



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


_lowercase				:					Tuple     					=logging.get_logger(__name__)

if is_vision_available():
	import PIL.Image
	from PIL import Image
	from PIL.Image import Image as ImageType
else:
	_lowercase				:					List[str]     					=object

if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf
class lowerCAmelCase_       :







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  value

	def __str__(	self       ):



				'''simple docstring'''


				return self.to_string()

	def 				_A			(	self       ):



				'''simple docstring'''


				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       ):



				'''simple docstring'''


				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_       (   A_ ,A_			):







	'''simple docstring'''




	def 				_A			(	self       ):



				'''simple docstring'''


				return self._value







	def 				_A			(	self       ):



				'''simple docstring'''


				return self._value


class lowerCAmelCase_       (   A_ ,A_			):







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				super().__init__(lowerCamelCase       )

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

				a__					=  None
				a__					=  None
				a__					=  None

				if isinstance(lowerCamelCase   ,       lowerCamelCase       ):
							a__					=  value
				elif isinstance(lowerCamelCase   ,       (str, pathlib.Path)       ):
							a__					=  value
				elif isinstance(lowerCamelCase   ,       torch.Tensor       ):
							a__					=  value
				else:
							raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase       )}'       )

	def 				_A			(	self   ,       lowerCamelCase=None   ,       lowerCamelCase=None       ):



				'''simple docstring'''


				from IPython.display import Image, display

				display(Image(self.to_string()       )       )

	def 				_A			(	self       ):



				'''simple docstring'''


				if self._raw is not None:
							return self._raw

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







	def 				_A			(	self       ):



				'''simple docstring'''


				if self._path is not None:
							return self._path

				if self._raw is not None:
							a__					=  tempfile.mkdtemp()
							a__					=  os.path.join(lowerCamelCase   ,       str(uuid.uuida()       ) + """.png"""       )
							self._raw.save(self._path       )

							return self._path

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

							# There is likely simpler than load into image into save
							a__					=  Image.fromarray((array * 255).astype(np.uinta       )       )

							a__					=  tempfile.mkdtemp()
							a__					=  os.path.join(lowerCamelCase   ,       str(uuid.uuida()       ) + """.png"""       )

							img.save(self._path       )

							return self._path


class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase=1_6000       ):



				'''simple docstring'''


				super().__init__(lowerCamelCase       )

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

				a__					=  None
				a__					=  None

				a__					=  samplerate

				if isinstance(lowerCamelCase   ,       (str, pathlib.Path)       ):
							a__					=  value
				elif isinstance(lowerCamelCase   ,       torch.Tensor       ):
							a__					=  value
				else:
							raise ValueError(f'Unsupported audio type: {type(lowerCamelCase       )}'       )

	def 				_A			(	self   ,       lowerCamelCase=None   ,       lowerCamelCase=None       ):



				'''simple docstring'''


				from IPython.display import Audio, display

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

	def 				_A			(	self       ):



				'''simple docstring'''


				if self._tensor is not None:
							return self._tensor

				if self._path is not None:
							a__				,       a__					=  sf.read(self._path       )
							a__					=  torch.tensor(lowerCamelCase       )
							return self._tensor







	def 				_A			(	self       ):



				'''simple docstring'''


				if self._path is not None:
							return self._path

				if self._tensor is not None:
							a__					=  tempfile.mkdtemp()
							a__					=  os.path.join(lowerCamelCase   ,       str(uuid.uuida()       ) + """.wav"""       )
							sf.write(self._path   ,       self._tensor   ,       samplerate=self.samplerate       )
							return self._path


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

if is_vision_available():
	_lowercase				:					List[Any]     					=AgentImage




def    UpperCAmelCase					(  *lowercase__						:					Optional[int]				,					**lowercase__						:					Dict	):



