






"""simple docstring"""





UpperCamelCase					=     float("""inf""")







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_)			->   List[str]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     [0] * n
          lowercase__:							Dict         =     [0] * n
          lowercase__:							List[Any]         =     [0] * n
          lowercase__:							str         =     [[] for _ in range(UpperCAmelCase_)]





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0)			->   Optional[int]:




          '''simple docstring'''




          self.adj[a].append([b, len(self.adj[b]), c, 0])
          self.adj[b].append([a, len(self.adj[a]) - 1, rcap, 0])





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Union[str, Any]:




          '''simple docstring'''




          if vertex == sink or not flow:
                 return flow

          for i in range(self.ptr[vertex]					, len(self.adj[vertex])):
                 lowercase__:							Optional[Any]         =     self.adj[vertex][i]
                 if self.lvl[e[0]] == self.lvl[vertex] + 1:
                        lowercase__:							Optional[int]         =     self.depth_first_search(e[0]					, UpperCAmelCase_					, min(UpperCAmelCase_					, e[2] - e[3]))
                        if p:
                               self.adj[vertex][i][3] += p
                               self.adj[e[0]][e[1]][3] -= p
                               return p
                 lowercase__:							int         =     self.ptr[vertex] + 1
          return 0





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							Union[str, Any]         =     0, source
          for l in range(31):  # noqa: E741  l = 30 maybe faster for random data
                 while True:
                        lowercase__    ,      lowercase__:							Union[str, Any]         =     [0] * len(self.q), [0] * len(self.q)
                        lowercase__    ,      lowercase__    ,      lowercase__:							str         =     0, 1, 1
                        while qi < qe and not self.lvl[sink]:
                               lowercase__:							Tuple         =     self.q[qi]
                               qi += 1
                               for e in self.adj[v]:
                                      if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                                             lowercase__:							Optional[int]         =     e[0]
                                             qe += 1
                                             lowercase__:							str         =     self.lvl[v] + 1

                        lowercase__:							List[str]         =     self.depth_first_search(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)
                        while p:
                               flow += p
                               lowercase__:							Any         =     self.depth_first_search(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)

                        if not self.lvl[sink]:
                               break

          return flow
UpperCamelCase					=     Dinic(10)
UpperCamelCase					=     0
UpperCamelCase					=     9
for vertex in range(1, 5):
  graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
  graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
  graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))








'''simple docstring'''





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


UpperCAmelCase            = logging.get_logger(__name__)

if is_vision_available():
						import PIL.Image
						from PIL import Image
						from PIL.Image import Image as ImageType
else:
						UpperCAmelCase            = object

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf

class     a :




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case							:  Any			=     value




				def __str__(				self						:					Optional[int]   ):
										return self.to_string()




				def 						__snake_case				(				self						:					Optional[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return self._value




				def 						__snake_case				(				self						:					List[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return str(self._value   )




class     a (					__magic_name__					,__magic_name__     ):




				def 						__snake_case				(				self						:					Optional[Any]   ):
										return self._value




				def 						__snake_case				(				self						:					str   ):
										return self._value




class     a (					__magic_name__					,__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

										if not is_vision_available():
																raise ImportError('''PIL must be installed in order to handle images.'''   )

										snake_case							:  Union[str, Any]			=     None
										snake_case							:  List[Any]			=     None
										snake_case							:  List[str]			=     None

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[str]			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  Any			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Union[str, Any]			=     value
										else:
																raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._raw is not None:
																return self._raw

										if self._path is not None:
																snake_case							:  str			=     Image.open(self._path   )
																return self._raw




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										if self._path is not None:
																return self._path

										if self._raw is not None:
																snake_case							:  int			=     tempfile.mkdtemp()
																snake_case							:  str			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )
																self._raw.save(self._path   )

																return self._path

										if self._tensor is not None:
																snake_case							:  Tuple			=     self._tensor.cpu().detach().numpy()

																# There is likely simpler than load into image into save
																snake_case							:  str			=     Image.fromarray((array * 2_55).astype(np.uinta   )   )

																snake_case							:  List[Any]			=     tempfile.mkdtemp()
																snake_case							:  Optional[Any]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )

																img.save(self._path   )

																return self._path




class     a (					__magic_name__     ):




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]=1_60_00   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

										if not is_soundfile_availble():
																raise ImportError('''soundfile must be installed in order to handle audio.'''   )

										snake_case							:  List[Any]			=     None
										snake_case							:  int			=     None

										snake_case							:  Tuple			=     samplerate

										if isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  int			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     value
										else:
																raise ValueError(F"""Unsupported audio type: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										from IPython.display import Audio, display

										display(Audio(self.to_string(),     rate=self.samplerate   )   )




				def 						__snake_case				(				self						:					List[str]   ):
										if self._tensor is not None:
																return self._tensor

										if self._path is not None:
																snake_case,       snake_case							:  Optional[Any]			=     sf.read(self._path   )
																snake_case							:  Any			=     torch.tensor(SCREAMING_SNAKE_CASE_   )
																return self._tensor




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._path is not None:
																return self._path

										if self._tensor is not None:
																snake_case							:  Optional[Any]			=     tempfile.mkdtemp()
																snake_case							:  List[str]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.wav'''   )
																sf.write(self._path,     self._tensor,     samplerate=self.samplerate   )
																return self._path




UpperCAmelCase            = {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCAmelCase            = {str: AgentText}

if is_vision_available():
						UpperCAmelCase            = AgentImage

def   A			(							*A_				:       List[str]     ,				**A_				:       Any							):
						snake_case							:  str			=     [(arg.to_raw() if isinstance(A_     ,				A_							) else arg) for arg in args]
						snake_case							:  Dict			=     {k: (v.to_raw() if isinstance(A_     ,				A_							) else v) for k, v in kwargs.items()}
						return args, kwargs





def   A			(							A_				:       Union[str, Any]     ,				A_				:       Any=None							):
						if isinstance(A_     ,				A_							):
												snake_case							:  Union[str, Any]			=     {}
												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:
																														snake_case							:  Optional[int]			=     AGENT_TYPE_MAPPING[output_types[i]](A_							)
																								else:
																														snake_case							:  List[str]			=     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							):
																																				snake_case							:  Dict			=     _v(A_							)
																								if k not in decoded_outputs:
																														snake_case							:  List[str]			=     AgentType[v]

						elif isinstance(A_     ,				(list, tuple)							):
												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
																								snake_case							:  Union[str, Any]			=     False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				decoded_outputs.append(_v(A_							)							)
																																				snake_case							:  Tuple			=     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
																		snake_case							:  List[str]			=     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

