
"""simple docstring"""




import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


snake_case					  =						{
    'vit_b32': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 3_2,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_b16': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 1_6,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_l14': {
        'embed_dim': 7_6_8,
        'image_resolution': 8_4_0,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 2_4,
        'vision_width': 1_0_2_4,
        'vision_patch_size': 1_4,
        'transformer_width': 7_6_8,
        'transformer_heads': 1_2,
        'transformer_layers': 1_2,
    },
}

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_="",	SCREAMING_SNAKE_CASE_="/"     ):
					SCREAMING_SNAKE_CASE           =      []

					for k, v in params.items():
										SCREAMING_SNAKE_CASE           =      parent_key + sep + k if parent_key else k

										if isinstance(_UpperCAmelCase,	collections.MutableMapping     ):
															items.extend(flatten_nested_dict(_UpperCAmelCase,	_UpperCAmelCase,	sep=_UpperCAmelCase     ).items()     )
										else:
															items.append((new_key, v)     )
					return dict(_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE_     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x,	_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      pt_attn_layer.in_proj_weight.chunk(3,	dim=0     )
					SCREAMING_SNAKE_CASE           =      pt_attn_layer.in_proj_bias.chunk(3,	dim=0     )

					SCREAMING_SNAKE_CASE           =      pt_attn_layer.out_proj.weight
					SCREAMING_SNAKE_CASE           =      pt_attn_layer.out_proj.bias

					SCREAMING_SNAKE_CASE           =      q_proj
					SCREAMING_SNAKE_CASE           =      q_proj_bias

					SCREAMING_SNAKE_CASE           =      k_proj
					SCREAMING_SNAKE_CASE           =      k_proj_bias

					SCREAMING_SNAKE_CASE           =      v_proj
					SCREAMING_SNAKE_CASE           =      v_proj_bias

					SCREAMING_SNAKE_CASE           =      out_proj_weights
					SCREAMING_SNAKE_CASE           =      out_proj_bias

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					copy_linear(hf_mlp.fca,	pt_mlp.c_fc     )
					copy_linear(hf_mlp.fca,	pt_mlp.c_proj     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      pt_linear.weight
					SCREAMING_SNAKE_CASE           =      pt_linear.bias

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					copy_linear(hf_layer.layer_norma,	pt_layer.ln_a     )
					copy_linear(hf_layer.layer_norma,	pt_layer.ln_a     )

					# copy MLP
					copy_mlp(hf_layer.mlp,	pt_layer.mlp     )

					# copy attn
					copy_attn_layer(hf_layer.self_attn,	pt_layer.attn     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):




					for hf_layer, pt_layer in zip(_UpperCAmelCase,	_UpperCAmelCase     ):
										copy_layer(_UpperCAmelCase,	_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      pt_model.token_embedding.weight
					SCREAMING_SNAKE_CASE           =      pt_model.positional_embedding

					# copy layer norm
					copy_linear(hf_encoder.final_layer_norm,	pt_model.ln_final     )

					# copy hidden layers
					copy_layers(hf_encoder.encoder.layers,	pt_model.transformer.resblocks     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      pt_model.text_projection.data.T

					# copy text encoder
					copy_encoder(hf_model.text_model,	_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      pt_model.visual.proj.data.T

					# copy layer norms
					copy_linear(hf_model.vision_model.pre_layernorm,	pt_model.visual.ln_pre     )
					copy_linear(hf_model.vision_model.post_layernorm,	pt_model.visual.ln_post     )

					# copy embeds
					SCREAMING_SNAKE_CASE           =      pt_model.visual.conva.weight.data
					SCREAMING_SNAKE_CASE           =      pt_model.visual.class_embedding
					SCREAMING_SNAKE_CASE           =      pt_model.visual.positional_embedding.data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers,	pt_model.visual.transformer.resblocks     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      flatten_nested_dict(flax_params['backbone']['merged_class_token']     )

					SCREAMING_SNAKE_CASE           =      torch.from_numpy(flax_class_token_params['scale']     )
					SCREAMING_SNAKE_CASE           =      torch.from_numpy(flax_class_token_params['bias']     )
					SCREAMING_SNAKE_CASE           =      nn.Parameter(_UpperCAmelCase     )
					SCREAMING_SNAKE_CASE           =      nn.Parameter(_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      hf_model.state_dict()
					SCREAMING_SNAKE_CASE           =      {}

