




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












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()






