import argparse
import json

import torch

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



def    lowerCamelCase_   (  _lowercase ,     _lowercase=1    )							->	str:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split("."    )[n_shave_prefix_segments:]    )
		else:
				return ".".join(path.split("."    )[:n_shave_prefix_segments]    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase=0    )							->	List[Any]:
		__A							:   Tuple	  =				[]
		for old_item in old_list:
				__A							:   List[str]	  =				old_item
				__A							:   str	  =				new_item.replace("block." ,     "resnets."    )
				__A							:   List[Any]	  =				new_item.replace("conv_shorcut" ,     "conv1"    )
				__A							:   Any	  =				new_item.replace("in_shortcut" ,     "conv_shortcut"    )
				__A							:   Tuple	  =				new_item.replace("temb_proj" ,     "time_emb_proj"    )

				__A							:   List[Any]	  =				shave_segments(_lowercase ,     n_shave_prefix_segments=_lowercase    )

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

		return mapping



def    lowerCamelCase_   (  _lowercase ,     _lowercase=0 ,     _lowercase=False    )							->	Union[str, Any]:
		__A							:   Tuple	  =				[]
		for old_item in old_list:
				__A							:   str	  =				old_item

				# In `model.mid`, the layer is called `attn`.
				if not in_mid:
						__A							:   List[Any]	  =				new_item.replace("attn" ,     "attentions"    )
				__A							:   Optional[Any]	  =				new_item.replace(".k." ,     ".key."    )
				__A							:   Union[str, Any]	  =				new_item.replace(".v." ,     ".value."    )
				__A							:   List[Any]	  =				new_item.replace(".q." ,     ".query."    )

				__A							:   Optional[Any]	  =				new_item.replace("proj_out" ,     "proj_attn"    )
				__A							:   Any	  =				new_item.replace("norm" ,     "group_norm"    )

				__A							:   Optional[int]	  =				shave_segments(_lowercase ,     n_shave_prefix_segments=_lowercase    )
				mapping.append({"old": old_item, "new": new_item}    )

		return mapping



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase=None ,     _lowercase=None ,     _lowercase=None    )							->	Optional[int]:
		assert isinstance(_lowercase ,     _lowercase    ), "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():
						__A							:   Dict	  =				old_checkpoint[path]
						__A							:   Any	  =				old_tensor.shape[0] // 3

						__A							:   int	  =				(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

						__A							:   List[Any]	  =				old_tensor.shape[0] // config.get("num_head_channels" ,     1    ) // 3

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

						__A							:   int	  =				query.reshape(_lowercase    ).squeeze()
						__A							:   str	  =				key.reshape(_lowercase    ).squeeze()
						__A							:   int	  =				value.reshape(_lowercase    ).squeeze()

		for path in paths:
				__A							:   Tuple	  =				path["new"]

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				__A							:   int	  =				new_path.replace("down." ,     "down_blocks."    )
				__A							:   Any	  =				new_path.replace("up." ,     "up_blocks."    )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								__A							:   List[Any]	  =				new_path.replace(replacement["old"] ,     replacement["new"]    )

				if "attentions" in new_path:
						__A							:   Optional[int]	  =				old_checkpoint[path["old"]].squeeze()
				else:
						__A							:   int	  =				old_checkpoint[path["old"]]



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	List[Any]:
		__A							:   Any	  =				{}

		__A							:   Union[str, Any]	  =				checkpoint["temb.dense.0.weight"]
		__A							:   Tuple	  =				checkpoint["temb.dense.0.bias"]
		__A							:   Union[str, Any]	  =				checkpoint["temb.dense.1.weight"]
		__A							:   Any	  =				checkpoint["temb.dense.1.bias"]

		__A							:   Dict	  =				checkpoint["norm_out.weight"]
		__A							:   Dict	  =				checkpoint["norm_out.bias"]

		__A							:   Any	  =				checkpoint["conv_in.weight"]
		__A							:   str	  =				checkpoint["conv_in.bias"]
		__A							:   Optional[Any]	  =				checkpoint["conv_out.weight"]
		__A							:   List[Any]	  =				checkpoint["conv_out.bias"]

		__A							:   str	  =				len({".".join(layer.split("."    )[:2]    ) for layer in checkpoint if "down" in layer}    )
		__A							:   List[str]	  =				{
		    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_lowercase    )
		}

