

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()







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




