code: def _clean_inputs(
      c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None
  ):
      """
      Given user inputs for a linear programming problem, return the
      objective vector, upper bound constraints, equality constraints,
      and simple bounds in a preferred format.
  
      Parameters
      ----------
      c : 1D array
          Coefficients of the linear objective function to be minimized.
      A_ub : 2D array, optional
          2D array such that ``A_ub @ x`` gives the values of the upper-bound
          inequality constraints at ``x``.
      b_ub : 1D array, optional
          1D array of values representing the upper-bound of each inequality
          constraint (row) in ``A_ub``.
      A_eq : 2D array, optional
          2D array such that ``A_eq @ x`` gives the values of the equality
          constraints at ``x``.
      b_eq : 1D array, optional
          1D array of values representing the RHS of each equality constraint
          (row) in ``A_eq``.
      bounds : sequence, optional
          ``(min, max)`` pairs for each element in ``x``, defining
          the bounds on that parameter. Use None for one of ``min`` or
          ``max`` when there is no bound in that direction. By default
          bounds are ``(0, None)`` (non-negative).
          If a sequence containing a single tuple is provided, then ``min`` and
          ``max`` will be applied to all variables in the problem.
  
      Returns
      -------
      c : 1D array
          Coefficients of the linear objective function to be minimized.
      A_ub : 2D array, optional
          2D array such that ``A_ub @ x`` gives the values of the upper-bound
          inequality constraints at ``x``.
      b_ub : 1D array, optional
          1D array of values representing the upper-bound of each inequality
          constraint (row) in ``A_ub``.
      A_eq : 2D array, optional
          2D array such that ``A_eq @ x`` gives the values of the equality
          constraints at ``x``.
      b_eq : 1D array, optional
          1D array of values representing the RHS of each equality constraint
          (row) in ``A_eq``.
      bounds : sequence of tuples
          ``(min, max)`` pairs for each element in ``x``, defining
          the bounds on that parameter. Use None for each of ``min`` or
          ``max`` when there is no bound in that direction. By default
          bounds are ``(0, None)`` (non-negative).
  
      """
  
      try:
          if c is None:
              raise TypeError
          try:
              c = np.asarray(c, dtype=float).copy().squeeze()
          except (
              BaseException
          ):  # typically a ValueError and shouldn't be, IMO
              raise TypeError
          if c.size == 1:
              c = c.reshape((-1))
          n_x = len(c)
          if n_x == 0 or len(c.shape) != 1:
              raise ValueError(
                  "Invalid input for linprog: c should be a 1D array; it must "
                  "not have more than one non-singleton dimension"
              )
          if not (np.isfinite(c).all()):
              raise ValueError(
                  "Invalid input for linprog: c must not contain values "
                  "inf, nan, or None"
              )
      except TypeError:
          raise TypeError(
              "Invalid input for linprog: c must be a 1D array of numerical "
              "coefficients"
          )
  
      try:
          try:
              if sps.issparse(A_eq) or sps.issparse(A_ub):
                  A_ub = (
                      sps.coo_matrix((0, n_x), dtype=float)
                      if A_ub is None
                      else sps.coo_matrix(A_ub, dtype=float).copy()
                  )
              else:
                  A_ub = (
                      np.zeros((0, n_x), dtype=float)
                      if A_ub is None
                      else np.asarray(A_ub, dtype=float).copy()
                  )
          except BaseException:
              raise TypeError
          n_ub = A_ub.shape[0]
          if len(A_ub.shape) != 2 or A_ub.shape[1] != len(c):
              raise ValueError(
                  "Invalid input for linprog: A_ub must have exactly two "
                  "dimensions, and the number of columns in A_ub must be "
                  "equal to the size of c "
              )
          if (
              sps.issparse(A_ub)
              and not np.isfinite(A_ub.data).all()
              or not sps.issparse(A_ub)
              and not np.isfinite(A_ub).all()
          ):
              raise ValueError(
                  "Invalid input for linprog: A_ub must not contain values "
                  "inf, nan, or None"
              )
      except TypeError:
          raise TypeError(
              "Invalid input for linprog: A_ub must be a numerical 2D array "
              "with each row representing an upper bound inequality constraint"
          )
  
      try:
          try:
              b_ub = (
                  np.array([], dtype=float)
                  if b_ub is None
                  else np.asarray(b_ub, dtype=float).copy().squeeze()
              )
          except BaseException:
              raise TypeError
          if b_ub.size == 1:
              b_ub = b_ub.reshape((-1))
          if len(b_ub.shape) != 1:
              raise ValueError(
                  "Invalid input for linprog: b_ub should be a 1D array; it "
                  "must not have more than one non-singleton dimension"
              )
          if len(b_ub) != n_ub:
              raise ValueError(
                  "Invalid input for linprog: The number of rows in A_ub must "
                  "be equal to the number of values in b_ub"
              )
          if not (np.isfinite(b_ub).all()):
              raise ValueError(
                  "Invalid input for linprog: b_ub must not contain values "
                  "inf, nan, or None"
              )
      except TypeError:
          raise TypeError(
              "Invalid input for linprog: b_ub must be a 1D array of "
              "numerical values, each representing the upper bound of an "
              "inequality constraint (row) in A_ub"
          )
  
