





from typing import Any

import numpy as np



class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    List[str]					, UpperCamelCase					:    np.ndarray					, UpperCamelCase					:    int							):


    '''simple docstring'''





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

    _snake_case      : List[Any]														=		tableau
    _snake_case							,    _snake_case      : Optional[int]														=		tableau.shape

    # Number of decision variables x1, x2, x3...
    _snake_case      : Optional[int]														=		n_vars

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

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

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

    # Objectives for each stage
    _snake_case      : Optional[int]														=		['max']

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

    _snake_case      : Dict														=		['']

    # Index of current pivot row and column
    _snake_case      : Dict														=		None
    _snake_case      : Tuple														=		None

    # Does objective row only contain (non)-negative values?
    _snake_case      : Optional[int]														=		False







  @staticmethod
  def UpperCamelCase_       (					*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
    _snake_case      : Optional[int]														=		['x', 's', 'a']
    _snake_case      : Optional[Any]														=		[]
    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 UpperCamelCase_       (					self					:    Optional[int]					, UpperCamelCase					:    np.ndarray							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    _snake_case      : List[Any]														=		(objective == 'min') - (objective == 'max')
    _snake_case      : Optional[Any]														=		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      : List[str]														=		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      : str														=		slice(self.n_stages					, self.n_rows							)

    # RHS
    _snake_case      : Any														=		tableau[s, -1]

    # Elements of pivot column within slice
    _snake_case      : Any														=		tableau[s, col_idx]

    # Array filled with nans
    _snake_case      : Optional[int]														=		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      : str														=		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
    _snake_case      : Tuple														=		np.nanargmin(UpperCamelCase							) + self.n_stages
    return row_idx, col_idx







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    np.ndarray					, UpperCamelCase					:    int					, UpperCamelCase					:    int							):


    '''simple docstring'''





    _snake_case      : Any														=		tableau[row_idx].copy()

    _snake_case      : Union[str, Any]														=		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      : List[Any]														=		piv_row
    return tableau







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    np.ndarray							):


    '''simple docstring'''





    self.objectives.pop()

    if not self.objectives:
      return tableau

    # Slice containing ids for artificial columns
    _snake_case      : List[str]														=		slice(-self.n_art_vars - 1					, -1							)

    # Delete the artificial variable columns
    _snake_case      : int														=		np.delete(UpperCamelCase					, UpperCamelCase					, axis=1							)

    # Delete the objective row of the first stage
    _snake_case      : List[str]														=		np.delete(UpperCamelCase					, 0					, axis=0							)

    _snake_case      : Dict														=		1
    self.n_rows -= 1
    _snake_case      : Any														=		0
    _snake_case      : Tuple														=		False
    return tableau







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''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:
        _snake_case      : Union[str, Any]														=		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      : str														=		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      : Tuple														=		self.change_stage(self.tableau							)
      else:
        _snake_case      : Union[str, Any]														=		self.pivot(self.tableau					, UpperCamelCase					, UpperCamelCase							)
    return {}







  def UpperCamelCase_       (					self					:    Dict					, UpperCamelCase					:    np.ndarray					, UpperCamelCase					:    list[str]							):


    '''simple docstring'''





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

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

      # First entry in the nonzero ids
      _snake_case      : Union[str, Any]														=		nonzero[0][0]
      _snake_case      : Optional[int]														=		tableau[nonzero_rowidx, i]

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












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


lowercase     :							Any	     =			logging.get_logger(__name__)

lowercase     :							int	     =			{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    'graphormer'
						A   :      List[str]						 =    ['past_key_values']






						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1						, _SCREAMING_SNAKE_CASE = 512 * 9						, _SCREAMING_SNAKE_CASE = 512 * 3						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 128						, _SCREAMING_SNAKE_CASE = 5						, _SCREAMING_SNAKE_CASE = 1024						, _SCREAMING_SNAKE_CASE = "multi_hop"						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 12						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 32						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = "gelu"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = 8						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE=1						, _SCREAMING_SNAKE_CASE=2						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							str         =				num_classes
								snake_case_ :							Union[str, Any]         =				num_atoms
								snake_case_ :							Dict         =				num_in_degree
								snake_case_ :							Union[str, Any]         =				num_out_degree
								snake_case_ :							List[Any]         =				num_edges
								snake_case_ :							Union[str, Any]         =				num_spatial
								snake_case_ :							int         =				num_edge_dis
								snake_case_ :							List[Any]         =				edge_type
								snake_case_ :							Any         =				multi_hop_max_dist
								snake_case_ :							Tuple         =				spatial_pos_max
								snake_case_ :							Union[str, Any]         =				max_nodes
								snake_case_ :							str         =				num_hidden_layers
								snake_case_ :							str         =				embedding_dim
								snake_case_ :							List[str]         =				embedding_dim
								snake_case_ :							Dict         =				ffn_embedding_dim
								snake_case_ :							Any         =				num_attention_heads
								snake_case_ :							List[str]         =				dropout
								snake_case_ :							List[str]         =				attention_dropout
								snake_case_ :							Optional[Any]         =				layerdrop
								snake_case_ :							Optional[int]         =				encoder_normalize_before
								snake_case_ :							Union[str, Any]         =				pre_layernorm
								snake_case_ :							int         =				apply_graphormer_init
								snake_case_ :							List[Any]         =				activation_fn
								snake_case_ :							str         =				embed_scale
								snake_case_ :							int         =				freeze_embeddings
								snake_case_ :							Optional[Any]         =				num_trans_layers_to_freeze
								snake_case_ :							int         =				share_input_output_embed
								snake_case_ :							int         =				traceable
								snake_case_ :							Tuple         =				q_noise
								snake_case_ :							Tuple         =				qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								snake_case_ :							List[Any]         =				kdim
								snake_case_ :							Tuple         =				vdim
								snake_case_ :							Any         =				self_attention
								snake_case_ :							Any         =				bias

								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE						, bos_token_id=_SCREAMING_SNAKE_CASE						, eos_token_id=_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE						, )
