



'''simple docstring'''







from typing import Any

import numpy as np




class 				__lowerCAmelCase:






			def __init__(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:Dict										=      tableau
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      tableau.shape

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

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

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

					# Number of slack variables added to make inequalities into equalities
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.n_rows - self.n_stages

					# Objectives for each stage
					SCREAMING_SNAKE_CASE_						:Dict										=      ['max']

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

					SCREAMING_SNAKE_CASE_						:Any										=      ['']

					# Index of current pivot row and column
					SCREAMING_SNAKE_CASE_						:Dict										=      None
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      None

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






			@staticmethod
			def 				_lowercase	(   *SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					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
					SCREAMING_SNAKE_CASE_						:int										=      ['x', 's', 'a']
					SCREAMING_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 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.objectives[-1]

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

					# RHS
					SCREAMING_SNAKE_CASE_						:Tuple										=      tableau[s, -1]

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

					# Array filled with nans
					SCREAMING_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
					SCREAMING_SNAKE_CASE_						:int										=      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
					SCREAMING_SNAKE_CASE_						:Dict										=      np.nanargmin(SCREAMING_SNAKE_CASE__	) + self.n_stages
					return row_idx, col_idx






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:int										=      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
					SCREAMING_SNAKE_CASE_						:str										=      piv_row
					return tableau






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					self.objectives.pop()

					if not self.objectives:
							return tableau

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

					# Delete the artificial variable columns
					SCREAMING_SNAKE_CASE_						:str										=      np.delete(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       axis=1	)

					# Delete the objective row of the first stage
					SCREAMING_SNAKE_CASE_						:int										=      np.delete(SCREAMING_SNAKE_CASE__      ,       0      ,       axis=0	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      1
					self.n_rows -= 1
					SCREAMING_SNAKE_CASE_						:str										=      0
					SCREAMING_SNAKE_CASE_						:str										=      False
					return tableau






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					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:
									SCREAMING_SNAKE_CASE_						:Tuple										=      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	)

							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						: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
									SCREAMING_SNAKE_CASE_						:Dict										=      self.change_stage(self.tableau	)
							else:
									SCREAMING_SNAKE_CASE_						:str										=      self.pivot(self.tableau      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__	)
					return {}






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







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

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

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

							# If there is only one nonzero value in column, which is one
							if n_nonzero == nonzero_val == 1:
									SCREAMING_SNAKE_CASE_						:Tuple										=      tableau[nonzero_rowidx, -1]
									SCREAMING_SNAKE_CASE_						:List[str]										=      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"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




