

import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel




def        _lowercase  (					a_					:       Union[str, Any]       ,a_					:       Tuple=1					)					->						str:




					'''simple docstring'''







					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('.'					)[n_shave_prefix_segments:]					)
					else:
										return ".".join(path.split('.'					)[:n_shave_prefix_segments]					)




def        _lowercase  (					a_					:       Tuple       ,a_					:       Optional[int]=0					)					->						Dict:




					'''simple docstring'''







					__magic_name__				=     []
					for old_item in old_list:
										__magic_name__				=     old_item
										__magic_name__				=     new_item.replace('block.'       ,'resnets.'					)
										__magic_name__				=     new_item.replace('conv_shorcut'       ,'conv1'					)
										__magic_name__				=     new_item.replace('in_shortcut'       ,'conv_shortcut'					)
										__magic_name__				=     new_item.replace('temb_proj'       ,'time_emb_proj'					)

										__magic_name__				=     shave_segments(a_       ,n_shave_prefix_segments=a_					)

										mapping.append({'old': old_item, 'new': new_item}					)

					return mapping




def        _lowercase  (					a_					:       Union[str, Any]       ,a_					:       List[Any]=0       ,a_					:       Union[str, Any]=False					)					->						Any:




					'''simple docstring'''







					__magic_name__				=     []
					for old_item in old_list:
										__magic_name__				=     old_item

										# In `model.mid`, the layer is called `attn`.
										if not in_mid:
															__magic_name__				=     new_item.replace('attn'       ,'attentions'					)
										__magic_name__				=     new_item.replace('.k.'       ,'.key.'					)
										__magic_name__				=     new_item.replace('.v.'       ,'.value.'					)
										__magic_name__				=     new_item.replace('.q.'       ,'.query.'					)

										__magic_name__				=     new_item.replace('proj_out'       ,'proj_attn'					)
										__magic_name__				=     new_item.replace('norm'       ,'group_norm'					)

										__magic_name__				=     shave_segments(a_       ,n_shave_prefix_segments=a_					)
										mapping.append({'old': old_item, 'new': new_item}					)

					return mapping




def        _lowercase  (					a_					:       Any       ,a_					:       str       ,a_					:       str       ,a_					:       int=None       ,a_					:       Tuple=None       ,a_					:       Dict=None					)					->						str:




					'''simple docstring'''







					assert isinstance(a_       ,a_					), "Paths should be a list of dicts containing 'old' and 'new' keys."

					if attention_paths_to_split is not None:
										if config is None:
															raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'					)

										for path, path_map in attention_paths_to_split.items():
															__magic_name__				=     old_checkpoint[path]
															__magic_name__				=     old_tensor.shape[0] // 3

															__magic_name__				=     (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

															__magic_name__				=     old_tensor.shape[0] // config.get('num_head_channels'       ,1					) // 3

															__magic_name__				=     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
															__magic_name__,						__magic_name__,						__magic_name__				=     old_tensor.split(channels // num_heads       ,dim=1					)

															__magic_name__				=     query.reshape(a_					).squeeze()
															__magic_name__				=     key.reshape(a_					).squeeze()
															__magic_name__				=     value.reshape(a_					).squeeze()

					for path in paths:
										__magic_name__				=     path['new']

										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										__magic_name__				=     new_path.replace('down.'       ,'down_blocks.'					)
										__magic_name__				=     new_path.replace('up.'       ,'up_blocks.'					)

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				__magic_name__				=     new_path.replace(replacement['old']       ,replacement['new']					)

										if "attentions" in new_path:
															__magic_name__				=     old_checkpoint[path['old']].squeeze()
										else:
															__magic_name__				=     old_checkpoint[path['old']]




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       Optional[Any]					)					->						Dict:




					'''simple docstring'''







					__magic_name__				=     {}

					__magic_name__				=     checkpoint['temb.dense.0.weight']
					__magic_name__				=     checkpoint['temb.dense.0.bias']
					__magic_name__				=     checkpoint['temb.dense.1.weight']
					__magic_name__				=     checkpoint['temb.dense.1.bias']

					__magic_name__				=     checkpoint['norm_out.weight']
					__magic_name__				=     checkpoint['norm_out.bias']

					__magic_name__				=     checkpoint['conv_in.weight']
					__magic_name__				=     checkpoint['conv_in.bias']
					__magic_name__				=     checkpoint['conv_out.weight']
					__magic_name__				=     checkpoint['conv_out.bias']

					__magic_name__				=     len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'down' in layer}					)
					__magic_name__				=     {
					    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(a_					)
					}

