



from typing import Any

import numpy as np

class 	__snake_case :







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_						):
											# Check if RHS is negative
											if np.any(tableau[:, -1]					,					where=tableau[:, -1] < 0						):
															raise ValueError("""RHS must be > 0"""						)

											a__			 =						tableau
											a__ ,     a__			 =						tableau.shape

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

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

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

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

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

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

											a__			 =						[""""""]

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

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


							@staticmethod
							def _a (							*a_						):
											if len(a_						) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

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

											# decision | slack | artificial
											a__			 =						["""x""", """s""", """a"""]
											a__			 =						[]
											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					,					a_						):
											a__			 =						self.objectives[-1]

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

											# RHS
											a__			 =						tableau[s, -1]

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

											# Array filled with nans
											a__			 =						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__			 =						np.divide(a_					,					a_					,					out=a_					,					where=divisor > 0						)

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


							def _a (							self					,					a_					,					a_					,					a_						):
											a__			 =						tableau[row_idx].copy()

											a__			 =						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__			 =						piv_row
											return tableau


							def _a (							self					,					a_						):
											self.objectives.pop()

											if not self.objectives:
															return tableau

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

											# Delete the artificial variable columns
											a__			 =						np.delete(a_					,					a_					,					axis=1						)

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

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


							def _a (							self						):
											# 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:
																			a__			 =						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__			 =						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__			 =						self.change_stage(self.tableau						)
															else:
																			a__			 =						self.pivot(self.tableau					,					a_					,					a_						)
											return {}







							def _a (							self					,					a_					,					a_						):
											a__			 =						{"""P""": abs(tableau[0, -1]						)}

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

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

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																			a__			 =						tableau[nonzero_rowidx, -1]
																			a__			 =						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(a_					,					0						)
											return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()






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


UpperCAmelCase								=   logging.get_logger(__name__)

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

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