      try:
          try:
              if sps.issparse(A_eq) or sps.issparse(A_ub):
                  A_eq = (
                      sps.coo_matrix((0, n_x), dtype=float)
                      if A_eq is None
                      else sps.coo_matrix(A_eq, dtype=float).copy()
                  )
              else:
                  A_eq = (
                      np.zeros((0, n_x), dtype=float)
                      if A_eq is None
                      else np.asarray(A_eq, dtype=float).copy()
                  )
          except BaseException:
              raise TypeError
          n_eq = A_eq.shape[0]
          if len(A_eq.shape) != 2 or A_eq.shape[1] != len(c):
              raise ValueError(
                  "Invalid input for linprog: A_eq must have exactly two "
                  "dimensions, and the number of columns in A_eq must be "
                  "equal to the size of c "
              )
  
          if (
              sps.issparse(A_eq)
              and not np.isfinite(A_eq.data).all()
              or not sps.issparse(A_eq)
              and not np.isfinite(A_eq).all()
          ):
              raise ValueError(
                  "Invalid input for linprog: A_eq must not contain values "
                  "inf, nan, or None"
              )
      except TypeError:
          raise TypeError(
              "Invalid input for linprog: A_eq must be a 2D array with each "
              "row representing an equality constraint"
          )
  
      try:
          try:
              b_eq = (
                  np.array([], dtype=float)
                  if b_eq is None
                  else np.asarray(b_eq, dtype=float).copy().squeeze()
              )
          except BaseException:
              raise TypeError
          if b_eq.size == 1:
              b_eq = b_eq.reshape((-1))
          if len(b_eq.shape) != 1:
              raise ValueError(
                  "Invalid input for linprog: b_eq should be a 1D array; it "
                  "must not have more than one non-singleton dimension"
              )
          if len(b_eq) != n_eq:
              raise ValueError(
                  "Invalid input for linprog: the number of rows in A_eq "
                  "must be equal to the number of values in b_eq"
              )
          if not (np.isfinite(b_eq).all()):
              raise ValueError(
                  "Invalid input for linprog: b_eq must not contain values "
                  "inf, nan, or None"
              )
      except TypeError:
          raise TypeError(
              "Invalid input for linprog: b_eq must be a 1D array of "
              "numerical values, each representing the right hand side of an "
              "equality constraints (row) in A_eq"
          )
  
      # "If a sequence containing a single tuple is provided, then min and max
      # will be applied to all variables in the problem."
      # linprog doesn't treat this right: it didn't accept a list with one tuple
      # in it
      try:
          if isinstance(bounds, str):
              raise TypeError
          if bounds is None or len(bounds) == 0:
              bounds = [(0, None)] * n_x
          elif len(bounds) == 1:
              b = bounds[0]
              if len(b) != 2:
                  raise ValueError(
                      "Invalid input for linprog: exactly one lower bound and "
                      "one upper bound must be specified for each element of x"
                  )
              bounds = [b] * n_x
          elif len(bounds) == n_x:
              try:
                  len(bounds[0])
              except BaseException:
                  bounds = [(bounds[0], bounds[1])] * n_x
              for i, b in enumerate(bounds):
                  if len(b) != 2:
                      raise ValueError(
                          "Invalid input for linprog, bound "
                          + str(i)
                          + " "
                          + str(b)
                          + ": exactly one lower bound and one upper bound must "
                          "be specified for each element of x"
                      )
          elif (
              len(bounds) == 2
              and np.isreal(bounds[0])
              and np.isreal(bounds[1])
          ):
              bounds = [(bounds[0], bounds[1])] * n_x
          else:
              raise ValueError(
                  "Invalid input for linprog: exactly one lower bound and one "
                  "upper bound must be specified for each element of x"
              )
  
          clean_bounds = (
              []
          )  # also creates a copy so user's object isn't changed
          for i, b in enumerate(bounds):
              if b[0] is not None and b[1] is not None and b[0] > b[1]:
                  raise ValueError(
                      "Invalid input for linprog, bound "
                      + str(i)
                      + " "
                      + str(b)
                      + ": a lower bound must be less than or equal to the "
                      "corresponding upper bound"
                  )
              if b[0] == np.inf:
                  raise ValueError(
                      "Invalid input for linprog, bound "
                      + str(i)
                      + " "
                      + str(b)
                      + ": infinity is not a valid lower bound"
                  )
              if b[1] == -np.inf:
                  raise ValueError(
                      "Invalid input for linprog, bound "
                      + str(i)
                      + " "
                      + str(b)
                      + ": negative infinity is not a valid upper bound"
                  )
              lb = (
                  float(b[0])
                  if b[0] is not None and b[0] != -np.inf
                  else None
              )
              ub = (
                  float(b[1])
                  if b[1] is not None and b[1] != np.inf
                  else None
              )
              clean_bounds.append((lb, ub))
          bounds = clean_bounds
      except ValueError as e:
          if "could not convert string to float" in e.args[0]:
              raise TypeError
          else:
              raise e
      except TypeError as e:
          print(e)
          raise TypeError(
              "Invalid input for linprog: bounds must be a sequence of "
              "(min,max) pairs, each defining bounds on an element of x "
          )
  
      return c, A_ub, b_ub, A_eq, b_eq, bounds
  
file_path: catboost/contrib/python/scipy/py2/scipy/optimize/_linprog_util.py
imports: import numpy
  import scipy.sparse
  from warnings import warn
  from .optimize import OptimizeWarning
  from scipy.optimize._remove_redundancy import _remove_redundancy, _remove_redundancy_sparse, _remove_redundancy_dense
label_desc: Use image node_id 2 for calling a global function with example usage: _clean_inputs(c, A_ub, b_ub, A_eq, b_eq, bounds) and returns: c,  A_ub,  b_ub,  A_eq,  b_eq,  bounds