					__magic_name__				=     len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'up' in layer}					)
					__magic_name__				=     {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(a_					)}

					for i in range(a_					):
										__magic_name__				=     (i - 1) // (config['layers_per_block'] + 1)

										if any('downsample' in layer for layer in down_blocks[i]					):
															__magic_name__				=     checkpoint[
															    F'down.{i}.downsample.op.weight'
															]
															__magic_name__				=     checkpoint[F'down.{i}.downsample.op.bias']
										#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
										#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

										if any('block' in layer for layer in down_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,2					).split('.'					)[:2]					) for layer in down_blocks[i] if 'block' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
															    for layer_id in range(a_					)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block']					):
																									__magic_name__				=     renew_resnet_paths(blocks[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_					)

										if any('attn' in layer for layer in down_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,2					).split('.'					)[:2]					) for layer in down_blocks[i] if 'attn' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
															    for layer_id in range(a_					)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block']					):
																									__magic_name__				=     renew_attention_paths(attns[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_       ,config=a_					)

					__magic_name__				=     [key for key in checkpoint if 'mid.block_1' in key]
					__magic_name__				=     [key for key in checkpoint if 'mid.block_2' in key]
					__magic_name__				=     [key for key in checkpoint if 'mid.attn_1' in key]

					# Mid new 2
					__magic_name__				=     renew_resnet_paths(a_					)
					assign_to_checkpoint(
					    a_       ,a_       ,a_       ,additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,)

					__magic_name__				=     renew_resnet_paths(a_					)
					assign_to_checkpoint(
					    a_       ,a_       ,a_       ,additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,)

					__magic_name__				=     renew_attention_paths(a_       ,in_mid=a_					)
					assign_to_checkpoint(
					    a_       ,a_       ,a_       ,additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,)

					for i in range(a_					):
										__magic_name__				=     num_up_blocks - 1 - i

										if any('upsample' in layer for layer in up_blocks[i]					):
															__magic_name__				=     checkpoint[
															    F'up.{i}.upsample.conv.weight'
															]
															__magic_name__				=     checkpoint[F'up.{i}.upsample.conv.bias']

										if any('block' in layer for layer in up_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,2					).split('.'					)[:2]					) for layer in up_blocks[i] if 'block' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(a_					)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block'] + 1					):
																									__magic_name__				=     {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
																									__magic_name__				=     renew_resnet_paths(blocks[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_       ,additional_replacements=[replace_indices]					)

										if any('attn' in layer for layer in up_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,2					).split('.'					)[:2]					) for layer in up_blocks[i] if 'attn' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(a_					)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block'] + 1					):
																									__magic_name__				=     {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
																									__magic_name__				=     renew_attention_paths(attns[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_       ,additional_replacements=[replace_indices]					)

					__magic_name__				=     {k.replace('mid_new_2'       ,'mid_block'					): v for k, v in new_checkpoint.items()}
					return new_checkpoint




def        _lowercase  (					a_					:       List[str]       ,a_					:       Dict					)					->						int:




					'''simple docstring'''







					__magic_name__				=     {}

					__magic_name__				=     checkpoint['encoder.norm_out.weight']
					__magic_name__				=     checkpoint['encoder.norm_out.bias']

					__magic_name__				=     checkpoint['encoder.conv_in.weight']
					__magic_name__				=     checkpoint['encoder.conv_in.bias']
					__magic_name__				=     checkpoint['encoder.conv_out.weight']
					__magic_name__				=     checkpoint['encoder.conv_out.bias']

					__magic_name__				=     checkpoint['decoder.norm_out.weight']
					__magic_name__				=     checkpoint['decoder.norm_out.bias']

					__magic_name__				=     checkpoint['decoder.conv_in.weight']
					__magic_name__				=     checkpoint['decoder.conv_in.bias']
					__magic_name__				=     checkpoint['decoder.conv_out.weight']
					__magic_name__				=     checkpoint['decoder.conv_out.bias']

					__magic_name__				=     len({'.'.join(layer.split('.'					)[:3]					) for layer in checkpoint if 'down' in layer}					)
					__magic_name__				=     {
					    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(a_					)
					}

					__magic_name__				=     len({'.'.join(layer.split('.'					)[:3]					) for layer in checkpoint if 'up' in layer}					)
					__magic_name__				=     {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(a_					)}

					for i in range(a_					):
										__magic_name__				=     (i - 1) // (config['layers_per_block'] + 1)

										if any('downsample' in layer for layer in down_blocks[i]					):
															__magic_name__				=     checkpoint[
															    F'encoder.down.{i}.downsample.conv.weight'
															]
															__magic_name__				=     checkpoint[
															    F'encoder.down.{i}.downsample.conv.bias'
															]

										if any('block' in layer for layer in down_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,3					).split('.'					)[:3]					) for layer in down_blocks[i] if 'block' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
															    for layer_id in range(a_					)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block']					):
																									__magic_name__				=     renew_resnet_paths(blocks[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_					)

										if any('attn' in layer for layer in down_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,3					).split('.'					)[:3]					) for layer in down_blocks[i] if 'attn' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
															    for layer_id in range(a_					)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block']					):
																									__magic_name__				=     renew_attention_paths(attns[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_       ,config=a_					)

					__magic_name__				=     [key for key in checkpoint if 'mid.block_1' in key]
					__magic_name__				=     [key for key in checkpoint if 'mid.block_2' in key]
					__magic_name__				=     [key for key in checkpoint if 'mid.attn_1' in key]

