






"""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


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		_SCREAMING_SNAKE_CASE :    Optional[int]    =						object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf






class 					a						:






				def __init__(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		int:
						lowerCamelCase_   					=						value






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






				def        UpperCamelCase	(	self					:							Dict )					->		Dict:
						logger.error(
						    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable' )
						return self._value






				def        UpperCamelCase	(	self					:							str )					->		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 					a						( __snake_case  ,				__snake_case		):






				def        UpperCamelCase	(	self					:							Optional[Any] )					->		Union[str, Any]:
						return self._value






				def        UpperCamelCase	(	self					:							str )					->		List[Any]:
						return self._value






class 					a						( __snake_case  ,				__snake_case		):






				def __init__(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		Dict:
						super().__init__(__SCREAMING_SNAKE_CASE )

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

						lowerCamelCase_   					=						None
						lowerCamelCase_   					=						None
						lowerCamelCase_   					=						None

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






				def        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							List[str]=None	,						__SCREAMING_SNAKE_CASE					:							List[str]=None )					->		int:
						from IPython.display import Image, display

						display(Image(self.to_string() ) )






				def        UpperCamelCase	(	self					:							int )					->		Dict:
						if self._raw is not None:
								return self._raw

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






				def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
						if self._path is not None:
								return self._path

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

								return self._path

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

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

								lowerCamelCase_   					=						tempfile.mkdtemp()
								lowerCamelCase_   					=						os.path.join(__SCREAMING_SNAKE_CASE	,						str(uuid.uuida() ) + '.png' )

								img.save(self._path )

								return self._path






class 					a						( __snake_case		):






				def __init__(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							str=16000 )					->		List[Any]:
						super().__init__(__SCREAMING_SNAKE_CASE )

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

						lowerCamelCase_   					=						None
						lowerCamelCase_   					=						None

						lowerCamelCase_   					=						samplerate

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






				def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str]=None	,						__SCREAMING_SNAKE_CASE					:							Dict=None )					->		List[Any]:
						from IPython.display import Audio, display

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






				def        UpperCamelCase	(	self					:							Any )					->		Union[str, Any]:
						if self._tensor is not None:
								return self._tensor

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






				def        UpperCamelCase	(	self					:							List[str] )					->		Union[str, Any]:
						if self._path is not None:
								return self._path

						if self._tensor is not None:
								lowerCamelCase_   					=						tempfile.mkdtemp()
								lowerCamelCase_   					=						os.path.join(__SCREAMING_SNAKE_CASE	,						str(uuid.uuida() ) + '.wav' )
								sf.write(self._path	,						self._tensor	,						samplerate=self.samplerate )
								return self._path
_SCREAMING_SNAKE_CASE :    Optional[int]    =						{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_SCREAMING_SNAKE_CASE :    str    =						{str: AgentText}

if is_vision_available():
		_SCREAMING_SNAKE_CASE :    Any    =						AgentImage







def 					lowerCamelCase__	(							*_lowerCamelCase	:		Optional[Any]		,    **_lowerCamelCase	:		Any			)				->    List[str]:
		lowerCamelCase_   					=						[(arg.to_raw() if isinstance(_lowerCamelCase		,    _lowerCamelCase			) else arg) for arg in args]
		lowerCamelCase_   					=						{k: (v.to_raw() if isinstance(_lowerCamelCase		,    _lowerCamelCase			) else v) for k, v in kwargs.items()}
		return args, kwargs







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Union[str, Any]=None			)				->    List[str]:
		if isinstance(_lowerCamelCase		,    _lowerCamelCase			):
				lowerCamelCase_   					=						{}
				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:
										lowerCamelCase_   					=						AGENT_TYPE_MAPPING[output_types[i]](_lowerCamelCase			)
								else:
										lowerCamelCase_   					=						AgentType(_lowerCamelCase			)

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

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

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

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

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

		return decoded_outputs












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int			)				->    int:
  if not root or root not in binary_tree_mirror_dictionary:
    return
  lowerCamelCase_					,     lowerCamelCase_   					=						binary_tree_mirror_dictionary[root][:2]
  lowerCamelCase_   					=						[right_child, left_child]
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int = 1			)				->    dict:
  if not binary_tree:
    raise ValueError('binary tree cannot be empty'			)
  if root not in binary_tree:
    lowerCamelCase_   					=						F'''root {root} is not present in the binary_tree'''
    raise ValueError(_lowerCamelCase			)
  lowerCamelCase_   					=						dict(_lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Tuple    =						{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'''Binary tree: {binary_tree}''')
  _SCREAMING_SNAKE_CASE :    Dict    =						binary_tree_mirror(binary_tree, 5)
  print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')




