



'''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 :      List[Any]             =object

if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf


class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	def __init__(		self					,   A			) ->							Optional[int]:
				A:      Union[str, Any]														=   value


	def __str__(		self			) ->							List[Any]:
				return self.to_string()


	def      a__ (		self			) ->							List[str]:
				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   SCREAMING_SNAKE_CASE__				(   _a     ,			_a					):
	"""simple docstring"""


	def      a__ (		self			) ->							str:
				return self._value





	def      a__ (		self			) ->							str:
				return self._value







class   SCREAMING_SNAKE_CASE__				(   _a     ,			_a					):
	"""simple docstring"""


	def __init__(		self					,   A			) ->							Optional[int]:
				super().__init__(A			)

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

				A:      Optional[Any]														=   None
				A:      str														=   None
				A:      Union[str, Any]														=   None

				if isinstance(A					,   A			):
							A:      Tuple														=   value
				elif isinstance(A					,   (str, pathlib.Path)			):
							A:      Dict														=   value
				elif isinstance(A					,   torch.Tensor			):
							A:      List[Any]														=   value
				else:
							raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(A			)}'			)


	def      a__ (		self					,   A=None					,   A=None			) ->							Dict:
				from IPython.display import Image, display

				display(Image(self.to_string()			)			)


	def      a__ (		self			) ->							str:
				if self._raw is not None:
							return self._raw

				if self._path is not None:
							A:      Union[str, Any]														=   Image.open(self._path			)
							return self._raw





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

				if self._raw is not None:
							A:      int														=   tempfile.mkdtemp()
							A:      List[Any]														=   os.path.join(A					,   str(uuid.uuida()			) + """.png"""			)
							self._raw.save(self._path			)

							return self._path

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

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

							A:      List[str]														=   tempfile.mkdtemp()
							A:      Dict														=   os.path.join(A					,   str(uuid.uuida()			) + """.png"""			)

							img.save(self._path			)

							return self._path







class   SCREAMING_SNAKE_CASE__				(   _a					):
	"""simple docstring"""


	def __init__(		self					,   A					,   A=1_60_00			) ->							str:
				super().__init__(A			)

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

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

				A:      Union[str, Any]														=   samplerate

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


	def      a__ (		self					,   A=None					,   A=None			) ->							int:
				from IPython.display import Audio, display

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


	def      a__ (		self			) ->							int:
				if self._tensor is not None:
							return self._tensor

				if self._path is not None:
							A							,		A:      int														=   sf.read(self._path			)
							A:      Union[str, Any]														=   torch.tensor(A			)
							return self._tensor





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

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







__SCREAMING_SNAKE_CASE :      str             ={'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
__SCREAMING_SNAKE_CASE :      Tuple             ={str: AgentText}

if is_vision_available():
					__SCREAMING_SNAKE_CASE :      List[str]             =AgentImage



def 		_SCREAMING_SNAKE_CASE							(				*lowerCamelCase__   :      Optional[int]							,							**lowerCamelCase__   :      List[Any]				):
			A:      Optional[Any]														=   [(arg.to_raw() if isinstance(lowerCamelCase__							,							lowerCamelCase__				) else arg) for arg in args]
			A:      List[Any]														=   {k: (v.to_raw() if isinstance(lowerCamelCase__							,							lowerCamelCase__				) else v) for k, v in kwargs.items()}
			return args, kwargs



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      str							,							lowerCamelCase__   :      Tuple=None				):




			if isinstance(lowerCamelCase__							,							lowerCamelCase__				):
						A:      Optional[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:
															A:      Any														=   AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase__				)
												else:
															A:      int														=   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				):
																		A:      Tuple														=   _v(lowerCamelCase__				)
												if k not in decoded_outputs:
															A:      int														=   AgentType[v]

			elif isinstance(lowerCamelCase__							,							(list, tuple)				):
						A:      Tuple														=   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
												A:      Dict														=   False
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
															if isinstance(lowerCamelCase__							,							_k				):
																		decoded_outputs.append(_v(lowerCamelCase__				)				)
																		A:      Optional[int]														=   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
									A:      Union[str, Any]														=   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 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			for index, item in enumerate(lowerCamelCase__				):
						if item == target:
									return index
			return -1



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			if not (0 <= high < len(lowerCamelCase__				) and 0 <= low < len(lowerCamelCase__				)):
						raise Exception("""Invalid upper or lower bound!"""				)
			if high < low:
						return -1
			if sequence[low] == target:
						return low
			if sequence[high] == target:
						return high
			return rec_linear_search(lowerCamelCase__							,							low + 1							,							high - 1							,							lowerCamelCase__				)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE :      List[str]             =input('Enter numbers separated by comma:\n').strip()
					__SCREAMING_SNAKE_CASE :      str             =[int(item.strip()) for item in user_input.split(',')]

					__SCREAMING_SNAKE_CASE :      List[str]             =int(input('Enter a single number to be found in the list:\n').strip())
					__SCREAMING_SNAKE_CASE :      List[Any]             =linear_search(sequence, target)
					if result != -1:
										print(f"""linear_search({sequence}, {target}) = {result}""")
					else:
										print(f"""{target} was not found in {sequence}""")





