





'''simple docstring'''


import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
    import tensorflow as tf

    tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset


A_				     =							5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def       A							(      _UpperCAmelCase   :				Namespace					)  ->				int:


  '''simple docstring'''




  return PTtoTFCommand(
      args.model_name						,args.local_dir						,args.max_error						,args.new_weights						,args.no_pr						,args.push						,args.extra_commit_description						,args.override_model_class						,)


class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      @staticmethod
      def 	snake_case  (     SCREAMING_SNAKE_CASE )				->  List[Any]:
        __lowerCAmelCase		:    Union[str, Any]			     =  parser.add_parser(
            'pt-to-tf'     , help=(
                'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
                ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
            )     , )
        train_parser.add_argument(
            '--model-name'     , type=SCREAMING_SNAKE_CASE     , required=SCREAMING_SNAKE_CASE     , help='The model name, including owner/organization, as seen on the hub.'     , )
        train_parser.add_argument(
            '--local-dir'     , type=SCREAMING_SNAKE_CASE     , default=''     , help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'     , )
        train_parser.add_argument(
            '--max-error'     , type=SCREAMING_SNAKE_CASE     , default=SCREAMING_SNAKE_CASE     , help=(
                F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
            )     , )
        train_parser.add_argument(
            '--new-weights'     , action='store_true'     , help='Optional flag to create new TensorFlow weights, even if they already exist.'     , )
        train_parser.add_argument(
            '--no-pr'     , action='store_true'     , help='Optional flag to NOT open a PR with converted weights.' )
        train_parser.add_argument(
            '--push'     , action='store_true'     , help='Optional flag to push the weights directly to `main` (requires permissions)'     , )
        train_parser.add_argument(
            '--extra-commit-description'     , type=SCREAMING_SNAKE_CASE     , default=''     , help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).'     , )
        train_parser.add_argument(
            '--override-model-class'     , type=SCREAMING_SNAKE_CASE     , default=SCREAMING_SNAKE_CASE     , help='If you think you know better than the auto-detector, you can specify the model class here. '
            'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'     , )
        train_parser.set_defaults(func=SCREAMING_SNAKE_CASE )




      @staticmethod
      def 	snake_case  (     SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  List[Any]:
        __lowerCAmelCase		:    Tuple			     =  set(pt_outputs.keys() )
        __lowerCAmelCase		:    List[Any]			     =  set(tf_outputs.keys() )
        if pt_out_attrs != tf_out_attrs:
          raise ValueError(
              F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
              F""" {tf_out_attrs})""" )

        # 2. For each output attribute, computes the difference
        def _find_pt_tf_differences(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE="" ):
          # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
          # recursivelly, keeping the name of the attribute.
          if isinstance(SCREAMING_SNAKE_CASE     , torch.Tensor ):
            __lowerCAmelCase		:    List[str]			     =  np.max(np.abs(pt_out.numpy() - tf_out.numpy() ) )
            __lowerCAmelCase		:    Union[str, Any]			     =  tensor_difference
          else:
            __lowerCAmelCase		:    Optional[Any]			     =  attr_name
            for i, pt_item in enumerate(SCREAMING_SNAKE_CASE ):
              # If it is a named attribute, we keep the name. Otherwise, just its index.
              if isinstance(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ):
                __lowerCAmelCase		:    List[Any]			     =  root_name + pt_item
                __lowerCAmelCase		:    Any			     =  tf_out[pt_item]
                __lowerCAmelCase		:    int			     =  pt_out[pt_item]
              else:
                __lowerCAmelCase		:    List[Any]			     =  root_name + F"""[{i}]"""
                __lowerCAmelCase		:    List[Any]			     =  tf_out[i]
              __lowerCAmelCase		:    Union[str, Any]			     =  _find_pt_tf_differences(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

          return differences

        return _find_pt_tf_differences(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , {} )