		__A							:   int	  =				len({".".join(layer.split("."    )[:2]    ) for layer in checkpoint if "up" in layer}    )
		__A							:   Dict	  =				{layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_lowercase    )}

		for i in range(_lowercase    ):
				__A							:   Tuple	  =				(i - 1) // (config["layers_per_block"] + 1)

				if any("downsample" in layer for layer in down_blocks[i]    ):
						__A							:   Dict	  =				checkpoint[
						    F"down.{i}.downsample.op.weight"
						]
						__A							:   Tuple	  =				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]    ):
						__A							:   Dict	  =				len(
						    {".".join(shave_segments(_lowercase ,     2    ).split("."    )[:2]    ) for layer in down_blocks[i] if "block" in layer}    )
						__A							:   Tuple	  =				{
						    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
						    for layer_id in range(_lowercase    )
						}

						if num_blocks > 0:
								for j in range(config["layers_per_block"]    ):
										__A							:   Any	  =				renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(_lowercase ,     _lowercase ,     _lowercase    )

				if any("attn" in layer for layer in down_blocks[i]    ):
						__A							:   Any	  =				len(
						    {".".join(shave_segments(_lowercase ,     2    ).split("."    )[:2]    ) for layer in down_blocks[i] if "attn" in layer}    )
						__A							:   Tuple	  =				{
						    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
						    for layer_id in range(_lowercase    )
						}

						if num_attn > 0:
								for j in range(config["layers_per_block"]    ):
										__A							:   Tuple	  =				renew_attention_paths(attns[j]    )
										assign_to_checkpoint(_lowercase ,     _lowercase ,     _lowercase ,     config=_lowercase    )

		__A							:   Tuple	  =				[key for key in checkpoint if "mid.block_1" in key]
		__A							:   Any	  =				[key for key in checkpoint if "mid.block_2" in key]
		__A							:   List[str]	  =				[key for key in checkpoint if "mid.attn_1" in key]

		# Mid new 2
		__A							:   str	  =				renew_resnet_paths(_lowercase    )
		assign_to_checkpoint(
		    _lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,     )

		__A							:   Optional[int]	  =				renew_resnet_paths(_lowercase    )
		assign_to_checkpoint(
		    _lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,     )

		__A							:   List[str]	  =				renew_attention_paths(_lowercase ,     in_mid=_lowercase    )
		assign_to_checkpoint(
		    _lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,     )

		for i in range(_lowercase    ):
				__A							:   List[Any]	  =				num_up_blocks - 1 - i

				if any("upsample" in layer for layer in up_blocks[i]    ):
						__A							:   List[str]	  =				checkpoint[
						    F"up.{i}.upsample.conv.weight"
						]
						__A							:   Union[str, Any]	  =				checkpoint[F"up.{i}.upsample.conv.bias"]

				if any("block" in layer for layer in up_blocks[i]    ):
						__A							:   int	  =				len(
						    {".".join(shave_segments(_lowercase ,     2    ).split("."    )[:2]    ) for layer in up_blocks[i] if "block" in layer}    )
						__A							:   Dict	  =				{
						    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_lowercase    )
						}

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

				if any("attn" in layer for layer in up_blocks[i]    ):
						__A							:   Optional[int]	  =				len(
						    {".".join(shave_segments(_lowercase ,     2    ).split("."    )[:2]    ) for layer in up_blocks[i] if "attn" in layer}    )
						__A							:   List[str]	  =				{
						    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_lowercase    )
						}

						if num_attn > 0:
								for j in range(config["layers_per_block"] + 1    ):
										__A							:   Optional[Any]	  =				{"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
										__A							:   int	  =				renew_attention_paths(attns[j]    )
										assign_to_checkpoint(_lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[replace_indices]    )

		__A							:   str	  =				{k.replace("mid_new_2" ,     "mid_block"    ): v for k, v in new_checkpoint.items()}
		return new_checkpoint



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	str:
		__A							:   Optional[int]	  =				{}

		__A							:   List[Any]	  =				checkpoint["encoder.norm_out.weight"]
		__A							:   List[Any]	  =				checkpoint["encoder.norm_out.bias"]

		__A							:   List[Any]	  =				checkpoint["encoder.conv_in.weight"]
		__A							:   List[Any]	  =				checkpoint["encoder.conv_in.bias"]
		__A							:   Tuple	  =				checkpoint["encoder.conv_out.weight"]
		__A							:   Optional[Any]	  =				checkpoint["encoder.conv_out.bias"]

		__A							:   List[Any]	  =				checkpoint["decoder.norm_out.weight"]
		__A							:   str	  =				checkpoint["decoder.norm_out.bias"]

		__A							:   Union[str, Any]	  =				checkpoint["decoder.conv_in.weight"]
		__A							:   Tuple	  =				checkpoint["decoder.conv_in.bias"]
		__A							:   Any	  =				checkpoint["decoder.conv_out.weight"]
		__A							:   Dict	  =				checkpoint["decoder.conv_out.bias"]

		__A							:   Tuple	  =				len({".".join(layer.split("."    )[:3]    ) for layer in checkpoint if "down" in layer}    )
		__A							:   Optional[Any]	  =				{
		    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_lowercase    )
		}

