import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device



def    lowerCamelCase_   (  _lowercase    )							->	Optional[Any]:
		__A							:   int	  =				{}
		for name in unet.attn_processors.keys():
				__A							:   Optional[int]	  =				None if name.endswith("attn1.processor"    ) else unet.config.cross_attention_dim
				if name.startswith("mid_block"    ):
						__A							:   Optional[Any]	  =				unet.config.block_out_channels[-1]
				elif name.startswith("up_blocks"    ):
						__A							:   Any	  =				int(name[len("up_blocks."    )]    )
						__A							:   List[str]	  =				list(reversed(unet.config.block_out_channels    )    )[block_id]
				elif name.startswith("down_blocks"    ):
						__A							:   List[str]	  =				int(name[len("down_blocks."    )]    )
						__A							:   int	  =				unet.config.block_out_channels[block_id]
				__A							:   str	  =				(
				    LoRAAttnProcessor2_0 if hasattr(_lowercase ,     "scaled_dot_product_attention"    ) else LoRAAttnProcessor
				)
				__A							:   int	  =				lora_attn_processor_class(
				    hidden_size=_lowercase ,     cross_attention_dim=_lowercase    )
		__A							:   Tuple	  =				AttnProcsLayers(_lowercase    )
		return lora_attn_procs, unet_lora_layers



def    lowerCamelCase_   (  _lowercase    )							->	Optional[int]:
		__A							:   int	  =				{}
		__A							:   int	  =				(
		    LoRAAttnProcessor2_0 if hasattr(_lowercase ,     "scaled_dot_product_attention"    ) else LoRAAttnProcessor
		)
		for name, module in text_encoder.named_modules():
				if name.endswith(_lowercase    ):
						__A							:   Any	  =				lora_attn_processor_class(
						    hidden_size=module.out_proj.out_features ,     cross_attention_dim=_lowercase    )
		return text_lora_attn_procs



def    lowerCamelCase_   (  _lowercase    )							->	Optional[int]:
		__A							:   List[Any]	  =				create_text_encoder_lora_attn_procs(_lowercase    )
		__A							:   Optional[int]	  =				AttnProcsLayers(_lowercase    )
		return text_encoder_lora_layers



def    lowerCamelCase_   (  _lowercase ,     _lowercase=False    )							->	str:
		for _, attn_proc in text_lora_attn_procs.items():
				# set up.weights
				for layer_name, layer_module in attn_proc.named_modules():
						if layer_name.endswith("_lora"    ):
								__A							:   Optional[int]	  =				(
								    torch.randn_like(layer_module.up.weight    )
								    if randn_weight
								    else torch.zeros_like(layer_module.up.weight    )
								)
								__A							:   List[Any]	  =				torch.nn.Parameter(_lowercase    )





class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   Any	  =				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							,			)
			__A							:   List[str]	  =				DDIMScheduler(
			    beta_start=0.0_00_85							,			beta_end=0.0_12							,			beta_schedule="scaled_linear"							,			clip_sample=__UpperCAmelCase							,			set_alpha_to_one=__UpperCAmelCase							,			steps_offset=1							,			)
			torch.manual_seed(0   )
			__A							:   int	  =				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							,			)
			__A							:   Dict	  =				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							,			)
			__A							:   List[str]	  =				CLIPTextModel(__UpperCAmelCase   )
			__A							:   Optional[int]	  =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

			__A  ,       __A							:   List[str]	  =				create_unet_lora_layers(__UpperCAmelCase   )
			__A							:   Union[str, Any]	  =				create_text_encoder_lora_layers(__UpperCAmelCase   )

			__A							:   Dict	  =				{
			    "unet": unet,
			    "scheduler": scheduler,
			    "vae": vae,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "safety_checker": None,
			    "feature_extractor": None,
			}
			__A							:   Union[str, Any]	  =				{
			    "unet_lora_layers": unet_lora_layers,
			    "text_encoder_lora_layers": text_encoder_lora_layers,
			    "unet_lora_attn_procs": unet_lora_attn_procs,
			}
			return pipeline_components, lora_components

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[Any]	  =				1
			__A							:   Any	  =				10
			__A							:   Tuple	  =				4
			__A							:   str	  =				(32, 32)

			__A							:   int	  =				torch.manual_seed(0   )
			__A							:   Optional[Any]	  =				floats_tensor((batch_size, num_channels) + sizes   )
			__A							:   int	  =				torch.randint(1							,			__UpperCAmelCase							,			size=(batch_size, sequence_length)							,			generator=__UpperCAmelCase   )

			__A							:   Any	  =				{
			    "prompt": "A painting of a squirrel eating a burger",
			    "generator": generator,
			    "num_inference_steps": 2,
			    "guidance_scale": 6.0,
			    "output_type": "numpy",
			}

			return noise, input_ids, pipeline_inputs

			# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb

	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				77

			__A							:   List[Any]	  =				torch.randint(2							,			56							,			size=(1, max_seq_length)							,			generator=torch.manual_seed(0   )   )

