code: 
  
  
  
  
  import os
  import pathlib
  import tempfile
  import uuid
  
  import numpy as np
  
  from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  if is_vision_available():
  							import PIL.Image
  							from PIL import Image
  							from PIL.Image import Image as ImageType
  else:
  							_A							    =	object
  
  if is_torch_available():
  							import torch
  
  if is_soundfile_availble():
  							import soundfile as sf
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  
  
  
  
  				def __str__( self							)    ->      str:
  											return self.to_string()
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											logger.error(
  											    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''							)
  											return self._value
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      str:
  											logger.error(
  											    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''							)
  											return str(self._value							)
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   UpperCamelCase__	):
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											return self._value
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											return self._value
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      str:
  											super().__init__(snake_case_							)
  
  											if not is_vision_available():
  																		raise ImportError('''PIL must be installed in order to handle images.'''							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: str	     =None
  
  											if isinstance(snake_case_		,		snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Any	     =value
  											elif isinstance(snake_case_		,		(str, pathlib.Path)							):
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =value
  											elif isinstance(snake_case_		,		torch.Tensor							):
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  											else:
  																		raise ValueError(F'Unsupported type for {self.__class__.__name__}: {type(snake_case_							)}'							)
  
  
  
  
  				def     __a		( self		,		snake_case_=None		,		snake_case_=None							)    ->      Union[str, Any]:
  											from IPython.display import Image, display
  
  											display(Image(self.to_string()							)							)
  
  
  
  
  				def     __a		( self							)    ->      int:
  											if self._raw is not None:
  																		return self._raw
  
  											if self._path is not None:
  																		SCREAMING_SNAKE_CASE			: Dict	     =Image.open(self._path							)
  																		return self._raw
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											if self._path is not None:
  																		return self._path
  
  											if self._raw is not None:
  																		SCREAMING_SNAKE_CASE			: Dict	     =tempfile.mkdtemp()
  																		SCREAMING_SNAKE_CASE			: Any	     =os.path.join(snake_case_		,		str(uuid.uuida()							) + '''.png'''							)
  																		self._raw.save(self._path							)
  
  																		return self._path
  
  											if self._tensor is not None:
  																		SCREAMING_SNAKE_CASE			: int	     =self._tensor.cpu().detach().numpy()
  
  																		# There is likely simpler than load into image into save
  																		SCREAMING_SNAKE_CASE			: Any	     =Image.fromarray((array * 255).astype(np.uinta							)							)
  
  																		SCREAMING_SNAKE_CASE			: str	     =tempfile.mkdtemp()
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(snake_case_		,		str(uuid.uuida()							) + '''.png'''							)
  
  																		img.save(self._path							)
  
  																		return self._path
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_=16_000							)    ->      int:
  											super().__init__(snake_case_							)
  
  											if not is_soundfile_availble():
  																		raise ImportError('''soundfile must be installed in order to handle audio.'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											SCREAMING_SNAKE_CASE			: int	     =samplerate
  
  											if isinstance(snake_case_		,		(str, pathlib.Path)							):
  																		SCREAMING_SNAKE_CASE			: Tuple	     =value
  											elif isinstance(snake_case_		,		torch.Tensor							):
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  											else:
  																		raise ValueError(F'Unsupported audio type: {type(snake_case_							)}'							)
  
  
  
  
  				def     __a		( self		,		snake_case_=None		,		snake_case_=None							)    ->      Union[str, Any]:
  											from IPython.display import Audio, display
  
  											display(Audio(self.to_string()		,		rate=self.samplerate							)							)
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											if self._tensor is not None:
  																		return self._tensor
  
  											if self._path is not None:
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: str	     =sf.read(self._path							)
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor(snake_case_							)
  																		return self._tensor
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      str:
  											if self._path is not None:
  																		return self._path
  
  											if self._tensor is not None:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =tempfile.mkdtemp()
  																		SCREAMING_SNAKE_CASE			: Tuple	     =os.path.join(snake_case_		,		str(uuid.uuida()							) + '''.wav'''							)
  																		sf.write(self._path		,		self._tensor		,		samplerate=self.samplerate							)
  																		return self._path
  
  
  
  
  
  
  
  _A							    =	{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
  _A							    =	{str: AgentText}
  
  if is_vision_available():
  							_A							    =	AgentImage
  def 						lowerCAmelCase_       (	*__a	,	**__a )       ->			List[str]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =[(arg.to_raw() if isinstance(__a	,	__a ) else arg) for arg in args]
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={k: (v.to_raw() if isinstance(__a	,	__a ) else v) for k, v in kwargs.items()}
  							return args, kwargs
  def 						lowerCAmelCase_       (	__a	,	__a=None )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if isinstance(__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     ={}
  														for i, (k, v) in enumerate(outputs.items() ):
  																					if output_types is not None:
  																												# If the class has defined outputs, we can map directly according to the class definition
  																												if output_types[i] in AGENT_TYPE_MAPPING:
  																																			SCREAMING_SNAKE_CASE			: int	     =AGENT_TYPE_MAPPING[output_types[i]](__a )
  																												else:
  																																			SCREAMING_SNAKE_CASE			: List[Any]	     =AgentType(__a )
  