		__A							:   Any	  =				len({".".join(layer.split("."    )[:3]    ) for layer in checkpoint if "up" in layer}    )
		__A							:   Union[str, Any]	  =				{layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_lowercase    )}

		for i in range(_lowercase    ):
				__A							:   Any	  =				(i - 1) // (config["layers_per_block"] + 1)

				if any("downsample" in layer for layer in down_blocks[i]    ):
						__A							:   List[Any]	  =				checkpoint[
						    F"encoder.down.{i}.downsample.conv.weight"
						]
						__A							:   str	  =				checkpoint[
						    F"encoder.down.{i}.downsample.conv.bias"
						]

				if any("block" in layer for layer in down_blocks[i]    ):
						__A							:   List[str]	  =				len(
						    {".".join(shave_segments(_lowercase ,     3    ).split("."    )[:3]    ) for layer in down_blocks[i] if "block" in layer}    )
						__A							:   Any	  =				{
						    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
						    for layer_id in range(_lowercase    )
						}

						if num_blocks > 0:
								for j in range(config["layers_per_block"]    ):
										__A							:   List[str]	  =				renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(_lowercase ,     _lowercase ,     _lowercase    )

				if any("attn" in layer for layer in down_blocks[i]    ):
						__A							:   Tuple	  =				len(
						    {".".join(shave_segments(_lowercase ,     3    ).split("."    )[:3]    ) for layer in down_blocks[i] if "attn" in layer}    )
						__A							:   Optional[int]	  =				{
						    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
						    for layer_id in range(_lowercase    )
						}

						if num_attn > 0:
								for j in range(config["layers_per_block"]    ):
										__A							:   Union[str, Any]	  =				renew_attention_paths(attns[j]    )
										assign_to_checkpoint(_lowercase ,     _lowercase ,     _lowercase ,     config=_lowercase    )

		__A							:   List[str]	  =				[key for key in checkpoint if "mid.block_1" in key]
		__A							:   str	  =				[key for key in checkpoint if "mid.block_2" in key]
		__A							:   Dict	  =				[key for key in checkpoint if "mid.attn_1" in key]

		# Mid new 2
		__A							:   Optional[Any]	  =				renew_resnet_paths(_lowercase    )
		assign_to_checkpoint(
		    _lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,     )

		__A							:   List[str]	  =				renew_resnet_paths(_lowercase    )
		assign_to_checkpoint(
		    _lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,     )

		__A							:   List[Any]	  =				renew_attention_paths(_lowercase ,     in_mid=_lowercase    )
		assign_to_checkpoint(
		    _lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,     )

		for i in range(_lowercase    ):
				__A							:   int	  =				num_up_blocks - 1 - i

				if any("upsample" in layer for layer in up_blocks[i]    ):
						__A							:   Optional[int]	  =				checkpoint[
						    F"decoder.up.{i}.upsample.conv.weight"
						]
						__A							:   Union[str, Any]	  =				checkpoint[
						    F"decoder.up.{i}.upsample.conv.bias"
						]

				if any("block" in layer for layer in up_blocks[i]    ):
						__A							:   Optional[int]	  =				len(
						    {".".join(shave_segments(_lowercase ,     3    ).split("."    )[:3]    ) for layer in up_blocks[i] if "block" in layer}    )
						__A							:   Optional[int]	  =				{
						    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_lowercase    )
						}

						if num_blocks > 0:
								for j in range(config["layers_per_block"] + 1    ):
										__A							:   Any	  =				{"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
										__A							:   Union[str, Any]	  =				renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(_lowercase ,     _lowercase ,     _lowercase ,     additional_replacements=[replace_indices]    )

				if any("attn" in layer for layer in up_blocks[i]    ):
						__A							:   Dict	  =				len(
						    {".".join(shave_segments(_lowercase ,     3    ).split("."    )[:3]    ) for layer in up_blocks[i] if "attn" in layer}    )
						__A							:   Union[str, Any]	  =				{
						    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_lowercase    )
						}

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

		__A							:   int	  =				{k.replace("mid_new_2" ,     "mid_block"    ): v for k, v in new_checkpoint.items()}
		__A							:   List[str]	  =				checkpoint["quant_conv.weight"]
		__A							:   List[str]	  =				checkpoint["quant_conv.bias"]
		if "quantize.embedding.weight" in checkpoint:
				__A							:   List[str]	  =				checkpoint["quantize.embedding.weight"]
		__A							:   Optional[int]	  =				checkpoint["post_quant_conv.weight"]
		__A							:   Optional[Any]	  =				checkpoint["post_quant_conv.bias"]

		return new_checkpoint


if __name__ == "__main__":
					UpperCamelCase      			=     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.')

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

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

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

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

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

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

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






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()





