





"""simple docstring"""


from typing import Any

import numpy as np



class      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

        snake_case__    							=				tableau
        snake_case__							,   snake_case__    							=				tableau.shape

        # Number of decision variables x1, x2, x3...
        snake_case__    							=				n_vars

        # Number of artificial variables to be minimised
        snake_case__    							=				len(np.where(tableau[self.n_vars : -1] == -1     )[0]     )

        # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
        snake_case__    							=				(self.n_art_vars > 0) + 1

        # Number of slack variables added to make inequalities into equalities
        snake_case__    							=				self.n_rows - self.n_stages

        # Objectives for each stage
        snake_case__    							=				["max"]

        # In two stage simplex, first minimise then maximise
        if self.n_art_vars:
         self.objectives.append("min"     )

        snake_case__    							=				[""]

        # Index of current pivot row and column
        snake_case__    							=				None
        snake_case__    							=				None

        # Does objective row only contain (non)-negative values?
        snake_case__    							=				False

       @staticmethod
       def 		_SCREAMING_SNAKE_CASE(			*__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        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
        snake_case__    							=				["x", "s", "a"]
        snake_case__    							=				[]
        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 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				self.objectives[-1]

        # Find entries of highest magnitude in objective rows
        snake_case__    							=				(objective == "min") - (objective == "max")
        snake_case__    							=				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:
         snake_case__    							=				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
        snake_case__    							=				slice(self.n_stages   ,					self.n_rows     )

        # RHS
        snake_case__    							=				tableau[s, -1]

        # Elements of pivot column within slice
        snake_case__    							=				tableau[s, col_idx]

        # Array filled with nans
        snake_case__    							=				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
        snake_case__    							=				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
        snake_case__    							=				np.nanargmin(__SCREAMING_SNAKE_CASE     ) + self.n_stages
        return row_idx, col_idx

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				tableau[row_idx].copy()

        snake_case__    							=				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
        snake_case__    							=				piv_row
        return tableau

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        self.objectives.pop()

        if not self.objectives:
         return tableau

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

        # Delete the artificial variable columns
        snake_case__    							=				np.delete(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					axis=1     )

        # Delete the objective row of the first stage
        snake_case__    							=				np.delete(__SCREAMING_SNAKE_CASE   ,					0   ,					axis=0     )

        snake_case__    							=				1
        self.n_rows -= 1
        snake_case__    							=				0
        snake_case__    							=				False
        return tableau

       def 		_SCREAMING_SNAKE_CASE(			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:
          snake_case__    							=				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     )

         snake_case__							,   snake_case__    							=				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
          snake_case__    							=				self.change_stage(self.tableau     )
         else:
          snake_case__    							=				self.pivot(self.tableau   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        return {}

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

        for i in range(self.n_vars     ):
         # Gives ids of nonzero entries in the ith column
         snake_case__    							=				np.nonzero(tableau[:, i]     )
         snake_case__    							=				len(nonzero[0]     )

         # First entry in the nonzero ids
         snake_case__    							=				nonzero[0][0]
         snake_case__    							=				tableau[nonzero_rowidx, i]

         # If there is only one nonzero value in column, which is one
         if n_nonzero == nonzero_val == 1:
          snake_case__    							=				tableau[nonzero_rowidx, -1]
          snake_case__    							=				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()








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