					# Rename class prediction head flax params to pytorch HF
					SCREAMING_SNAKE_CASE           =      flatten_nested_dict(flax_params['class_head']     )

					for flax_key, v in flax_class_params.items():
										SCREAMING_SNAKE_CASE           =      flax_key.replace('/',	'.'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('.kernel',	'.weight'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('Dense_0',	'dense0'     )
										SCREAMING_SNAKE_CASE           =      'class_head.' + torch_key

										if "weight" in torch_key and v.ndim == 2:
															SCREAMING_SNAKE_CASE           =      v.T

										SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.from_numpy(_UpperCAmelCase     )     )

					# Rename box prediction box flax params to pytorch HF
					SCREAMING_SNAKE_CASE           =      flatten_nested_dict(flax_params['obj_box_head']     )

					for flax_key, v in flax_box_params.items():
										SCREAMING_SNAKE_CASE           =      flax_key.replace('/',	'.'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('.kernel',	'.weight'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('_',	''     ).lower()
										SCREAMING_SNAKE_CASE           =      'box_head.' + torch_key

										if "weight" in torch_key and v.ndim == 2:
															SCREAMING_SNAKE_CASE           =      v.T

										SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.from_numpy(_UpperCAmelCase     )     )

					# Copy flax params to PyTorch params
					for name, param in new_params.items():
										if name in pt_params.keys():
															pt_params[name].copy_(_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):




					for k, v in flax_attn_params.items():
										if k.startswith('transformer'     ):
															SCREAMING_SNAKE_CASE           =      k.replace('transformer.resblocks',	'text_model.encoder.layers'     )
										else:
															SCREAMING_SNAKE_CASE           =      k.replace('visual.transformer.resblocks',	'vision_model.encoder.layers'     )

										SCREAMING_SNAKE_CASE           =      torch_key.replace('attn',	'self_attn'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('key',	'k_proj'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('value',	'v_proj'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('query',	'q_proj'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('out',	'out_proj'     )

										if "bias" in torch_key and v.ndim == 2:
															SCREAMING_SNAKE_CASE           =      v.shape[0] * v.shape[1]
															SCREAMING_SNAKE_CASE           =      v.reshape(_UpperCAmelCase     )

										if "weight" in torch_key and "out" in torch_key:
															SCREAMING_SNAKE_CASE           =      (v.shape[0] * v.shape[1], v.shape[2])
															SCREAMING_SNAKE_CASE           =      v.reshape(_UpperCAmelCase     ).T

										if "weight" in torch_key and "out" not in torch_key:
															SCREAMING_SNAKE_CASE           =      (v.shape[0], v.shape[1] * v.shape[2])
															SCREAMING_SNAKE_CASE           =      v.reshape(_UpperCAmelCase     ).T

										# Copy flax CLIP attn params to HF PyTorch params
										SCREAMING_SNAKE_CASE           =      torch.from_numpy(_UpperCAmelCase     )
										hf_backbone.state_dict()[torch_key].copy_(_UpperCAmelCase     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      {}
					SCREAMING_SNAKE_CASE           =      []

					for k, v in params.items():
										if "attn." in k:
															SCREAMING_SNAKE_CASE           =      k[: k.rindex('attn.'     ) + 5]
															if base in processed_attn_layers:
																				continue

															processed_attn_layers.append(_UpperCAmelCase     )
															SCREAMING_SNAKE_CASE           =      params[base + 'out.weight'].shape[-1]
															SCREAMING_SNAKE_CASE           =      params[base + 'out.weight'].reshape(_UpperCAmelCase,	_UpperCAmelCase     ).T
															SCREAMING_SNAKE_CASE           =      params[base + 'out.bias']
										else:
															SCREAMING_SNAKE_CASE           =      v
					return new_params

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      CLIP(**_UpperCAmelCase     )
					torch_model.eval()
					SCREAMING_SNAKE_CASE           =      torch_model.state_dict()

					SCREAMING_SNAKE_CASE           =      flatten_nested_dict(flax_params['backbone']['clip']     )
					SCREAMING_SNAKE_CASE           =      {}

					for flax_key, v in flax_clip_params.items():
										SCREAMING_SNAKE_CASE           =      flax_key.replace('/',	'.'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('text.token_embedding.embedding',	'token_embedding.kernel'     )

										if (
										    torch_key.startswith('text.transformer'     )
										    or torch_key.startswith('text.text_projection'     )
										    or torch_key.startswith('text.ln_final'     )
										    or torch_key.startswith('text.positional_embedding'     )
										):
															SCREAMING_SNAKE_CASE           =      torch_key[5:]