      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , *SCREAMING_SNAKE_CASE     , )				->  List[Any]:
        __lowerCAmelCase		:    Union[str, Any]			     =  logging.get_logger('transformers-cli/pt_to_tf' )
        __lowerCAmelCase		:    List[str]			     =  model_name
        __lowerCAmelCase		:    str			     =  local_dir if local_dir else os.path.join('/tmp'     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  max_error
        __lowerCAmelCase		:    Union[str, Any]			     =  new_weights
        __lowerCAmelCase		:    List[str]			     =  no_pr
        __lowerCAmelCase		:    str			     =  push
        __lowerCAmelCase		:    Optional[int]			     =  extra_commit_description
        __lowerCAmelCase		:    Tuple			     =  override_model_class




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  int:

        def _get_audio_input():
          __lowerCAmelCase		:    Tuple			     =  load_dataset('hf-internal-testing/librispeech_asr_dummy'     , 'clean'     , split='validation' )
          __lowerCAmelCase		:    int			     =  ds.sort('id' ).select(range(2 ) )[:2]['audio']
          __lowerCAmelCase		:    List[Any]			     =  [x['array'] for x in speech_samples]
          return raw_samples

        __lowerCAmelCase		:    int			     =  type(pt_model.config )
        if model_config_class in PROCESSOR_MAPPING:
          __lowerCAmelCase		:    int			     =  AutoProcessor.from_pretrained(self._local_dir )
          if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
            __lowerCAmelCase		:    int			     =  processor.tokenizer.eos_token
        elif model_config_class in IMAGE_PROCESSOR_MAPPING:
          __lowerCAmelCase		:    Any			     =  AutoImageProcessor.from_pretrained(self._local_dir )
        elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
          __lowerCAmelCase		:    Optional[int]			     =  AutoFeatureExtractor.from_pretrained(self._local_dir )
        elif model_config_class in TOKENIZER_MAPPING:
          __lowerCAmelCase		:    Optional[int]			     =  AutoTokenizer.from_pretrained(self._local_dir )
          if processor.pad_token is None:
            __lowerCAmelCase		:    List[str]			     =  processor.eos_token
        else:
          raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})""" )

        __lowerCAmelCase		:    Optional[int]			     =  set(inspect.signature(pt_model.forward ).parameters.keys() )
        __lowerCAmelCase		:    List[str]			     =  {}
        if "input_ids" in model_forward_signature:
          processor_inputs.update(
              {
                  'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
                  'padding': True,
                  'truncation': True,
              } )
        if "pixel_values" in model_forward_signature:
          __lowerCAmelCase		:    Any			     =  load_dataset('cifar10'     , 'plain_text'     , split='test' )[:2]['img']
          processor_inputs.update({'images': sample_images} )
        if "input_features" in model_forward_signature:
          __lowerCAmelCase		:    Optional[Any]			     =  inspect.signature(processor.feature_extractor ).parameters
          # Pad to the largest input length by default but take feature extractor default
          # padding value if it exists e.g. "max_length" and is not False or None
          if "padding" in feature_extractor_signature:
            __lowerCAmelCase		:    Any			     =  feature_extractor_signature['padding'].default
            if default_strategy is not False and default_strategy is not None:
              __lowerCAmelCase		:    Dict			     =  default_strategy
            else:
              __lowerCAmelCase		:    Any			     =  True
          else:
            __lowerCAmelCase		:    str			     =  True
          processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy} )
        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
          processor_inputs.update({'audio': _get_audio_input(), 'padding': True} )
        __lowerCAmelCase		:    str			     =  processor(**SCREAMING_SNAKE_CASE     , return_tensors='pt' )
        __lowerCAmelCase		:    str			     =  processor(**SCREAMING_SNAKE_CASE     , return_tensors='tf' )