			__A							:   Optional[Any]	  =				{}
			__A							:   Optional[int]	  =				inputs
			return prepared_inputs

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A  ,       __A							:   Optional[Any]	  =				self.get_dummy_components()
			LoraLoaderMixin.save_lora_weights(
			    save_directory=__UpperCAmelCase							,			unet_lora_layers=lora_components["unet_lora_layers"]							,			text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]							,			)
			self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase							,			"pytorch_lora_weights.bin"   )   )   )

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Tuple	  =				self.get_dummy_components()
			__A							:   Optional[Any]	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Dict	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   Dict	  =				self.get_dummy_inputs()

			__A							:   Any	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				original_images[0, -3:, -3:, -1]

			with tempfile.TemporaryDirectory() as tmpdirname:
					LoraLoaderMixin.save_lora_weights(
					    save_directory=__UpperCAmelCase							,			unet_lora_layers=lora_components["unet_lora_layers"]							,			text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]							,			)
					self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase							,			"pytorch_lora_weights.bin"   )   )   )
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

			__A							:   str	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   int	  =				lora_images[0, -3:, -3:, -1]

			# Outputs shouldn't match.
			self.assertFalse(torch.allclose(torch.from_numpy(__UpperCAmelCase   )							,			torch.from_numpy(__UpperCAmelCase   )   )   )

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Dict	  =				self.get_dummy_components()
			__A							:   int	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Tuple	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   str	  =				self.get_dummy_inputs()

			__A							:   List[str]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				original_images[0, -3:, -3:, -1]

			with tempfile.TemporaryDirectory() as tmpdirname:
					LoraLoaderMixin.save_lora_weights(
					    save_directory=__UpperCAmelCase							,			unet_lora_layers=lora_components["unet_lora_layers"]							,			text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]							,			safe_serialization=__UpperCAmelCase							,			)
					self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase							,			"pytorch_lora_weights.safetensors"   )   )   )
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

			__A							:   Any	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   List[str]	  =				lora_images[0, -3:, -3:, -1]

			# Outputs shouldn't match.
			self.assertFalse(torch.allclose(torch.from_numpy(__UpperCAmelCase   )							,			torch.from_numpy(__UpperCAmelCase   )   )   )

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Any	  =				self.get_dummy_components()
			__A							:   Optional[Any]	  =				lora_components["unet_lora_attn_procs"]
			__A							:   Optional[Any]	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Any	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   List[str]	  =				self.get_dummy_inputs()

			__A							:   str	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   List[Any]	  =				original_images[0, -3:, -3:, -1]

			with tempfile.TemporaryDirectory() as tmpdirname:
					__A							:   Dict	  =				sd_pipe.unet
					unet.set_attn_processor(__UpperCAmelCase   )
					unet.save_attn_procs(__UpperCAmelCase   )
					self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase							,			"pytorch_lora_weights.bin"   )   )   )
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

			__A							:   Optional[Any]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				lora_images[0, -3:, -3:, -1]

			# Outputs shouldn't match.
			self.assertFalse(torch.allclose(torch.from_numpy(__UpperCAmelCase   )							,			torch.from_numpy(__UpperCAmelCase   )   )   )

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   List[str]	  =				self.get_dummy_components()
			__A							:   Dict	  =				StableDiffusionPipeline(**__UpperCAmelCase   )

			__A							:   int	  =				self.get_dummy_tokens()

			# inference without lora
			__A							:   Dict	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_without_lora.shape == (1, 77, 32)

			# create lora_attn_procs with zeroed out up.weights
			__A							:   Any	  =				create_text_encoder_lora_attn_procs(pipe.text_encoder   )
			set_lora_up_weights(__UpperCAmelCase							,			randn_weight=__UpperCAmelCase   )

			# monkey patch
			pipe._modify_text_encoder(__UpperCAmelCase   )

			# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
			del text_attn_procs
			gc.collect()

			# inference with lora
			__A							:   Union[str, Any]	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_with_lora.shape == (1, 77, 32)

			assert torch.allclose(
			    __UpperCAmelCase							,			__UpperCAmelCase   ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

			# create lora_attn_procs with randn up.weights
			__A							:   Optional[Any]	  =				create_text_encoder_lora_attn_procs(pipe.text_encoder   )
			set_lora_up_weights(__UpperCAmelCase							,			randn_weight=__UpperCAmelCase   )

			# monkey patch
			pipe._modify_text_encoder(__UpperCAmelCase   )

			# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
			del text_attn_procs
			gc.collect()

			# inference with lora
			__A							:   Tuple	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_with_lora.shape == (1, 77, 32)

			assert not torch.allclose(
			    __UpperCAmelCase							,			__UpperCAmelCase   ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Any	  =				self.get_dummy_components()
			__A							:   Any	  =				StableDiffusionPipeline(**__UpperCAmelCase   )

			__A							:   str	  =				self.get_dummy_tokens()

			# inference without lora
			__A							:   Tuple	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_without_lora.shape == (1, 77, 32)

			# create lora_attn_procs with randn up.weights
			__A							:   Union[str, Any]	  =				create_text_encoder_lora_attn_procs(pipe.text_encoder   )
			set_lora_up_weights(__UpperCAmelCase							,			randn_weight=__UpperCAmelCase   )

			# monkey patch
			pipe._modify_text_encoder(__UpperCAmelCase   )

			# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
			del text_attn_procs
			gc.collect()

			# inference with lora
			__A							:   List[str]	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_with_lora.shape == (1, 77, 32)

			assert not torch.allclose(
			    __UpperCAmelCase							,			__UpperCAmelCase   ), "lora outputs should be different to without lora outputs"

			# remove monkey patch
			pipe._remove_text_encoder_monkey_patch()

			# inference with removed lora
			__A							:   Optional[int]	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_without_lora_removed.shape == (1, 77, 32)

			assert torch.allclose(
			    __UpperCAmelCase							,			__UpperCAmelCase   ), "remove lora monkey patch should restore the original outputs"

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   List[str]	  =				self.get_dummy_components()
			__A							:   Tuple	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Optional[Any]	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   Tuple	  =				self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdirname:
					LoraLoaderMixin.save_lora_weights(
					    save_directory=__UpperCAmelCase							,			unet_lora_layers=lora_components["unet_lora_layers"]							,			text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]							,			)
					self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase							,			"pytorch_lora_weights.bin"   )   )   )
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

			__A							:   int	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   List[str]	  =				lora_images[0, -3:, -3:, -1]

			__A							:   Dict	  =				sd_pipe(**__UpperCAmelCase							,			cross_attention_kwargs={"scale": 0.5}   ).images
			__A							:   Optional[int]	  =				lora_images_with_scale[0, -3:, -3:, -1]

			# Outputs shouldn't match.
			self.assertFalse(
			    torch.allclose(torch.from_numpy(__UpperCAmelCase   )							,			torch.from_numpy(__UpperCAmelCase   )   )   )

	def 					__UpperCAmelCase(       self   ):
			with tempfile.TemporaryDirectory() as tmpdirname:
					self.create_lora_weight_file(__UpperCAmelCase   )

					__A  ,       __A							:   Optional[Any]	  =				self.get_dummy_components()
					__A							:   int	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
					__A							:   List[Any]	  =				sd_pipe.to(__UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

					# check if vanilla attention processors are used
					for _, module in sd_pipe.unet.named_modules():
							if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
									self.assertIsInstance(module.processor							,			(AttnProcessor, AttnProcessor2_0)   )

            # load LoRA weight file
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

					# check if lora attention processors are used
					for _, module in sd_pipe.unet.named_modules():
							if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
									__A							:   Optional[Any]	  =				(
									    LoRAAttnProcessor2_0 if hasattr(__UpperCAmelCase							,			"scaled_dot_product_attention"   ) else LoRAAttnProcessor
									)
									self.assertIsInstance(module.processor							,			__UpperCAmelCase   )

	@unittest.skipIf(torch_device != "cuda"							,			"This test is supposed to run on GPU"   )
	def 					__UpperCAmelCase(       self   ):
			with tempfile.TemporaryDirectory() as tmpdirname:
					self.create_lora_weight_file(__UpperCAmelCase   )

					__A  ,       __A							:   Optional[int]	  =				self.get_dummy_components()
					__A							:   int	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
					__A							:   Optional[int]	  =				sd_pipe.to(__UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

					# enable XFormers
					sd_pipe.enable_xformers_memory_efficient_attention()

					# check if xFormers attention processors are used
					for _, module in sd_pipe.unet.named_modules():
							if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
									self.assertIsInstance(module.processor							,			__UpperCAmelCase   )

            # load LoRA weight file
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

					# check if lora attention processors are used
					for _, module in sd_pipe.unet.named_modules():
							if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
									self.assertIsInstance(module.processor							,			__UpperCAmelCase   )







	@unittest.skipIf(torch_device != "cuda"							,			"This test is supposed to run on GPU"   )
	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   List[str]	  =				self.get_dummy_components()
			__A							:   Tuple	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Any	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   Optional[int]	  =				self.get_dummy_inputs()

			# enable XFormers
			sd_pipe.enable_xformers_memory_efficient_attention()

			__A							:   Union[str, Any]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				original_images[0, -3:, -3:, -1]

			with tempfile.TemporaryDirectory() as tmpdirname:
					LoraLoaderMixin.save_lora_weights(
					    save_directory=__UpperCAmelCase							,			unet_lora_layers=lora_components["unet_lora_layers"]							,			text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]							,			)
					self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase							,			"pytorch_lora_weights.bin"   )   )   )
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

			__A							:   Optional[Any]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				lora_images[0, -3:, -3:, -1]

			# Outputs shouldn't match.
			self.assertFalse(torch.allclose(torch.from_numpy(__UpperCAmelCase   )							,			torch.from_numpy(__UpperCAmelCase   )   )   )






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()