										SCREAMING_SNAKE_CASE           =      torch_key.replace('text_projection.kernel',	'text_projection'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('visual.proj.kernel',	'visual.proj'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('.scale',	'.weight'     )
										SCREAMING_SNAKE_CASE           =      torch_key.replace('.kernel',	'.weight'     )

										if "conv" in torch_key or "downsample.0.weight" in torch_key:
															SCREAMING_SNAKE_CASE           =      v.transpose(3,	2,	0,	1     )

										elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
															# Fully connected layers are transposed, embeddings are not
															SCREAMING_SNAKE_CASE           =      v.T

										SCREAMING_SNAKE_CASE           =      v

					SCREAMING_SNAKE_CASE           =      _convert_attn_layers(_UpperCAmelCase     )
					new_torch_params.update(_UpperCAmelCase     )
					SCREAMING_SNAKE_CASE           =      {}

					# Copy flax CLIP backbone params to PyTorch params
					for name, param in new_torch_params.items():
										if name in torch_clip_params.keys():
															SCREAMING_SNAKE_CASE           =      torch.from_numpy(new_torch_params[name]     )
															torch_clip_params[name].copy_(_UpperCAmelCase     )
										else:
															SCREAMING_SNAKE_CASE           =      param

					return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=None     ):
					SCREAMING_SNAKE_CASE           =      Repository(_UpperCAmelCase,	clone_from=f'''google/{pytorch_dump_folder_path}'''     )
					repo.git_pull()

					if config_path is not None:
										SCREAMING_SNAKE_CASE           =      OwlViTConfig.from_pretrained(_UpperCAmelCase     )
					else:
										SCREAMING_SNAKE_CASE           =      OwlViTConfig()

					SCREAMING_SNAKE_CASE           =      OwlViTModel(_UpperCAmelCase     ).eval()
					SCREAMING_SNAKE_CASE           =      OwlViTForObjectDetection(_UpperCAmelCase     ).eval()

					copy_text_model_and_projection(_UpperCAmelCase,	_UpperCAmelCase     )
					copy_vision_model_and_projection(_UpperCAmelCase,	_UpperCAmelCase     )
					SCREAMING_SNAKE_CASE           =      pt_backbone.logit_scale
					copy_flax_attn_params(_UpperCAmelCase,	_UpperCAmelCase     )

					SCREAMING_SNAKE_CASE           =      hf_backbone
					copy_class_merge_token(_UpperCAmelCase,	_UpperCAmelCase     )
					copy_class_box_heads(_UpperCAmelCase,	_UpperCAmelCase     )

					# Save HF model
					hf_model.save_pretrained(repo.local_dir     )

					# Initialize image processor
					SCREAMING_SNAKE_CASE           =      OwlViTImageProcessor(
					    size=config.vision_config.image_size,	crop_size=config.vision_config.image_size     )
					# Initialize tokenizer
					SCREAMING_SNAKE_CASE           =      CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32',	pad_token='!',	model_max_length=1_6     )

					# Initialize processor
					SCREAMING_SNAKE_CASE           =      OwlViTProcessor(image_processor=_UpperCAmelCase,	tokenizer=_UpperCAmelCase     )
					image_processor.save_pretrained(repo.local_dir     )
					processor.save_pretrained(repo.local_dir     )

					repo.git_add()
					repo.git_commit('Upload model and processor'     )
					repo.git_push()


if __name__ == "__main__":
	snake_case					  =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--owlvit_version',
	    default=None,
	    type=str,
	    required=True,
	    help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
	)
	parser.add_argument(
	    '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
	)
	parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
	parser.add_argument(
	    '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
	)
	snake_case					  =						parser.parse_args()

	# Initialize PyToch clip model
	snake_case					  =						args.owlvit_version
	if model_name == "clip_b16":
		snake_case					  =						CONFIGS['vit_b16']
	elif model_name == "clip_b32":
		snake_case					  =						CONFIGS['vit_b32']
	elif model_name == "clip_l14":
		snake_case					  =						CONFIGS['vit_l14']

	# Load from checkpoint and convert params to float-32
	snake_case					  =						checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
	snake_case					  =						jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
	del variables

	# Convert CLIP backbone
	snake_case  ,     snake_case  ,     snake_case					  =						convert_clip_backbone(flax_params, torch_config)

	convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)







