




'''simple docstring'''





from typing import Any

import numpy as np

class      _a    :






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

          _snake_case							=   tableau
          _snake_case							,      _snake_case							=   tableau.shape

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

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

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

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

          # Objectives for each stage
          _snake_case							=   ["max"]

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

          _snake_case							=   [""]

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

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






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

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

          # decision | slack | artificial
          _snake_case							=   ["x", "s", "a"]
          _snake_case							=   []
          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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    tuple[Any, Any]:
          _snake_case							=   self.objectives[-1]

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

          # RHS
          _snake_case							=   tableau[s, -1]

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

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    np.ndarray:
          _snake_case							=   tableau[row_idx].copy()

          _snake_case							=   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							=   piv_row
          return tableau






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    np.ndarray:
          self.objectives.pop()

          if not self.objectives:
               return tableau

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

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

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

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






     def        _lowercase			(				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:
                    _snake_case							=   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							=   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							=   self.change_stage(self.tableau					)
               else:
                    _snake_case							=   self.pivot(self.tableau      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)
          return {}






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    dict[str, float]:
          _snake_case							=   {"P": abs(tableau[0, -1]					)}

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

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

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''simple docstring'''





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class      _a       (		enum.Enum							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""timm"""
     SCREAMING_SNAKE_CASE_							:					int					       =		"""transformers"""







def 			__a			(     _UpperCamelCase:					Optional[Iterable[str]]      ,		_UpperCamelCase:					Optional[Iterable[int]]      ,		_UpperCamelCase:					Optional[Iterable[str]]					)		->				Union[str, Any]:

     """simple docstring"""




     if stage_names is None:
          raise ValueError("Stage_names must be set for transformers backbones"					)

     if out_features is not None:
          if not isinstance(_UpperCamelCase      ,		(list,)					):
               raise ValueError(F"""out_features must be a list {type(_UpperCamelCase					)}"""					)
          if any(feat not in stage_names for feat in out_features					):
               raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""					)

     if out_indices is not None:
          if not isinstance(_UpperCamelCase      ,		(list, tuple)					):
               raise ValueError(F"""out_indices must be a list or tuple, got {type(_UpperCamelCase					)}"""					)
          if any(idx >= len(_UpperCamelCase					) for idx in out_indices					):
               raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

     if out_features is not None and out_indices is not None:
          if len(_UpperCamelCase					) != len(_UpperCamelCase					):
               raise ValueError("out_features and out_indices should have the same length if both are set"					)
          if out_features != [stage_names[idx] for idx in out_indices]:
               raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Any:

     """simple docstring"""




     if out_indices is None and out_features is None:
          _snake_case							=   [len(_UpperCamelCase					) - 1]
          _snake_case							=   [stage_names[-1]]
     elif out_indices is None and out_features is not None:
          _snake_case							=   [stage_names.index(_UpperCamelCase					) for layer in out_features]
     elif out_features is None and out_indices is not None:
          _snake_case							=   [stage_names[idx] for idx in out_indices]
     return out_features, out_indices





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Tuple[List[str], List[int]]:

     """simple docstring"""




     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     _snake_case							,      _snake_case							=   _align_output_features_output_indices(
         out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     return output_features, output_indices

class      _a       :
     SCREAMING_SNAKE_CASE_							:					Optional[BackboneType]					       =		None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          if getattr(self      ,"_backbone"      ,_SCREAMING_SNAKE_CASE					) is None:
               raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

          # These will diagree with the defaults for the transformers models e.g. for resnet50
          # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
          # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
          _snake_case							=   [stage["module"] for stage in self._backbone.feature_info.info]
          _snake_case							=   [stage["num_chs"] for stage in self._backbone.feature_info.info]
          _snake_case							=   self._backbone.feature_info.out_indices
          _snake_case							=   self._backbone.feature_info.module_name()

          # We verify the out indices and out features are valid
          verify_out_features_out_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)
          _snake_case							,      _snake_case							=   out_features, out_indices






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"stage_names"					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_features"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_indices"      ,_SCREAMING_SNAKE_CASE					)

          _snake_case							=   stage_names
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=_SCREAMING_SNAKE_CASE					)
          # Number of channels for each stage. This is set in the transformer backbone model init
          _snake_case							=   None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   config

          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"use_timm_backbone"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

          if self.backbone_type == BackboneType.TIMM:
               self._init_timm_backbone(_SCREAMING_SNAKE_CASE					)
          elif self.backbone_type == BackboneType.TRANSFORMERS:
               self._init_transformers_backbone(_SCREAMING_SNAKE_CASE					)
          else:
               raise ValueError(f"""backbone_type {self.backbone_type} not supported."""					)






     @property
     def        _lowercase			(				self					)							->    int:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    List[str]:
          # the current backbones will output the number of channels for each stage
          # even if that stage is not in the out_features list.
          return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






     @property
     def        _lowercase			(				self					)							->    Any:
          return [self.out_feature_channels[name] for name in self.out_features]






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   dict(inspect.signature(self.forward					).parameters					)
          _snake_case							=   {k: v for k, v in kwargs.items() if k in signature}
          return self(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,)							->    Any:
          raise NotImplementedError("This method should be implemented by the derived class."					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output







class      _a       :






     @property
     def        _lowercase			(				self					)							->    List[str]:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output