        # Extra input requirements, in addition to the input modality
        if (
            config.is_encoder_decoder
            or (hasattr(SCREAMING_SNAKE_CASE     , 'encoder' ) and hasattr(SCREAMING_SNAKE_CASE     , 'decoder' ))
            or "decoder_input_ids" in tf_dummy_inputs
        ):
          __lowerCAmelCase		:    int			     =  np.asarray([[1], [1]]     , dtype=SCREAMING_SNAKE_CASE ) * (pt_model.config.decoder_start_token_id or 0)
          pt_input.update({'decoder_input_ids': torch.tensor(SCREAMING_SNAKE_CASE )} )
          tf_input.update({'decoder_input_ids': tf.convert_to_tensor(SCREAMING_SNAKE_CASE )} )

        return pt_input, tf_input







      def 	snake_case  (     self )				->  Dict:
        # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
        if version.parse(huggingface_hub.__version__ ) < version.parse('0.9.0' ):
          raise ImportError(
              'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
              ' installation.' )
        else:
          from huggingface_hub import Repository, create_commit
          from huggingface_hub._commit_api import CommitOperationAdd

        # Fetch remote data
        __lowerCAmelCase		:    List[str]			     =  Repository(local_dir=self._local_dir     , clone_from=self._model_name )

        # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
        __lowerCAmelCase		:    int			     =  AutoConfig.from_pretrained(self._local_dir )
        __lowerCAmelCase		:    List[str]			     =  config.architectures
        if self._override_model_class is not None:
          if self._override_model_class.startswith('TF' ):
            __lowerCAmelCase		:    List[str]			     =  [self._override_model_class[2:]]
          else:
            __lowerCAmelCase		:    int			     =  [self._override_model_class]
          try:
            __lowerCAmelCase		:    Dict			     =  getattr(import_module('transformers' )     , architectures[0] )
          except AttributeError:
            raise ValueError(F"""Model class {self._override_model_class} not found in transformers.""" )
          try:
            __lowerCAmelCase		:    int			     =  getattr(import_module('transformers' )     , 'TF' + architectures[0] )
          except AttributeError:
            raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers.""" )
        elif architectures is None:  # No architecture defined -- use auto classes
          __lowerCAmelCase		:    int			     =  getattr(import_module('transformers' )     , 'AutoModel' )
          __lowerCAmelCase		:    Dict			     =  getattr(import_module('transformers' )     , 'TFAutoModel' )
          self._logger.warning('No detected architecture, using AutoModel/TFAutoModel' )
        else:  # Architecture defined -- use it
          if len(SCREAMING_SNAKE_CASE ) > 1:
            raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})""" )
          self._logger.warning(F"""Detected architecture: {architectures[0]}""" )
          __lowerCAmelCase		:    Dict			     =  getattr(import_module('transformers' )     , architectures[0] )
          try:
            __lowerCAmelCase		:    Optional[Any]			     =  getattr(import_module('transformers' )     , 'TF' + architectures[0] )
          except AttributeError:
            raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.""" )

        # Check the TF dummy inputs to see what keys we need in the forward pass
        __lowerCAmelCase		:    Any			     =  tf_class.from_config(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Tuple			     =  tf_from_pt_model.dummy_inputs

        del tf_from_pt_model  # Try to keep only one model in memory at a time

        # Load the model and get some basic inputs
        __lowerCAmelCase		:    Tuple			     =  pt_class.from_pretrained(self._local_dir )
        pt_model.eval()

        __lowerCAmelCase     ,					__lowerCAmelCase		:    int			     =  self.get_inputs(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

        with torch.no_grad():
          __lowerCAmelCase		:    Tuple			     =  pt_model(**SCREAMING_SNAKE_CASE     , output_hidden_states=SCREAMING_SNAKE_CASE )
        del pt_model  # will no longer be used, and may have a large memory footprint

        __lowerCAmelCase		:    Optional[Any]			     =  tf_class.from_pretrained(self._local_dir     , from_pt=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[str]			     =  tf_from_pt_model(**SCREAMING_SNAKE_CASE     , output_hidden_states=SCREAMING_SNAKE_CASE     , training=SCREAMING_SNAKE_CASE )

