code: 
  
  from typing import Any
  
  import numpy as np
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__  )     ->      None:
          # Check if RHS is negative
          if np.any(tableau[:, -1]     ,      where=tableau[:, -1] < 0  ):
              raise ValueError('''RHS must be > 0'''  )
  
          __UpperCAmelCase									=			tableau
          __UpperCAmelCase					,     __UpperCAmelCase									=			tableau.shape
  
          # Number of decision variables x1, x2, x3...
          __UpperCAmelCase									=			n_vars
  
          # Number of artificial variables to be minimised
          __UpperCAmelCase									=			len(np.where(tableau[self.n_vars : -1] == -1  )[0]  )
  
          # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
          __UpperCAmelCase									=			(self.n_art_vars > 0) + 1
  
          # Number of slack variables added to make inequalities into equalities
          __UpperCAmelCase									=			self.n_rows - self.n_stages
  
          # Objectives for each stage
          __UpperCAmelCase									=			['''max''']
  
          # In two stage simplex, first minimise then maximise
          if self.n_art_vars:
              self.objectives.append('''min'''  )
  
          __UpperCAmelCase									=			['''''']
  
          # Index of current pivot row and column
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
  
          # Does objective row only contain (non)-negative values?
          __UpperCAmelCase									=			False
  
  
      @staticmethod
      def      lowerCAmelCase_						(*lowercase__  )     ->      list[str]:
          if len(lowercase__  ) != 3:
              raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''  )
  
          if not all(x >= 0 and isinstance(lowercase__     ,      lowercase__  ) for x in args  ):
              raise ValueError('''All arguments must be non-negative integers'''  )
  
          # decision | slack | artificial
          __UpperCAmelCase									=			['''x''', '''s''', '''a''']
          __UpperCAmelCase									=			[]
          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      lowerCAmelCase_						(self     ,      lowercase__  )     ->      tuple[Any, Any]:
          __UpperCAmelCase									=			self.objectives[-1]
  
          # Find entries of highest magnitude in objective rows
          __UpperCAmelCase									=			(objective == '''min''') - (objective == '''max''')
          __UpperCAmelCase									=			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:
              __UpperCAmelCase									=			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
          __UpperCAmelCase									=			slice(self.n_stages     ,      self.n_rows  )
  
          # RHS
          __UpperCAmelCase									=			tableau[s, -1]
  
          # Elements of pivot column within slice
          __UpperCAmelCase									=			tableau[s, col_idx]
  
          # Array filled with nans
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			np.divide(lowercase__     ,      lowercase__     ,      out=lowercase__     ,      where=divisor > 0  )
  
          # Arg of minimum quotient excluding the nan values. n_stages is added
          # to compensate for earlier exclusion of objective columns
          __UpperCAmelCase									=			np.nanargmin(lowercase__  ) + self.n_stages
          return row_idx, col_idx
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      np.ndarray:
          __UpperCAmelCase									=			tableau[row_idx].copy()
  
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			piv_row
          return tableau
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      np.ndarray:
          self.objectives.pop()
  
          if not self.objectives:
              return tableau
  
          # Slice containing ids for artificial columns
          __UpperCAmelCase									=			slice(-self.n_art_vars - 1     ,      -1  )
  
          # Delete the artificial variable columns
          __UpperCAmelCase									=			np.delete(lowercase__     ,      lowercase__     ,      axis=1  )
  
          # Delete the objective row of the first stage
          __UpperCAmelCase									=			np.delete(lowercase__     ,      0     ,      axis=0  )
  
          __UpperCAmelCase									=			1
          self.n_rows -= 1
          __UpperCAmelCase									=			0
          __UpperCAmelCase									=			False
          return tableau
  
  
      def      lowerCAmelCase_						(self  )     ->      dict[Any, Any]:
          # Stop simplex algorithm from cycling.
          for _ in range(100  ):
              # Completion of each stage removes an objective. If both stages
              # are complete, then no objectives are left
              if not self.objectives:
                  __UpperCAmelCase									=			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  )
  
              __UpperCAmelCase					,     __UpperCAmelCase									=			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
                  __UpperCAmelCase									=			self.change_stage(self.tableau  )
              else:
                  __UpperCAmelCase									=			self.pivot(self.tableau     ,      lowercase__     ,      lowercase__  )
          return {}
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      dict[str, float]:
          __UpperCAmelCase									=			{'''P''': abs(tableau[0, -1]  )}
  
          for i in range(self.n_vars  ):
              # Gives ids of nonzero entries in the ith column
              __UpperCAmelCase									=			np.nonzero(tableau[:, i]  )
              __UpperCAmelCase									=			len(nonzero[0]  )
  
              # First entry in the nonzero ids
              __UpperCAmelCase									=			nonzero[0][0]
              __UpperCAmelCase									=			tableau[nonzero_rowidx, i]
  
              # If there is only one nonzero value in column, which is one
              if n_nonzero == nonzero_val == 1:
                  __UpperCAmelCase									=			tableau[nonzero_rowidx, -1]
                  __UpperCAmelCase									=			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(lowercase__     ,      0  )
          return output_dict
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
  
  
  
  
style_context: 
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  A_		:			int										=   logging.get_logger(__name__)
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       "speech-encoder-decoder"
      a__     							=       True
  
  
      def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
          super().__init__(**lowercase__  )
          if "encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError(
                  F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                  F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )
  
          __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
          __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
          __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
          __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )
  
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			True
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
  
          return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
          __UpperCAmelCase									=			self.encoder.to_dict()
          __UpperCAmelCase									=			self.decoder.to_dict()
          __UpperCAmelCase									=			self.__class__.model_type
          return output
  
  
  
  
  