




'''simple docstring'''





from typing import Any

import numpy as np




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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

      _a				:							Any							       =      tableau
      _a       , _a				:							int							       =      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[str]							       =      (self.n_art_vars > 0) + 1

      # Number of slack variables added to make inequalities into equalities
      _a				:							Optional[Any]							       =      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				:							List[str]							       =      [""""""]

      # Index of current pivot row and column
      _a				:							List[str]							       =      None
      _a				:							List[str]							       =      None

      # Does objective row only contain (non)-negative values?
      _a				:							List[Any]							       =      False



  @staticmethod
  def     a_							(			*__lowerCAmelCase ):







      '''simple docstring'''


      if len(__lowerCAmelCase ) != 3:
          raise ValueError("""Must provide n_vars, n_slack, and n_art_vars""" )

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

      # decision | slack | artificial
      _a				:							Optional[Any]							       =      ["""x""", """s""", """a"""]
      _a				:							Any							       =      []
      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     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.objectives[-1]

      # Find entries of highest magnitude in objective rows
      _a				:							Union[str, Any]							       =      (objective == """min""") - (objective == """max""")
      _a				:							Tuple							       =      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				:							int							       =      slice(self.n_stages		,     self.n_rows )

      # RHS
      _a				:							int							       =      tableau[s, -1]

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

      # Array filled with nans
      _a				:							Dict							       =      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				:							Optional[Any]							       =      np.divide(__lowerCAmelCase		,     __lowerCAmelCase		,     out=__lowerCAmelCase		,     where=divisor > 0 )

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      tableau[row_idx].copy()

      _a				:							Dict							       =      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     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      self.objectives.pop()

      if not self.objectives:
          return tableau

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

      # Delete the artificial variable columns
      _a				:							Union[str, Any]							       =      np.delete(__lowerCAmelCase		,     __lowerCAmelCase		,     axis=1 )

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

      _a				:							int							       =      1
      self.n_rows -= 1
      _a				:							Union[str, Any]							       =      0
      _a				:							Any							       =      False
      return tableau



  def     a_							(			self ):







      '''simple docstring'''


      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				:							Union[str, 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				:							Optional[Any]							       =      self.change_stage(self.tableau )
          else:
              _a				:							List[str]							       =      self.pivot(self.tableau		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return {}



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      {"""P""": abs(tableau[0, -1] )}

      for i in range(self.n_vars ):
          # Gives ids of nonzero entries in the ith column
          _a				:							Any							       =      np.nonzero(tableau[:, i] )
          _a				:							Dict							       =      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				:							List[str]							       =      tableau[nonzero_rowidx, -1]
              _a				:							Dict							       =      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(__lowerCAmelCase		,     0 )
      return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