  																					else:
  																												# If the class does not have defined output, then we map according to the type
  																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
  																																			if isinstance(__a	,	_k ):
  																																										SCREAMING_SNAKE_CASE			: Union[str, Any]	     =_v(__a )
  																												if k not in decoded_outputs:
  																																			SCREAMING_SNAKE_CASE			: str	     =AgentType[v]
  
  							elif isinstance(__a	,	(list, tuple) ):
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =type(__a )()
  														for i, v in enumerate(__a ):
  																					if output_types is not None:
  																												# If the class has defined outputs, we can map directly according to the class definition
  																												if output_types[i] in AGENT_TYPE_MAPPING:
  																																			decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__a ) )
  																												else:
  																																			decoded_outputs.append(AgentType(__a ) )
  																					else:
  																												# If the class does not have defined output, then we map according to the type
  																												SCREAMING_SNAKE_CASE			: List[Any]	     =False
  																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
  																																			if isinstance(__a	,	_k ):
  																																										decoded_outputs.append(_v(__a ) )
  																																										SCREAMING_SNAKE_CASE			: List[Any]	     =True
  
  																												if not found:
  																																			decoded_outputs.append(AgentType(__a ) )
  
  							else:
  														if output_types[0] in AGENT_TYPE_MAPPING:
  																					# If the class has defined outputs, we can map directly according to the class definition
  																					SCREAMING_SNAKE_CASE			: Any	     =AGENT_TYPE_MAPPING[output_types[0]](__a )
  
  														else:
  																					# If the class does not have defined output, then we map according to the type
  																					for _k, _v in INSTANCE_TYPE_MAPPING.items():
  																												if isinstance(__a	,	_k ):
  																																			return _v(__a )
  																					return AgentType(__a )
  
  							return decoded_outputs
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_							)    ->      None:
  											# Check if RHS is negative
  											if np.any(tableau[:, -1]		,		where=tableau[:, -1] < 0							):
  																		raise ValueError('''RHS must be > 0'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape
  
  											# Number of decision variables x1, x2, x3...
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars
  
  											# Number of artificial variables to be minimised
  											SCREAMING_SNAKE_CASE			: Union[str, 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			: str	     =['''max''']
  
  											# In two stage simplex, first minimise then maximise
  											if self.n_art_vars:
  																		self.objectives.append('''min'''							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =['''''']
  
  											# Index of current pivot row and column
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											# Does objective row only contain (non)-negative values?
  											SCREAMING_SNAKE_CASE			: Dict	     =False
  
  
  
  
  				@staticmethod
  				def     __a		( *snake_case_							)    ->      list[str]:
  											if len(snake_case_							) != 3:
  																		raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''							)
  
  											if not all(x >= 0 and isinstance(snake_case_		,		snake_case_							) for x in args							):
  																		raise ValueError('''All arguments must be non-negative integers'''							)
  
  											# decision | slack | artificial
  											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
  											SCREAMING_SNAKE_CASE			: 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     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]
  
  											# Find entries of highest magnitude in objective rows
  											SCREAMING_SNAKE_CASE			: str	     =(objective == '''min''') - (objective == '''max''')
  											SCREAMING_SNAKE_CASE			: Union[str, 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:
  																		SCREAMING_SNAKE_CASE			: List[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			: Tuple	     =slice(self.n_stages		,		self.n_rows							)
  
  											# RHS
  											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]
  
  											# Elements of pivot column within slice
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[s, col_idx]
  
  											# Array filled with nans
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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			: Optional[Any]	     =np.divide(snake_case_		,		snake_case_		,		out=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			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
  											return row_idx, col_idx
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      np.ndarray:
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[row_idx].copy()
  
  											SCREAMING_SNAKE_CASE			: Optional[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
  											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
  											return tableau
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
  											self.objectives.pop()
  
  											if not self.objectives:
  																		return tableau
  
  											# Slice containing ids for artificial columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =slice(-self.n_art_vars - 1		,		-1							)
  
  											# Delete the artificial variable columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)
  
  											# Delete the objective row of the first stage
  											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =1
  											self.n_rows -= 1
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  											SCREAMING_SNAKE_CASE			: List[str]	     =False
  											return tableau
  
  
  
  
  				def     __a		( 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:
  																									SCREAMING_SNAKE_CASE			: int	     =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			: Any	     =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			: Optional[int]	     =self.change_stage(self.tableau							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
  											return {}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      dict[str, float]:
  											SCREAMING_SNAKE_CASE			: Any	     ={'''P''': abs(tableau[0, -1]							)}
  
  											for i in range(self.n_vars							):
  																		# Gives ids of nonzero entries in the ith column
  																		SCREAMING_SNAKE_CASE			: Any	     =np.nonzero(tableau[:, i]							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(nonzero[0]							)
  
  																		# First entry in the nonzero ids
  																		SCREAMING_SNAKE_CASE			: List[str]	     =nonzero[0][0]
  																		SCREAMING_SNAKE_CASE			: 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			: Union[str, Any]	     =tableau[nonzero_rowidx, -1]
  																									SCREAMING_SNAKE_CASE			: Any	     =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(snake_case_		,		0							)
  											return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  
  
  