



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    {
				    """sld_warmup_steps""": 15,
				    """sld_guidance_scale""": 20,
				    """sld_threshold""": 0.0,
				    """sld_momentum_scale""": 0.0,
				    """sld_mom_beta""": 0.0,
				}
				lowercase__     =    {
				    """sld_warmup_steps""": 10,
				    """sld_guidance_scale""": 10_00,
				    """sld_threshold""": 0.0_1,
				    """sld_momentum_scale""": 0.3,
				    """sld_mom_beta""": 0.4,
				}
				lowercase__     =    {
				    """sld_warmup_steps""": 7,
				    """sld_guidance_scale""": 20_00,
				    """sld_threshold""": 0.0_2_5,
				    """sld_momentum_scale""": 0.5,
				    """sld_mom_beta""": 0.7,
				}
				lowercase__     =    {
				    """sld_warmup_steps""": 0,
				    """sld_guidance_scale""": 50_00,
				    """sld_threshold""": 1.0,
				    """sld_momentum_scale""": 0.5,
				    """sld_mom_beta""": 0.7,
				}




@dataclass
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker



from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





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

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

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

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

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

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

						# Objectives for each stage
						__magic_name__				=     ['max']

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

						__magic_name__				=     ['']

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

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







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





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

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

						# decision | slack | artificial
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						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:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

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

						# RHS
						__magic_name__				=     tableau[s, -1]

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

						# Array filled with nans
						__magic_name__				=     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
						__magic_name__				=     np.divide(__UpperCamelCase	,			__UpperCamelCase	,			out=__UpperCamelCase	,			where=divisor > 0   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     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
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

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

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     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   )

											__magic_name__,						__magic_name__				=     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
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

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

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

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()




