
from typing import Any

import numpy as np







class 	A_       :




	'''simple docstring'''


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

		lowercase					  =   tableau
		lowercase  , lowercase					  =   tableau.shape

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

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

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

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

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

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

		lowercase					  =   ['']

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

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





	@staticmethod
	def      SCREAMING_SNAKE_CASE__						(	*snake_case	):
		if len(snake_case	) != 3:
			raise ValueError('Must provide n_vars, n_slack, and n_art_vars'	)

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

		# decision | slack | artificial
		lowercase					  =   ['x', 's', 'a']
		lowercase					  =   []
		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		,   snake_case	):
		lowercase					  =   self.objectives[-1]

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

		# RHS
		lowercase					  =   tableau[s, -1]

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

		# Array filled with nans
		lowercase					  =   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
		lowercase					  =   np.divide(snake_case		,   snake_case		,   out=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
		lowercase					  =   np.nanargmin(snake_case	) + self.n_stages
		return row_idx, col_idx





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   tableau[row_idx].copy()

		lowercase					  =   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
		lowercase					  =   piv_row
		return tableau





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		self.objectives.pop()

		if not self.objectives:
			return tableau

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

		# Delete the artificial variable columns
		lowercase					  =   np.delete(snake_case		,   snake_case		,   axis=1	)

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

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





	def      SCREAMING_SNAKE_CASE__						(	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:
				lowercase					  =   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	)

			lowercase  , lowercase					  =   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
				lowercase					  =   self.change_stage(self.tableau	)
			else:
				lowercase					  =   self.pivot(self.tableau		,   snake_case		,   snake_case	)
		return {}





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		lowercase					  =   {'P': abs(tableau[0, -1]	)}

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

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

