





'''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,
)


A_				     =							{
    "vit_b32": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}




def       A							(      _UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				List[str]=""						,_UpperCAmelCase   :				Any="/"					)  ->				Optional[int]:


  '''simple docstring'''




  __lowerCAmelCase		:    str			     =  []

  for k, v in params.items():
    __lowerCAmelCase		:    Dict			     =  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       A							(      _UpperCAmelCase   :				Dict					)  ->				Any:


  '''simple docstring'''




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




def       A							(      _UpperCAmelCase   :				Optional[Any]						,_UpperCAmelCase   :				Any					)  ->				List[Any]:


  '''simple docstring'''




  __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    str			     =  pt_attn_layer.in_proj_weight.chunk(3						,dim=0					)
  __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Union[str, Any]			     =  pt_attn_layer.in_proj_bias.chunk(3						,dim=0					)

  __lowerCAmelCase		:    Any			     =  pt_attn_layer.out_proj.weight
  __lowerCAmelCase		:    Dict			     =  pt_attn_layer.out_proj.bias

  __lowerCAmelCase		:    int			     =  q_proj
  __lowerCAmelCase		:    List[str]			     =  q_proj_bias

  __lowerCAmelCase		:    str			     =  k_proj
  __lowerCAmelCase		:    List[str]			     =  k_proj_bias

  __lowerCAmelCase		:    Optional[Any]			     =  v_proj
  __lowerCAmelCase		:    Tuple			     =  v_proj_bias

  __lowerCAmelCase		:    List[str]			     =  out_proj_weights
  __lowerCAmelCase		:    Tuple			     =  out_proj_bias




def       A							(      _UpperCAmelCase   :				Dict						,_UpperCAmelCase   :				Any					)  ->				Optional[int]:


  '''simple docstring'''




  copy_linear(hf_mlp.fca						,pt_mlp.c_fc					)
  copy_linear(hf_mlp.fca						,pt_mlp.c_proj					)




def       A							(      _UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				Dict					)  ->				Optional[Any]:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[Any]			     =  pt_linear.weight
  __lowerCAmelCase		:    Tuple			     =  pt_linear.bias




def       A							(      _UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				Dict					)  ->				List[Any]:


  '''simple docstring'''




  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       A							(      _UpperCAmelCase   :				int						,_UpperCAmelCase   :				Optional[Any]					)  ->				Any:


  '''simple docstring'''




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




def       A							(      _UpperCAmelCase   :				Tuple						,_UpperCAmelCase   :				Any					)  ->				Any:


  '''simple docstring'''




  __lowerCAmelCase		:    Tuple			     =  pt_model.token_embedding.weight
  __lowerCAmelCase		:    Dict			     =  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       A							(      _UpperCAmelCase   :				int						,_UpperCAmelCase   :				Optional[Any]					)  ->				List[Any]:


  '''simple docstring'''




  __lowerCAmelCase		:    List[str]			     =  pt_model.text_projection.data.T

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




def       A							(      _UpperCAmelCase   :				Optional[int]						,_UpperCAmelCase   :				int					)  ->				List[str]:


  '''simple docstring'''




  __lowerCAmelCase		:    int			     =  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
  __lowerCAmelCase		:    Union[str, Any]			     =  pt_model.visual.conva.weight.data
  __lowerCAmelCase		:    str			     =  pt_model.visual.class_embedding
  __lowerCAmelCase		:    str			     =  pt_model.visual.positional_embedding.data

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




def       A							(      _UpperCAmelCase   :				Dict						,_UpperCAmelCase   :				List[Any]					)  ->				Union[str, Any]:


  '''simple docstring'''




  __lowerCAmelCase		:    Tuple			     =  flatten_nested_dict(flax_params['backbone']['merged_class_token']					)

  __lowerCAmelCase		:    str			     =  torch.from_numpy(flax_class_token_params['scale']					)
  __lowerCAmelCase		:    Optional[Any]			     =  torch.from_numpy(flax_class_token_params['bias']					)
  __lowerCAmelCase		:    str			     =  nn.Parameter(_UpperCAmelCase					)
  __lowerCAmelCase		:    List[Any]			     =  nn.Parameter(_UpperCAmelCase					)




def       A							(      _UpperCAmelCase   :				Any						,_UpperCAmelCase   :				Any					)  ->				Any:


  '''simple docstring'''




  __lowerCAmelCase		:    List[str]			     =  hf_model.state_dict()
  __lowerCAmelCase		:    Tuple			     =  {}

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

  for flax_key, v in flax_class_params.items():
    __lowerCAmelCase		:    int			     =  flax_key.replace('/'						,'.'					)
    __lowerCAmelCase		:    List[str]			     =  torch_key.replace('.kernel'						,'.weight'					)
    __lowerCAmelCase		:    Union[str, Any]			     =  torch_key.replace('Dense_0'						,'dense0'					)
    __lowerCAmelCase		:    Tuple			     =  'class_head.' + torch_key

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

    __lowerCAmelCase		:    str			     =  nn.Parameter(torch.from_numpy(_UpperCAmelCase					)					)

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

  for flax_key, v in flax_box_params.items():
    __lowerCAmelCase		:    Union[str, Any]			     =  flax_key.replace('/'						,'.'					)
    __lowerCAmelCase		:    Union[str, Any]			     =  torch_key.replace('.kernel'						,'.weight'					)
    __lowerCAmelCase		:    Dict			     =  torch_key.replace('_'						,''					).lower()
    __lowerCAmelCase		:    Tuple			     =  'box_head.' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      __lowerCAmelCase		:    Union[str, Any]			     =  v.T

