
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     :				Dict             =       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 						__magic_name__				:





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]			):
							lowerCAmelCase						:   Dict							=       value





		def __str__(							self      :		Dict			):
							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      :		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 						__magic_name__				(  snake_case,			snake_case      ):





		def  _A							(							self      :		Dict			):
							return self._value





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



class 						__magic_name__				(  snake_case,			snake_case      ):





		def __init__(							self      :		Dict  ,					lowerCamelCase__      :		Union[str, Any]			):
							super().__init__(lowerCamelCase__			)

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

							lowerCAmelCase						:   Tuple							=       None
							lowerCAmelCase						:   int							=       None
							lowerCAmelCase						:   Optional[int]							=       None

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





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		int=None  ,					lowerCamelCase__      :		Any=None			):
							from IPython.display import Image, display

							display(Image(self.to_string()			)			)





		def  _A							(							self      :		Tuple			):
							if self._raw is not None:
												return self._raw

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





		def  _A							(							self      :		str			):
							if self._path is not None:
												return self._path

							if self._raw is not None:
												lowerCAmelCase						:   str							=       tempfile.mkdtemp()
												lowerCAmelCase						:   List[str]							=       os.path.join(lowerCamelCase__  ,					str(uuid.uuida()			) + '''.png'''			)
												self._raw.save(self._path			)

												return self._path

							if self._tensor is not None:
												lowerCAmelCase						:   Optional[Any]							=       self._tensor.cpu().detach().numpy()

												# There is likely simpler than load into image into save
												lowerCAmelCase						:   str							=       Image.fromarray((array * 2_5_5).astype(np.uinta			)			)

												lowerCAmelCase						:   str							=       tempfile.mkdtemp()
												lowerCAmelCase						:   List[str]							=       os.path.join(lowerCamelCase__  ,					str(uuid.uuida()			) + '''.png'''			)

												img.save(self._path			)

												return self._path



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Union[str, Any]=1_6_0_0_0			):
							super().__init__(lowerCamelCase__			)

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

							lowerCAmelCase						:   Optional[int]							=       None
							lowerCAmelCase						:   Dict							=       None

							lowerCAmelCase						:   Union[str, Any]							=       samplerate

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





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		int=None  ,					lowerCamelCase__      :		Tuple=None			):
							from IPython.display import Audio, display

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





		def  _A							(							self      :		Any			):
							if self._tensor is not None:
												return self._tensor

							if self._path is not None:
												lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       sf.read(self._path			)
												lowerCAmelCase						:   Tuple							=       torch.tensor(lowerCamelCase__			)
												return self._tensor





		def  _A							(							self      :		Tuple			):
							if self._path is not None:
												return self._path

							if self._tensor is not None:
												lowerCAmelCase						:   str							=       tempfile.mkdtemp()
												lowerCAmelCase						:   int							=       os.path.join(lowerCamelCase__  ,					str(uuid.uuida()			) + '''.wav'''			)
												sf.write(self._path  ,					self._tensor  ,					samplerate=self.samplerate			)
												return self._path



__SCREAMING_SNAKE_CASE     :				List[str]             =       {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       {str: AgentText}

if is_vision_available():
		__SCREAMING_SNAKE_CASE     :				Any             =       AgentImage
def       UpperCAmelCase__							(    *__magic_name__  :       str							,					**__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       [(arg.to_raw() if isinstance(__magic_name__							,					__magic_name__					) else arg) for arg in args]
					lowerCAmelCase						:   Dict							=       {k: (v.to_raw() if isinstance(__magic_name__							,					__magic_name__					) else v) for k, v in kwargs.items()}
					return args, kwargs
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]							,					__magic_name__  :       str=None					):


					'''simple docstring'''

					if isinstance(__magic_name__							,					__magic_name__					):
										lowerCAmelCase						:   Tuple							=       {}
										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						:   int							=       AGENT_TYPE_MAPPING[output_types[i]](__magic_name__					)
																				else:
																									lowerCAmelCase						:   Union[str, Any]							=       AgentType(__magic_name__					)

															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(__magic_name__							,					_k					):
																														lowerCAmelCase						:   Any							=       _v(__magic_name__					)
																				if k not in decoded_outputs:
																									lowerCAmelCase						:   str							=       AgentType[v]

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

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

					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						:   Optional[Any]							=       AGENT_TYPE_MAPPING[output_types[0]](__magic_name__					)

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

					return decoded_outputs


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


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

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
