCase__							,			)

			__A							:   List[str]	  =				output.images
			__A							:   Optional[int]	  =				image[0, -3:, -3:, -1]
			__A							:   List[str]	  =				[
			    0.3_08_97_19,
			    0.30_50_01_44,
			    0.29_01_60_42,
			    0.30_63_09_64,
			    0.32_56_87,
			    0.29_41_92_25,
			    0.2_90_80_91,
			    0.28_72_35_98,
			    0.27_69_62_94,
			]

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

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

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				"cuda"
			__A							:   Any	  =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   )
			__A							:   Union[str, Any]	  =				pipe.to(lowerCAmelCase__   )
			pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

			__A							:   Dict	  =				"a castle next to a river"
			__A							:   Union[str, Any]	  =				{
			    "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,
			}

			__A							:   List[str]	  =				48
			__A							:   int	  =				7

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

			__A							:   Tuple	  =				output.images
			__A							:   Tuple	  =				image[0, -3:, -3:, -1]
			__A							:   Union[str, Any]	  =				[
			    0.75_16_35_55,
			    0.76_03_71_45,
			    0.6_17_85,
			    0.9_18_96_73,
			    0.8_62_77_01,
			    0.85_18_96_94,
			    0.8_51_28_13,
			    0.87_01_20_76,
			    0.8_31_28_57,
			]

			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(lowerCAmelCase__   )
			__A							:   int	  =				pipe(
			    [prompt]							,			generator=lowerCAmelCase__							,			guidance_scale=lowerCAmelCase__							,			num_inference_steps=50							,			output_type="np"							,			width=512							,			height=512							,			**lowerCAmelCase__							,			)

			__A							:   List[Any]	  =				output.images
			__A							:   Union[str, Any]	  =				image[0, -3:, -3:, -1]
			__A							:   Optional[Any]	  =				[
			    0.73_55_33_65,
			    0.7_53_72_71,
			    0.74_34_19_05,
			    0.66_48_03_56,
			    0.6_47_29_25,
			    0.63_03_94_16,
			    0.64_81_29_05,
			    0.6_74_97_17,
			    0.6_51_71_02,
			]

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

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







	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				"cuda"
			__A							:   Tuple	  =				StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							,			torch_dtype=torch.floataa   )
			__A							:   str	  =				pipe.to(lowerCAmelCase__   )
			pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

			__A							:   str	  =				"a photo of a cat"
			__A							:   List[Any]	  =				{
			    "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,
			}

			__A							:   List[str]	  =				3
			__A							:   Tuple	  =				7

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

			__A							:   Optional[Any]	  =				output.images
			__A							:   Optional[int]	  =				image[0, -3:, -3:, -1]
			__A							:   List[Any]	  =				[
			    0.34_88_76_95,
			    0.3_87_69_53,
			    0.3_75,
			    0.34_42_38_28,
			    0.3_58_15_43,
			    0.35_71_77_73,
			    0.3_38_37_89,
			    0.34_57_03_12,
			    0.35_93_75,
			]

			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(lowerCAmelCase__   )
			__A							:   Union[str, Any]	  =				pipe(
			    [prompt]							,			generator=lowerCAmelCase__							,			guidance_scale=lowerCAmelCase__							,			num_inference_steps=50							,			output_type="np"							,			width=512							,			height=512							,			**lowerCAmelCase__							,			)

			__A							:   Union[str, Any]	  =				output.images
			__A							:   List[Any]	  =				image[0, -3:, -3:, -1]
			__A							:   List[str]	  =				[
			    0.42_28_51_56,
			    0.36_91_40_62,
			    0.29_07_71_48,
			    0.42_04_10_16,
			    0.41_91_89_45,
			    0.35_49_80_47,
			    0.3_61_81_64,
			    0.4_42_38_28,
			    0.43_11_52_34,
			]

			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 _a       :

	'''simple docstring'''



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

			__A							:   Any	  =				tableau
			__A  ,       __A							:   Dict	  =				tableau.shape

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

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

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

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

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

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

			__A							:   Tuple	  =				[""]

			# Index of current pivot row and column
			__A							:   Tuple	  =				None
			__A							:   List[str]	  =				None

			# Does objective row only contain (non)-negative values?
			__A							:   Optional[int]	  =				False

	@staticmethod
	def 					__UpperCAmelCase(       *__UpperCAmelCase   ):
			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							:   List[Any]	  =				["x", "s", "a"]
			__A							:   List[str]	  =				[]
			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							,			__UpperCAmelCase   ):
			__A							:   Optional[Any]	  =				self.objectives[-1]

			# Find entries of highest magnitude in objective rows
			__A							:   Optional[int]	  =				(objective == "min") - (objective == "max")
			__A							:   Any	  =				np.argmax(sign * tableau[0, : self.n_vars]   )

			# Choice is only valid if below 0 for maximise, and above for minimise
			if sign * self.tableau[0, col_idx] <= 0:
					__A							:   Any	  =				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							:   Optional[int]	  =				slice(self.n_stages							,			self.n_rows   )

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

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

			# Array filled with nans
			__A							:   Union[str, 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
			__A							:   int	  =				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							:   Optional[Any]	  =				np.nanargmin(__UpperCAmelCase   ) + self.n_stages
			return row_idx, col_idx

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				tableau[row_idx].copy()

			__A							:   Tuple	  =				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							:   str	  =				piv_row
			return tableau

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			self.objectives.pop()

			if not self.objectives:
					return tableau

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

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

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

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

	def 					__UpperCAmelCase(       self   ):
			# 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							:   Optional[Any]	  =				self.generate_col_titles(
							    self.n_vars							,			self.n_slack							,			self.n_art_vars   )

							# Find the values of each variable at optimal solution
							return self.interpret_tableau(self.tableau							,			self.col_titles   )

					__A  ,       __A							:   Dict	  =				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							:   int	  =				self.change_stage(self.tableau   )
					else:
							__A							:   Dict	  =				self.pivot(self.tableau							,			__UpperCAmelCase							,			__UpperCAmelCase   )
			return {}







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				{"P": abs(tableau[0, -1]   )}

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

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

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