'''simple docstring'''


from typing import Any

import numpy as np


class 		UpperCamelCase__     :
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  None:
        # Check if RHS is negative
        if np.any(tableau[:, -1]     , where=tableau[:, -1] < 0 ):
          raise ValueError('RHS must be > 0' )

        __lowerCAmelCase		:    Any			     =  tableau
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Optional[int]			     =  tableau.shape

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

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

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

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

        # Objectives for each stage
        __lowerCAmelCase		:    List[Any]			     =  ['max']

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

        __lowerCAmelCase		:    Optional[Any]			     =  ['']

        # Index of current pivot row and column
        __lowerCAmelCase		:    List[Any]			     =  None
        __lowerCAmelCase		:    Dict			     =  None

        # Does objective row only contain (non)-negative values?
        __lowerCAmelCase		:    Any			     =  False




      @staticmethod
      def 	snake_case  (     *SCREAMING_SNAKE_CASE )				->  list[str]:
        if len(SCREAMING_SNAKE_CASE ) != 3:
          raise ValueError('Must provide n_vars, n_slack, and n_art_vars' )

        if not all(x >= 0 and isinstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) for x in args ):
          raise ValueError('All arguments must be non-negative integers' )

        # decision | slack | artificial
        __lowerCAmelCase		:    int			     =  ['x', 's', 'a']
        __lowerCAmelCase		:    Tuple			     =  []
        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 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  tuple[Any, Any]:
        __lowerCAmelCase		:    List[Any]			     =  self.objectives[-1]

        # Find entries of highest magnitude in objective rows
        __lowerCAmelCase		:    List[Any]			     =  (objective == 'min') - (objective == 'max')
        __lowerCAmelCase		:    Dict			     =  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:
          __lowerCAmelCase		:    Dict			     =  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
        __lowerCAmelCase		:    str			     =  slice(self.n_stages     , self.n_rows )

        # RHS
        __lowerCAmelCase		:    Union[str, Any]			     =  tableau[s, -1]

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

        # Array filled with nans
        __lowerCAmelCase		:    Optional[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
        __lowerCAmelCase		:    Tuple			     =  np.divide(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , out=SCREAMING_SNAKE_CASE     , where=divisor > 0 )

        # Arg of minimum quotient excluding the nan values. n_stages is added
        # to compensate for earlier exclusion of objective columns
        __lowerCAmelCase		:    Any			     =  np.nanargmin(SCREAMING_SNAKE_CASE ) + self.n_stages
        return row_idx, col_idx




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  np.ndarray:
        __lowerCAmelCase		:    Tuple			     =  tableau[row_idx].copy()

        __lowerCAmelCase		:    Optional[int]			     =  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
        __lowerCAmelCase		:    str			     =  piv_row
        return tableau




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  np.ndarray:
        self.objectives.pop()

        if not self.objectives:
          return tableau

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

        # Delete the artificial variable columns
        __lowerCAmelCase		:    str			     =  np.delete(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , axis=1 )

        # Delete the objective row of the first stage
        __lowerCAmelCase		:    Optional[int]			     =  np.delete(SCREAMING_SNAKE_CASE     , 0     , axis=0 )

        __lowerCAmelCase		:    Tuple			     =  1
        self.n_rows -= 1
        __lowerCAmelCase		:    str			     =  0
        __lowerCAmelCase		:    Any			     =  False
        return tableau




      def 	snake_case  (     self )				->  dict[Any, Any]:
        # Stop simplex algorithm from cycling.
        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:
            __lowerCAmelCase		:    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 )

          __lowerCAmelCase     ,					__lowerCAmelCase		:    Tuple			     =  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
            __lowerCAmelCase		:    Dict			     =  self.change_stage(self.tableau )
          else:
            __lowerCAmelCase		:    Optional[Any]			     =  self.pivot(self.tableau     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        return {}







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  dict[str, float]:
        __lowerCAmelCase		:    List[Any]			     =  {'P': abs(tableau[0, -1] )}

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

          # First entry in the nonzero ids
          __lowerCAmelCase		:    Optional[Any]			     =  nonzero[0][0]
          __lowerCAmelCase		:    str			     =  tableau[nonzero_rowidx, i]

          # If there is only one nonzero value in column, which is one
          if n_nonzero == nonzero_val == 1:
            __lowerCAmelCase		:    Dict			     =  tableau[nonzero_rowidx, -1]
            __lowerCAmelCase		:    List[Any]			     =  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(SCREAMING_SNAKE_CASE     , 0 )
        return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()





