code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from typing import Any
  
  import numpy as np
  class 						__lowerCAmelCase   :
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Dict				,			UpperCamelCase_:		np.ndarray				,			UpperCamelCase_:		int					):
       # Check if RHS is negative
       if np.any(tableau[:, -1]				,			where=tableau[:, -1] < 0					):
          raise ValueError("RHS must be > 0"					)
  
       UpperCamelCase_     =tableau
       UpperCamelCase_    ,							UpperCamelCase_     =tableau.shape
  
       # Number of decision variables x1, x2, x3...
       UpperCamelCase_     =n_vars
  
       # Number of artificial variables to be minimised
       UpperCamelCase_     =len(np.where(tableau[self.n_vars : -1] == -1					)[0]					)
  
       # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
       UpperCamelCase_     =(self.n_art_vars > 0) + 1
  
       # Number of slack variables added to make inequalities into equalities
       UpperCamelCase_     =self.n_rows - self.n_stages
  
       # Objectives for each stage
       UpperCamelCase_     =["max"]
  
       # In two stage simplex, first minimise then maximise
       if self.n_art_vars:
          self.objectives.append("min"					)
  
       UpperCamelCase_     =[""]
  
       # Index of current pivot row and column
       UpperCamelCase_     =None
       UpperCamelCase_     =None
  
       # Does objective row only contain (non)-negative values?
       UpperCamelCase_     =False
    @staticmethod
    def 					UpperCamelCase__   (      *UpperCamelCase_:		int					):
       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
       UpperCamelCase_     =["x", "s", "a"]
       UpperCamelCase_     =[]
       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:		Tuple				,			UpperCamelCase_:		np.ndarray					):
       UpperCamelCase_     =self.objectives[-1]
  
       # Find entries of highest magnitude in objective rows
       UpperCamelCase_     =(objective == "min") - (objective == "max")
       UpperCamelCase_     =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:
          UpperCamelCase_     =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
       UpperCamelCase_     =slice(self.n_stages				,			self.n_rows					)
  
       # RHS
       UpperCamelCase_     =tableau[s, -1]
  
       # Elements of pivot column within slice
       UpperCamelCase_     =tableau[s, col_idx]
  
       # Array filled with nans
       UpperCamelCase_     =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
       UpperCamelCase_     =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
       UpperCamelCase_     =np.nanargmin(UpperCamelCase_					) + self.n_stages
       return row_idx, col_idx
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		np.ndarray				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int					):
       UpperCamelCase_     =tableau[row_idx].copy()
  
       UpperCamelCase_     =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
       UpperCamelCase_     =piv_row
       return tableau
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		np.ndarray					):
       self.objectives.pop()
  
       if not self.objectives:
          return tableau
  
       # Slice containing ids for artificial columns
       UpperCamelCase_     =slice(-self.n_art_vars - 1				,			-1					)
  
       # Delete the artificial variable columns
       UpperCamelCase_     =np.delete(UpperCamelCase_				,			UpperCamelCase_				,			axis=1					)
  
       # Delete the objective row of the first stage
       UpperCamelCase_     =np.delete(UpperCamelCase_				,			0				,			axis=0					)
  
       UpperCamelCase_     =1
       self.n_rows -= 1
       UpperCamelCase_     =0
       UpperCamelCase_     =False
       return tableau
    def 					UpperCamelCase__   (      self:		Optional[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:
             UpperCamelCase_     =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					)
  
          UpperCamelCase_    ,							UpperCamelCase_     =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
             UpperCamelCase_     =self.change_stage(self.tableau					)
          else:
             UpperCamelCase_     =self.pivot(self.tableau				,			UpperCamelCase_				,			UpperCamelCase_					)
       return {}
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		np.ndarray				,			UpperCamelCase_:		list[str]					):
       UpperCamelCase_     ={"P": abs(tableau[0, -1]					)}
  
       for i in range(self.n_vars					):
          # Gives ids of nonzero entries in the ith column
          UpperCamelCase_     =np.nonzero(tableau[:, i]					)
          UpperCamelCase_     =len(nonzero[0]					)
  
          # First entry in the nonzero ids
          UpperCamelCase_     =nonzero[0][0]
          UpperCamelCase_     =tableau[nonzero_rowidx, i]
  
          # If there is only one nonzero value in column, which is one
          if n_nonzero == nonzero_val == 1:
             UpperCamelCase_     =tableau[nonzero_rowidx, -1]
             UpperCamelCase_     =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()
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
    __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
    __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
       UpperCamelCase_     =None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."				,			UpperCamelCase_				,			)
          UpperCamelCase_     =kwargs.pop("feature_extractor"					)
  
       UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."					)
       if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."					)
  
       super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
       UpperCamelCase_     =self.image_processor
    def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):
  
       if text is None and videos is None:
          raise ValueError("You have to specify either text or videos. Both cannot be none."					)
  
       if text is not None:
          UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if videos is not None:
          UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if text is not None and videos is not None:
          UpperCamelCase_     =image_features.pixel_values
          return encoding
       elif text is not None:
          return encoding
       else:
          return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
       return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
       return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    @property
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
    @property
    def 					UpperCamelCase__   (      self:		str					):
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
       return self.image_processor_class
  
  
  
  
  
    @property
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
       return self.image_processor
  