

"""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       :  Optional[Any]        = object

if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

class     __a   :

			"""simple docstring"""

			def __init__(							self     :					Optional[Any]						,  lowercase_     :					Optional[int]						):
								UpperCamelCase__		:    Dict         =value






			def __str__(							self     :					Optional[int]						):
								return self.to_string()






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






			def    _lowerCAmelCase			(							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     __a   (      __SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE    ):

			"""simple docstring"""

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






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



class     __a   (      __SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE    ):

			"""simple docstring"""

			def __init__(							self     :					List[Any]						,  lowercase_     :					Optional[Any]						):
								super().__init__(UpperCamelCase__						)

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

								UpperCamelCase__		:    Optional[int]         =None
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    Union[str, Any]         =None

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






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Optional[Any]=None						,  lowercase_     :					Optional[int]=None						):
								from IPython.display import Image, display

								display(Image(self.to_string()						)						)






			def    _lowerCAmelCase			(							self     :					Tuple						):







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

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






			def    _lowerCAmelCase			(							self     :					str						):







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

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

													return self._path

								if self._tensor is not None:
													UpperCamelCase__		:    Union[str, Any]         =self._tensor.cpu().detach().numpy()

													# There is likely simpler than load into image into save
													UpperCamelCase__		:    Optional[int]         =Image.fromarray((array * 255).astype(np.uinta						)						)

													UpperCamelCase__		:    Union[str, Any]         =tempfile.mkdtemp()
													UpperCamelCase__		:    Optional[Any]         =os.path.join(UpperCamelCase__						,  str(uuid.uuida()						) + '''.png'''						)

													img.save(self._path						)

													return self._path



class     __a   (      __SCREAMING_SNAKE_CASE    ):

			"""simple docstring"""

			def __init__(							self     :					int						,  lowercase_     :					Dict						,  lowercase_     :					Optional[int]=1_6000						):
								super().__init__(UpperCamelCase__						)

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

								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    int         =None

								UpperCamelCase__		:    int         =samplerate

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






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					Optional[int]=None						,  lowercase_     :					Union[str, Any]=None						):
								from IPython.display import Audio, display

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






			def    _lowerCAmelCase			(							self     :					Optional[Any]						):







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

								if self._path is not None:
													UpperCamelCase__		:    Optional[Any]         =sf.read(self._path						)
													UpperCamelCase__		:    List[Any]         =torch.tensor(UpperCamelCase__						)
													return self._tensor






			def    _lowerCAmelCase			(							self     :					Any						):







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

								if self._tensor is not None:
													UpperCamelCase__		:    Any         =tempfile.mkdtemp()
													UpperCamelCase__		:    Optional[Any]         =os.path.join(UpperCamelCase__						,  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       :  List[str]        = {str: AgentText}

if is_vision_available():
	_SCREAMING_SNAKE_CASE       :  Optional[int]        = AgentImage



def        _lowerCAmelCase      (						*UpperCAmelCase     :							Tuple      ,							**UpperCAmelCase     :							Optional[int]				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__				) else arg) for arg in args]
					UpperCamelCase__		:    Any         ={k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__				) else v) for k, v in kwargs.items()}
					return args, kwargs







def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Any=None				):


					'''simple docstring'''






					if isinstance(SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__				):
										UpperCamelCase__		:    Union[str, 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:
																									UpperCamelCase__		:    Any         =AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__				)
																				else:
																									UpperCamelCase__		:    List[str]         =AgentType(SCREAMING_SNAKE_CASE__				)

															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(SCREAMING_SNAKE_CASE__      ,							_k				):
																														UpperCamelCase__		:    List[Any]         =_v(SCREAMING_SNAKE_CASE__				)
																				if k not in decoded_outputs:
																									UpperCamelCase__		:    Dict         =AgentType[v]

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

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

					else:
										if output_types[0] in AGENT_TYPE_MAPPING:
															# If the class has defined outputs, we can map directly according to the class definition
															UpperCamelCase__		:    int         =AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE__				)

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

					return decoded_outputs





"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						('_front', '_back', '_len')

			@dataclass
			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None



			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						('_cur',)






						def __init__(							self     :					Optional[int]						,  lowercase_     :					Deque._Node | None						):
											UpperCamelCase__		:    List[str]         =cur






						def __iter__(							self     :					List[str]						):
											return self






						def    _lowerCAmelCase			(							self     :					List[Any]						):
											if self._cur is None:
																# finished iterating
																raise StopIteration
											UpperCamelCase__		:    str         =self._cur.val
											UpperCamelCase__		:    Any         =self._cur.next_node

											return val






			def __init__(							self     :					str						,  lowercase_     :					Iterable[Any] | None = None						):
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    int         =0

								if iterable is not None:
													# append every value to the deque
													for val in iterable:
																		self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					Any						):
								UpperCamelCase__		:    Union[str, Any]         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    List[Any]         =1
								else:
													# connect nodes
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    Optional[Any]         =self._back
													UpperCamelCase__		:    str         =node  # assign new back to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Any						):
								UpperCamelCase__		:    str         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    str         =node
													UpperCamelCase__		:    Any         =1
								else:
													# connect nodes
													UpperCamelCase__		:    int         =self._front
													UpperCamelCase__		:    int         =node
													UpperCamelCase__		:    Any         =node  # assign new front to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					List[Any]						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.appendleft(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Optional[int]						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[Any]         =self._back
								UpperCamelCase__		:    Any         =self._back.prev_node  # set new back
								# drop the last node - python will deallocate memory automatically
								UpperCamelCase__		:    List[str]         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					str						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[str]         =self._front
								UpperCamelCase__		:    List[Any]         =self._front.next_node  # set new front and drop the first node
								UpperCamelCase__		:    Dict         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					Dict						):
								return self._front is None






			def __len__(							self     :					Any						):
								return self._len






			def __eq__(							self     :					Dict						,  lowercase_     :					object						):

								if not isinstance(lowercase_						,  lowercase_						):
													return NotImplemented

								UpperCamelCase__		:    Union[str, Any]         =self._front
								UpperCamelCase__		:    Union[str, Any]         =other._front

								# if the length of the dequeues are not the same, they are not equal
								if len(self						) != len(lowercase_						):
													return False

								while me is not None and oth is not None:
													# compare every value
													if me.val != oth.val:
																		return False
													UpperCamelCase__		:    Union[str, Any]         =me.next_node
													UpperCamelCase__		:    Union[str, Any]         =oth.next_node

								return True






			def __iter__(							self     :					Optional[int]						):
								return Deque._Iterator(self._front						)






			def __repr__(							self     :					Tuple						):
								UpperCamelCase__		:    List[Any]         =[]
								UpperCamelCase__		:    Optional[int]         =self._front
								while aux is not None:
													# append the values in a list to display
													values_list.append(aux.val						)
													UpperCamelCase__		:    Optional[Any]         =aux.next_node

								return f'''[{', '.join(repr(lowercase_						) for val in values_list						)}]'''


if __name__ == "__main__":
				import doctest

				doctest.testmod()


