
'''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


__A       =      logging.get_logger(__name__)

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

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf





class      A							:



			def __init__(   self    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    value



			def __str__(   self    )     ->						List[str]:




									'''simple docstring'''



									return self.to_string()



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




									'''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    )     ->						str:




									'''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      A							(    __UpperCAmelCase						,  __UpperCAmelCase			):



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




									'''simple docstring'''



									return self._value



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									return self._value





class      A							(    __UpperCAmelCase						,  __UpperCAmelCase			):



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




									'''simple docstring'''



									super().__init__(lowerCamelCase__    )

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

									lowercase__								=    None
									lowercase__								=    None
									lowercase__								=    None

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



			def A__	(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    )     ->						Dict:




									'''simple docstring'''



									from IPython.display import Image, display

									display(Image(self.to_string()    )    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



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

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



			def A__	(   self    )     ->						str:




									'''simple docstring'''



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

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

															return self._path

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

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

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

															img.save(self._path    )

															return self._path





class      A							(    __UpperCAmelCase			):



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=16_000    )     ->						Tuple:




									'''simple docstring'''



									super().__init__(lowerCamelCase__    )

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

									lowercase__								=    None
									lowercase__								=    None

									lowercase__								=    samplerate

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



			def A__	(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    )     ->						Union[str, Any]:




									'''simple docstring'''



									from IPython.display import Audio, display

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



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



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

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



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



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

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




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

if is_vision_available():
				__A       =      AgentImage





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







def 	_A						(							lowercase__						,						lowercase__=None		):
						if isinstance(lowercase__						,						lowercase__		):
												lowercase__								=    {}
												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:
																														lowercase__								=    AGENT_TYPE_MAPPING[output_types[i]](lowercase__		)
																								else:
																														lowercase__								=    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		):
																																				lowercase__								=    _v(lowercase__		)
																								if k not in decoded_outputs:
																														lowercase__								=    AgentType[v]

						elif isinstance(lowercase__						,						(list, tuple)		):
												lowercase__								=    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
																								lowercase__								=    False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowercase__						,						_k		):
																																				decoded_outputs.append(_v(lowercase__		)		)
																																				lowercase__								=    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
																		lowercase__								=    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








'''simple docstring'''



def 	_A						(							lowercase__		):
						lowercase__								=    1.3
						lowercase__								=    len(lowercase__		)
						lowercase__								=    False

						while not completed:
												# Update the gap value for a next comb
												lowercase__								=    int(gap / shrink_factor		)
												if gap <= 1:
																		lowercase__								=    True

												lowercase__								=    0
												while index + gap < len(lowercase__		):
																		if data[index] > data[index + gap]:
																								# Swap values
																								lowercase__	,  lowercase__								=    data[index + gap], data[index]
																								lowercase__								=    False
																		index += 1

						return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				print(comb_sort(unsorted))






