
'''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__       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       value



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




									'''simple docstring'''



									return self.to_string()



			def A__ (					self       )			->		int:




									'''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       )			->		Optional[int]:




									'''simple docstring'''



									return self._value



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									return self._value





class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):



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




									'''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       )			->		List[Any]:




									'''simple docstring'''



									from IPython.display import Image, display

									display(Image(self.to_string()       )       )



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




									'''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       )			->		Optional[Any]:




									'''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       )			->		Union[str, Any]:




									'''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       )			->		str:




									'''simple docstring'''



									from IPython.display import Audio, display

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



			def A__ (					self       )			->		int:




									'''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       )			->		Dict:




									'''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__	):

						# validate:
						if not isinstance(lowercase__		,		lowercase__	) or not all(
						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
												raise ValueError("""The citations should be a list of non negative integers."""	)

						citations.sort()
						lowercase__							 =       len(lowercase__	)

						for i in range(lowercase__	):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
				import doctest

				doctest.testmod()