    __lowerCAmelCase		:    Optional[int]			     =  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       A							(      _UpperCAmelCase   :				Optional[int]						,_UpperCAmelCase   :				Tuple					)  ->				int:


  '''simple docstring'''




  for k, v in flax_attn_params.items():
    if k.startswith('transformer'					):
      __lowerCAmelCase		:    Optional[Any]			     =  k.replace('transformer.resblocks'						,'text_model.encoder.layers'					)
    else:
      __lowerCAmelCase		:    int			     =  k.replace('visual.transformer.resblocks'						,'vision_model.encoder.layers'					)

    __lowerCAmelCase		:    Optional[Any]			     =  torch_key.replace('attn'						,'self_attn'					)
    __lowerCAmelCase		:    Optional[Any]			     =  torch_key.replace('key'						,'k_proj'					)
    __lowerCAmelCase		:    List[Any]			     =  torch_key.replace('value'						,'v_proj'					)
    __lowerCAmelCase		:    List[str]			     =  torch_key.replace('query'						,'q_proj'					)
    __lowerCAmelCase		:    List[Any]			     =  torch_key.replace('out'						,'out_proj'					)

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

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

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

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




def       A							(      _UpperCAmelCase   :				Union[str, Any]					)  ->				Union[str, Any]:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[int]			     =  {}
  __lowerCAmelCase		:    Any			     =  []

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

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




def       A							(      _UpperCAmelCase   :				Union[str, Any]						,_UpperCAmelCase   :				List[str]					)  ->				Any:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[Any]			     =  CLIP(**_UpperCAmelCase					)
  torch_model.eval()
  __lowerCAmelCase		:    Union[str, Any]			     =  torch_model.state_dict()

  __lowerCAmelCase		:    int			     =  flatten_nested_dict(flax_params['backbone']['clip']					)
  __lowerCAmelCase		:    List[str]			     =  {}

  for flax_key, v in flax_clip_params.items():
    __lowerCAmelCase		:    List[Any]			     =  flax_key.replace('/'						,'.'					)
    __lowerCAmelCase		:    Dict			     =  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'					)
    ):
      __lowerCAmelCase		:    Tuple			     =  torch_key[5:]

    __lowerCAmelCase		:    Dict			     =  torch_key.replace('text_projection.kernel'						,'text_projection'					)
    __lowerCAmelCase		:    Tuple			     =  torch_key.replace('visual.proj.kernel'						,'visual.proj'					)
    __lowerCAmelCase		:    List[Any]			     =  torch_key.replace('.scale'						,'.weight'					)
    __lowerCAmelCase		:    Dict			     =  torch_key.replace('.kernel'						,'.weight'					)

    if "conv" in torch_key or "downsample.0.weight" in torch_key:
      __lowerCAmelCase		:    Optional[Any]			     =  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
      __lowerCAmelCase		:    Tuple			     =  v.T

    __lowerCAmelCase		:    int			     =  v

  __lowerCAmelCase		:    str			     =  _convert_attn_layers(_UpperCAmelCase					)
  new_torch_params.update(_UpperCAmelCase					)
  __lowerCAmelCase		:    Dict			     =  {}

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

  return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def       A							(      _UpperCAmelCase   :				Any						,_UpperCAmelCase   :				List[str]						,_UpperCAmelCase   :				Union[str, Any]						,_UpperCAmelCase   :				str						,_UpperCAmelCase   :				Dict=None					)  ->				List[Any]:


  '''simple docstring'''




  __lowerCAmelCase		:    str			     =  Repository(_UpperCAmelCase						,clone_from=F"""google/{pytorch_dump_folder_path}"""					)
  repo.git_pull()

  if config_path is not None:
    __lowerCAmelCase		:    Tuple			     =  OwlViTConfig.from_pretrained(_UpperCAmelCase					)
  else:
    __lowerCAmelCase		:    Union[str, Any]			     =  OwlViTConfig()

  __lowerCAmelCase		:    Dict			     =  OwlViTModel(_UpperCAmelCase					).eval()
  __lowerCAmelCase		:    Optional[Any]			     =  OwlViTForObjectDetection(_UpperCAmelCase					).eval()

  copy_text_model_and_projection(_UpperCAmelCase						,_UpperCAmelCase					)
  copy_vision_model_and_projection(_UpperCAmelCase						,_UpperCAmelCase					)
  __lowerCAmelCase		:    str			     =  pt_backbone.logit_scale
  copy_flax_attn_params(_UpperCAmelCase						,_UpperCAmelCase					)

  __lowerCAmelCase		:    Dict			     =  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
  __lowerCAmelCase		:    List[Any]			     =  OwlViTImageProcessor(
      size=config.vision_config.image_size						,crop_size=config.vision_config.image_size					)
  # Initialize tokenizer
  __lowerCAmelCase		:    Optional[int]			     =  CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'						,pad_token='!'						,model_max_length=1_6					)

  # Initialize processor
  __lowerCAmelCase		:    str			     =  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__":
    A_				     =							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."
    )
    A_				     =							parser.parse_args()

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

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

    # Convert CLIP backbone
    A_     ,  A_     ,  A_				     =							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()