        # Confirms that cross loading PT weights into TF worked.
        __lowerCAmelCase		:    Dict			     =  self.find_pt_tf_differences(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  {k: v for k, v in crossload_differences.items() if 'hidden' not in k}
        __lowerCAmelCase		:    Optional[Any]			     =  {k: v for k, v in crossload_differences.items() if 'hidden' in k}
        if len(SCREAMING_SNAKE_CASE ) == 0 and architectures is not None:
          raise ValueError(
              F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
              ' output was found. All outputs start with \'hidden\'' )
        __lowerCAmelCase		:    Tuple			     =  max(output_differences.values() ) if output_differences else 0.0
        __lowerCAmelCase		:    List[Any]			     =  max(hidden_differences.values() )
        if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
          raise ValueError(
              'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
              + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
              + '\n'.join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error] )
              + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
              + '\n'.join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error] ) )

        # Save the weights in a TF format (if needed) and confirms that the results are still good
        __lowerCAmelCase		:    Tuple			     =  os.path.join(self._local_dir     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Union[str, Any]			     =  os.path.join(self._local_dir     , SCREAMING_SNAKE_CASE )
        if (not os.path.exists(SCREAMING_SNAKE_CASE ) and not os.path.exists(SCREAMING_SNAKE_CASE )) or self._new_weights:
          tf_from_pt_model.save_pretrained(self._local_dir )
        del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

        __lowerCAmelCase		:    int			     =  tf_class.from_pretrained(self._local_dir )
        __lowerCAmelCase		:    Optional[Any]			     =  tf_model(**SCREAMING_SNAKE_CASE     , output_hidden_states=SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    Tuple			     =  self.find_pt_tf_differences(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Dict			     =  {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
        __lowerCAmelCase		:    Union[str, Any]			     =  {k: v for k, v in conversion_differences.items() if 'hidden' in k}
        if len(SCREAMING_SNAKE_CASE ) == 0 and architectures is not None:
          raise ValueError(
              F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
              ' output was found. All outputs start with \'hidden\'' )
        __lowerCAmelCase		:    Tuple			     =  max(output_differences.values() ) if output_differences else 0.0
        __lowerCAmelCase		:    str			     =  max(hidden_differences.values() )
        if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
          raise ValueError(
              'The converted TensorFlow model has different outputs, something went wrong!\n'
              + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
              + '\n'.join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error] )
              + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
              + '\n'.join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error] ) )

        __lowerCAmelCase		:    Dict			     =  'Update TF weights' if self._new_weights else 'Add TF weights'
        if self._push:
          repo.git_add(auto_lfs_track=SCREAMING_SNAKE_CASE )
          repo.git_commit(SCREAMING_SNAKE_CASE )
          repo.git_push(blocking=SCREAMING_SNAKE_CASE )  # this prints a progress bar with the upload
          self._logger.warning(F"""TF weights pushed into {self._model_name}""" )
        elif not self._no_pr:
          self._logger.warning('Uploading the weights into a new PR...' )
          __lowerCAmelCase		:    Dict			     =  (
              'Model converted by the [`transformers`\' `pt_to_tf`'
              ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
              'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
              F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
              F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
              F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
              F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
          )
          if self._max_error > MAX_ERROR:
            commit_descrition += (
                F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
            )
          if self._extra_commit_description:
            commit_descrition += "\n\n" + self._extra_commit_description

          # sharded model -> adds all related files (index and .h5 shards)
          if os.path.exists(SCREAMING_SNAKE_CASE ):
            __lowerCAmelCase		:    int			     =  [
                CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE     , path_or_fileobj=SCREAMING_SNAKE_CASE )
            ]
            for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5' ):
              operations += [
                  CommitOperationAdd(path_in_repo=os.path.basename(SCREAMING_SNAKE_CASE )     , path_or_fileobj=SCREAMING_SNAKE_CASE )
              ]
          else:
            __lowerCAmelCase		:    str			     =  [CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE     , path_or_fileobj=SCREAMING_SNAKE_CASE )]

          __lowerCAmelCase		:    Tuple			     =  create_commit(
              repo_id=self._model_name     , operations=SCREAMING_SNAKE_CASE     , commit_message=SCREAMING_SNAKE_CASE     , commit_description=SCREAMING_SNAKE_CASE     , repo_type='model'     , create_pr=SCREAMING_SNAKE_CASE     , ).pr_url
          self._logger.warning(F"""PR open in {hub_pr_url}""" )












'''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()





