





'''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    =			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    =			object

if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf






class 							__lowercase				:







	'''simple docstring'''

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







				'''simple docstring'''





				__lowercase   =							value


	def __str__(self       )							->							Optional[int]:







				'''simple docstring'''





				return self.to_string()


	def   _UpperCAmelCase   (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   _UpperCAmelCase   (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 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__  ):







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				return self._value


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				return self._value






class 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__  ):







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase       )							->							Union[str, 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   _UpperCAmelCase   (self   ,_lowerCamelCase=None   ,_lowerCamelCase=None       )							->							Optional[Any]:







				'''simple docstring'''





				from IPython.display import Image, display

				display(Image(self.to_string()       )       )


	def   _UpperCAmelCase   (self       )							->							Optional[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   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''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 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase=16000       )							->							Optional[int]:







				'''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   _UpperCAmelCase   (self   ,_lowerCamelCase=None   ,_lowerCamelCase=None       )							->							List[str]:







				'''simple docstring'''





				from IPython.display import Audio, display

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


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''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   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''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





_SCREAMING_SNAKE_CASE    =			{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_SCREAMING_SNAKE_CASE    =			{str: AgentText}

if is_vision_available():
	_SCREAMING_SNAKE_CASE    =			AgentImage




def       _lowerCAmelCase  ( *lowerCamelCase_	:     str						,				**lowerCamelCase_	:     List[Any] ):
			__lowercase   =							[(arg.to_raw() if isinstance(lowerCamelCase_						,				lowerCamelCase_ ) else arg) for arg in args]
			__lowercase   =							{k: (v.to_raw() if isinstance(lowerCamelCase_						,				lowerCamelCase_ ) else v) for k, v in kwargs.items()}
			return args, kwargs




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple=None ):
			if isinstance(lowerCamelCase_						,				lowerCamelCase_ ):
						__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]](lowerCamelCase_ )
												else:
															__lowercase   =							AgentType(lowerCamelCase_ )

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

			elif isinstance(lowerCamelCase_						,				(list, tuple) ):
						__lowercase   =							type(lowerCamelCase_ )()
						for i, v in enumerate(lowerCamelCase_ ):
									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]](lowerCamelCase_ ) )
												else:
															decoded_outputs.append(AgentType(lowerCamelCase_ ) )
									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(lowerCamelCase_						,				_k ):
																		decoded_outputs.append(_v(lowerCamelCase_ ) )
																		__lowercase   =							True

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

			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]](lowerCamelCase_ )

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

			return decoded_outputs












'''simple docstring'''



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Union[str, Any]      =				CustomConfig


	def __init__(self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       )
				__lowercase   =							torch.nn.Linear(config.hidden_size   ,config.hidden_size       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.linear(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[Any]:







				'''simple docstring'''





				pass






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Dict      =				NoSuperInitConfig


	def __init__(self   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       )
				__lowercase   =							torch.nn.Linear(config.attribute   ,config.attribute       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.linear(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				pass