			'''simple docstring'''





			a__					=  [(arg.to_raw() if isinstance(lowercase__				,					lowercase__	) else arg) for arg in args]
			a__					=  {k: (v.to_raw() if isinstance(lowercase__				,					lowercase__	) else v) for k, v in kwargs.items()}
			return args, kwargs





def    UpperCAmelCase					(  lowercase__						:					Any				,					lowercase__						:					Tuple=None	):



			'''simple docstring'''





			if isinstance(lowercase__				,					lowercase__	):
						a__					=  {}
						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:
															a__					=  AGENT_TYPE_MAPPING[output_types[i]](lowercase__	)
												else:
															a__					=  AgentType(lowercase__	)

									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(lowercase__				,					_k	):
																		a__					=  _v(lowercase__	)
												if k not in decoded_outputs:
															a__					=  AgentType[v]

			elif isinstance(lowercase__				,					(list, tuple)	):
						a__					=  type(lowercase__	)()
						for i, v in enumerate(lowercase__	):
									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]](lowercase__	)	)
												else:
															decoded_outputs.append(AgentType(lowercase__	)	)
									else:
												# If the class does not have defined output, then we map according to the type
												a__					=  False
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
															if isinstance(lowercase__				,					_k	):
																		decoded_outputs.append(_v(lowercase__	)	)
																		a__					=  True

												if not found:
															decoded_outputs.append(AgentType(lowercase__	)	)

			else:
						if output_types[0] in AGENT_TYPE_MAPPING:
									# If the class has defined outputs, we can map directly according to the class definition
									a__					=  AGENT_TYPE_MAPPING[output_types[0]](lowercase__	)

						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(lowercase__				,					_k	):
															return _v(lowercase__	)
									return AgentType(lowercase__	)

			return decoded_outputs






def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			print("""\nVertex Distance"""	)
			for i in range(lowercase__	):
						if dist[i] != float("""inf"""	):
									print(lowercase__				,					"""\t"""				,					int(dist[i]	)				,					end="""\t"""	)
						else:
									print(lowercase__				,					"""\t"""				,					"""INF"""				,					end="""\t"""	)
						print()




def    UpperCAmelCase					(  lowercase__						:					Optional[int]				,					lowercase__						:					Any				,					lowercase__						:					Tuple	):



			'''simple docstring'''





			a__					=  float("""inf"""	)
			a__					=  -1
			for i in range(lowercase__	):
						if (not vset[i]) and mdist[i] < min_val:
									a__					=  i
									a__					=  mdist[i]
			return min_ind




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					Tuple				,					lowercase__						:					List[Any]	):



			'''simple docstring'''





			a__					=  [float("""inf"""	) for _ in range(lowercase__	)]
			a__					=  [False for _ in range(lowercase__	)]
			a__					=  0.0

			for _ in range(v - 1	):
						a__					=  min_dist(lowercase__				,					lowercase__				,					lowercase__	)
						a__					=  True

						for i in range(lowercase__	):
									if (
									    (not vset[i])
									    and graph[u][i] != float("""inf"""	)
									    and mdist[u] + graph[u][i] < mdist[i]
									):
												a__					=  mdist[u] + graph[u][i]

			print_dist(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	_lowercase				:					Any     					=int(input("""Enter number of vertices: """).strip())
	_lowercase				:					List[str]     					=int(input("""Enter number of edges: """).strip())

	_lowercase				:					List[str]     					=[[float("""inf""") for i in range(V)] for j in range(V)]

	for i in range(V):
		_lowercase				:					Dict     					=0.0

	for i in range(E):
		print("""\nEdge """, i + 1)
		_lowercase				:					Dict     					=int(input("""Enter source:""").strip())
		_lowercase				:					Optional[Any]     					=int(input("""Enter destination:""").strip())
		_lowercase				:					List[Any]     					=float(input("""Enter weight:""").strip())
		_lowercase				:					List[str]     					=weight

	_lowercase				:					List[Any]     					=int(input("""\nEnter shortest path source:""").strip())
	dijkstra(graph, V, gsrc)

