import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase      			=     logging.get_logger(__name__)



def    lowerCamelCase_   (  )							->	Dict:
		__A							:   Dict	  =				DetaConfig(
		    num_queries=900 ,     encoder_ffn_dim=2_048 ,     decoder_ffn_dim=2_048 ,     num_feature_levels=5 ,     assign_first_stage=_lowerCamelCase ,     with_box_refine=_lowerCamelCase ,     two_stage=_lowerCamelCase ,     )

		# set labels
		__A							:   List[str]	  =				91
		__A							:   Optional[int]	  =				"""huggingface/label-files"""
		__A							:   Union[str, Any]	  =				"""coco-detection-id2label.json"""
		__A							:   List[Any]	  =				json.load(open(cached_download(hf_hub_url(_lowerCamelCase ,     _lowerCamelCase ,     repo_type="dataset"    )    ) ,     "r"    )    )
		__A							:   Tuple	  =				{int(_lowerCamelCase    ): v for k, v in idalabel.items()}
		__A							:   List[Any]	  =				idalabel
		__A							:   Dict	  =				{v: k for k, v in idalabel.items()}

		return config



def    lowerCamelCase_   (  _lowercase    )							->	Tuple:
		__A							:   Optional[int]	  =				[]

		# stem
		# fmt: off
		rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")    )
		rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")    )
		rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")    )
		rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")    )
		rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")    )
		# stages
		for stage_idx in range(len(config.backbone_config.depths    )    ):
				for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
								    )    )
						# 3 convs
						for i in range(3    ):
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
								        F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
								    )    )
				# transformer encoder
		for i in range(config.encoder_layers    ):
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")    )

		# transformer decoder
		for i in range(config.decoder_layers    ):
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")    )

		# fmt: on

		return rename_keys



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Dict:
		__A							:   Optional[int]	  =				dct.pop(_lowerCamelCase    )
		__A							:   Dict	  =				val



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	str:
		# transformer decoder self-attention layers
		__A							:   int	  =				config.d_model
		for i in range(config.decoder_layers    ):
				# read in weights + bias of input projection layer of self-attention
				__A							:   Tuple	  =				state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
				__A							:   Tuple	  =				state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"    )
				# next, add query, keys and values (in that order) to the state dict
				__A							:   List[str]	  =				in_proj_weight[:hidden_size, :]
				__A							:   Union[str, Any]	  =				in_proj_bias[:hidden_size]
				__A							:   str	  =				in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				__A							:   Dict	  =				in_proj_bias[hidden_size : hidden_size * 2]
				__A							:   Optional[Any]	  =				in_proj_weight[-hidden_size:, :]
				__A							:   Tuple	  =				in_proj_bias[-hidden_size:]



def    lowerCamelCase_   (  )							->	List[str]:
		__A							:   Union[str, Any]	  =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		__A							:   Dict	  =				Image.open(requests.get(_lowerCamelCase ,     stream=_lowerCamelCase    ).raw    )

		return im



@torch.no_grad()
def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Optional[int]:
		__A							:   str	  =				get_deta_config()

		# load original state dict
		if model_name == "deta-resnet-50":
				__A							:   List[str]	  =				"""adet_checkpoint0011.pth"""
		elif model_name == "deta-resnet-50-24-epochs":
				__A							:   List[str]	  =				"""adet_2x_checkpoint0023.pth"""
		else:
				raise ValueError(F"Model name {model_name} not supported"    )
		__A							:   List[str]	  =				hf_hub_download(repo_id="nielsr/deta-checkpoints" ,     filename=_lowerCamelCase    )
		__A							:   Union[str, Any]	  =				torch.load(_lowerCamelCase ,     map_location="cpu"    )["""model"""]

		# rename keys
		__A							:   List[str]	  =				create_rename_keys(_lowerCamelCase    )
		for src, dest in rename_keys:
				rename_key(_lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase    )
		read_in_decoder_q_k_v(_lowerCamelCase ,     _lowerCamelCase    )

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						__A							:   Optional[Any]	  =				state_dict.pop(_lowerCamelCase    )
						__A							:   str	  =				val
				if "input_proj" in key:
						__A							:   List[Any]	  =				state_dict.pop(_lowerCamelCase    )
						__A							:   Tuple	  =				val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						__A							:   Optional[Any]	  =				state_dict.pop(_lowerCamelCase    )
						__A							:   Dict	  =				val

				# finally, create HuggingFace model and load state dict
		__A							:   Union[str, Any]	  =				DetaForObjectDetection(_lowerCamelCase    )
		model.load_state_dict(_lowerCamelCase    )
		model.eval()

		__A							:   Dict	  =				"""cuda""" if torch.cuda.is_available() else """cpu"""
		model.to(_lowerCamelCase    )

		# load image processor
		__A							:   Tuple	  =				DetaImageProcessor(format="coco_detection"    )

		# verify our conversion on image
		__A							:   List[Any]	  =				prepare_img()
		__A							:   int	  =				processor(images=_lowerCamelCase ,     return_tensors="pt"    )
		__A							:   Dict	  =				encoding["""pixel_values"""]
		__A							:   Optional[int]	  =				model(pixel_values.to(_lowerCamelCase    )    )

		# verify logits
		if model_name == "deta-resnet-50":
				__A							:   Any	  =				torch.tensor(
				    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]    )
				__A							:   str	  =				torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]    )
		elif model_name == "deta-resnet-50-24-epochs":
				__A							:   str	  =				torch.tensor(
				    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]    )
				__A							:   List[Any]	  =				torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]    )

		assert torch.allclose(outputs.logits[0, :3, :3] ,     expected_logits.to(_lowerCamelCase    ) ,     atol=1E-4    )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,     expected_boxes.to(_lowerCamelCase    ) ,     atol=1E-4    )
		print("Everything ok!"    )

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."    )
				Path(_lowerCamelCase    ).mkdir(exist_ok=_lowerCamelCase    )
				model.save_pretrained(_lowerCamelCase    )
				processor.save_pretrained(_lowerCamelCase    )

		# Push to hub
		if push_to_hub:
				print("Pushing model and processor to hub..."    )
				model.push_to_hub(F"jozhang97/{model_name}"    )
				processor.push_to_hub(F"jozhang97/{model_name}"    )


if __name__ == "__main__":
					UpperCamelCase      			=     argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    type=str,
					    default='deta-resnet-50',
					    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
					    help='Name of the model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default=None,
					    type=str,
					    help='Path to the folder to output PyTorch model.',
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)
					UpperCamelCase      			=     parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


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()





