



'''simple docstring'''





from typing import Any

import numpy as np


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    None:
    # Check if RHS is negative
    if np.any(tableau[:, -1]					,    where=tableau[:, -1] < 0  ):
       raise ValueError("""RHS must be > 0"""  )

    A_  :		str						=					tableau
    A_						,  A_  :		List[str]						=					tableau.shape

    # Number of decision variables x1, x2, x3...
    A_  :		Tuple						=					n_vars

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

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    A_  :		Optional[Any]						=					(self.n_art_vars > 0) + 1

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

    # Objectives for each stage
    A_  :		List[str]						=					["""max"""]

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

    A_  :		Dict						=					[""""""]

    # Index of current pivot row and column
    A_  :		str						=					None
    A_  :		List[Any]						=					None

    # Does objective row only contain (non)-negative values?
    A_  :		Any						=					False







 @staticmethod
 def       UpperCAmelCase_       (      *_lowerCamelCase  )    ->    list[str]:
    if len(_lowerCamelCase  ) != 3:
       raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""  )

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

    # decision | slack | artificial
    A_  :		List[Any]						=					["""x""", """s""", """a"""]
    A_  :		Optional[int]						=					[]
    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					,    _lowerCamelCase  )    ->    tuple[Any, Any]:
    A_  :		Optional[Any]						=					self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    A_  :		str						=					(objective == """min""") - (objective == """max""")
    A_  :		List[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:
       A_  :		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
    A_  :		Tuple						=					slice(self.n_stages					,    self.n_rows  )

    # RHS
    A_  :		int						=					tableau[s, -1]

    # Elements of pivot column within slice
    A_  :		int						=					tableau[s, col_idx]

    # Array filled with nans
    A_  :		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
    A_  :		Union[str, Any]						=					np.divide(_lowerCamelCase					,    _lowerCamelCase					,    out=_lowerCamelCase					,    where=divisor > 0  )

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    A_  :		List[Any]						=					np.nanargmin(_lowerCamelCase  ) + self.n_stages
    return row_idx, col_idx







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    np.ndarray:
    A_  :		Union[str, Any]						=					tableau[row_idx].copy()

    A_  :		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
    A_  :		str						=					piv_row
    return tableau







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    np.ndarray:
    self.objectives.pop()

    if not self.objectives:
       return tableau

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

    # Delete the artificial variable columns
    A_  :		Optional[Any]						=					np.delete(_lowerCamelCase					,    _lowerCamelCase					,    axis=1  )

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

    A_  :		Optional[int]						=					1
    self.n_rows -= 1
    A_  :		Tuple						=					0
    A_  :		Tuple						=					False
    return tableau







 def       UpperCAmelCase_       (      self  )    ->    dict[Any, Any]:
    # 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_  :		Dict						=					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_  :		Dict						=					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_  :		int						=					self.change_stage(self.tableau  )
       else:
          A_  :		int						=					self.pivot(self.tableau					,    _lowerCamelCase					,    _lowerCamelCase  )
    return {}







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    dict[str, float]:
    A_  :		List[Any]						=					{"""P""": abs(tableau[0, -1]  )}

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

       # First entry in the nonzero ids
       A_  :		List[Any]						=					nonzero[0][0]
       A_  :		List[Any]						=					tableau[nonzero_rowidx, i]

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    :     List[Any]			  =   {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Any			  =   ['LayoutLMTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   [
      'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMForMaskedLM',
      'LayoutLMForSequenceClassification',
      'LayoutLMForTokenClassification',
      'LayoutLMForQuestionAnswering',
      'LayoutLMModel',
      'LayoutLMPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     str			  =   [
      'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLayoutLMForMaskedLM',
      'TFLayoutLMForSequenceClassification',
      'TFLayoutLMForTokenClassification',
      'TFLayoutLMForQuestionAnswering',
      'TFLayoutLMMainLayer',
      'TFLayoutLMModel',
      'TFLayoutLMPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    :     List[str]			  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
