



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


UpperCAmelCase								=   logging.get_logger(__name__)

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

if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

class 	__snake_case :







							'''simple docstring'''






							def __init__(							self					,					a_						):
											a__			 =						value


							def __str__(							self						):
											return self.to_string()


							def _a (							self						):
											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						):
											logger.error(
											    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""						)
											return str(self._value						)




class 	__snake_case (				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a (							self						):
											return self._value







							def _a (							self						):
											return self._value




class 	__snake_case (				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self					,					a_						):
											super().__init__(a_						)

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

											a__			 =						None
											a__			 =						None
											a__			 =						None

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


							def _a (							self					,					a_=None					,					a_=None						):
											from IPython.display import Image, display

											display(Image(self.to_string()						)						)


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

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







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

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

															return self._path

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

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

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

															img.save(self._path						)

															return self._path




class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_=16_000						):
											super().__init__(a_						)

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

											a__			 =						None
											a__			 =						None

											a__			 =						samplerate

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


							def _a (							self					,					a_=None					,					a_=None						):
											from IPython.display import Audio, display

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


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

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







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

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




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

if is_vision_available():
	UpperCAmelCase								=   AgentImage

def      A_ (		*__a       :			Union[str, Any]	,						**__a       :			str						):

				"""simple docstring"""
				a__			 =						[(arg.to_raw() if isinstance(__a	,						__a						) else arg) for arg in args]
				a__			 =						{k: (v.to_raw() if isinstance(__a	,						__a						) else v) for k, v in kwargs.items()}
				return args, kwargs




def      A_ (		__a       :			str	,						__a       :			int=None						):

				"""simple docstring"""
				if isinstance(__a	,						__a						):
								a__			 =						{}
								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__			 =						AGENT_TYPE_MAPPING[output_types[i]](__a						)
																else:
																				a__			 =						AgentType(__a						)

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

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

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

				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__			 =						AGENT_TYPE_MAPPING[output_types[0]](__a						)

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

				return decoded_outputs






from __future__ import annotations

def      A_ (		__a       :			list[int | float]						):

				"""simple docstring"""
				if len(__a						) == 0:
								raise ValueError("""find_max() arg is an empty sequence"""						)
				a__			 =						nums[0]
				for x in nums:
								if x > max_num:
												a__			 =						x
				return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)