					# Mid new 2
					__magic_name__				=     renew_resnet_paths(a_					)
					assign_to_checkpoint(
					    a_       ,a_       ,a_       ,additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,)

					__magic_name__				=     renew_resnet_paths(a_					)
					assign_to_checkpoint(
					    a_       ,a_       ,a_       ,additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,)

					__magic_name__				=     renew_attention_paths(a_       ,in_mid=a_					)
					assign_to_checkpoint(
					    a_       ,a_       ,a_       ,additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,)

					for i in range(a_					):
										__magic_name__				=     num_up_blocks - 1 - i

										if any('upsample' in layer for layer in up_blocks[i]					):
															__magic_name__				=     checkpoint[
															    F'decoder.up.{i}.upsample.conv.weight'
															]
															__magic_name__				=     checkpoint[
															    F'decoder.up.{i}.upsample.conv.bias'
															]

										if any('block' in layer for layer in up_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,3					).split('.'					)[:3]					) for layer in up_blocks[i] if 'block' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(a_					)
															}

															if num_blocks > 0:
																				for j in range(config['layers_per_block'] + 1					):
																									__magic_name__				=     {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
																									__magic_name__				=     renew_resnet_paths(blocks[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_       ,additional_replacements=[replace_indices]					)

										if any('attn' in layer for layer in up_blocks[i]					):
															__magic_name__				=     len(
															    {'.'.join(shave_segments(a_       ,3					).split('.'					)[:3]					) for layer in up_blocks[i] if 'attn' in layer}					)
															__magic_name__				=     {
															    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(a_					)
															}

															if num_attn > 0:
																				for j in range(config['layers_per_block'] + 1					):
																									__magic_name__				=     {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
																									__magic_name__				=     renew_attention_paths(attns[j]					)
																									assign_to_checkpoint(a_       ,a_       ,a_       ,additional_replacements=[replace_indices]					)

					__magic_name__				=     {k.replace('mid_new_2'       ,'mid_block'					): v for k, v in new_checkpoint.items()}
					__magic_name__				=     checkpoint['quant_conv.weight']
					__magic_name__				=     checkpoint['quant_conv.bias']
					if "quantize.embedding.weight" in checkpoint:
										__magic_name__				=     checkpoint['quantize.embedding.weight']
					__magic_name__				=     checkpoint['post_quant_conv.weight']
					__magic_name__				=     checkpoint['post_quant_conv.bias']

					return new_checkpoint


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

						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
						)

						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The config json file corresponding to the architecture.",
						)

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						A__							=				parser.parse_args()
						A__							=				torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												A__							=				json.loads(f.read())

						# unet case
						A__							=				{key.split(".")[0] for key in checkpoint.keys()}
						if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
												A__							=				convert_vq_autoenc_checkpoint(checkpoint, config)
						else:
												A__							=				convert_ddpm_checkpoint(checkpoint, config)

						if "ddpm" in config:
												del config["ddpm"]

						if config["_class_name"] == "VQModel":
												A__							=				VQModel(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						elif config["_class_name"] == "AutoencoderKL":
												A__							=				AutoencoderKL(**config)
												model.load_state_dict(converted_checkpoint)
												model.save_pretrained(args.dump_path)
						else:
												A__							=				UNetaDModel(**config)
												model.load_state_dict(converted_checkpoint)

												A__							=				DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

												A__							=				DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)







from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						if np.any(tableau[:, -1]	,			where=tableau[:, -1] < 0   ):
											raise ValueError('RHS must be > 0'   )

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

						# Number of decision variables x1, x2, x3...
						__magic_name__				=     n_vars

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

						# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
						__magic_name__				=     (self.n_art_vars > 0) + 1

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

						# Objectives for each stage
						__magic_name__				=     ['max']

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

						__magic_name__				=     ['']

						# Index of current pivot row and column
						__magic_name__				=     None
						__magic_name__				=     None

						# Does objective row only contain (non)-negative values?
						__magic_name__				=     False







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





						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
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						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 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     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:
											__magic_name__				=     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
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

						# Elements of pivot column within slice
						__magic_name__				=     tableau[s, col_idx]

						# Array filled with nans
						__magic_name__				=     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
						__magic_name__				=     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
						__magic_name__				=     np.nanargmin(__UpperCamelCase   ) + self.n_stages
						return row_idx, col_idx







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     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
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

						# Slice containing ids for artificial columns
						__magic_name__				=     slice(-self.n_art_vars - 1	,			-1   )

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     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   )

											__magic_name__,						__magic_name__				=     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
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

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

											# First entry in the nonzero ids
											__magic_name__				=     nonzero[0][0]
											__magic_name__				=     tableau[nonzero_rowidx, i]

											# If there is only one nonzero value in column, which is one
											if n_nonzero == nonzero_val == 1:
																__magic_name__				=     tableau[nonzero_rowidx, -1]
																__magic_name__				=     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()




