
'''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


__lowercase	=    logging.get_logger(__name__)

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

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf

class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: Dict					,						UpperCamelCase_				: Dict      ):
			lowerCAmelCase_   :       Optional[int]    							=value




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




	def 	__A      (						self				: Union[str, Any]      ):
			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      ):
			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     (  lowerCAmelCase_						,    lowerCAmelCase_ ):
	"""simple docstring"""





	def 	__A      (						self				: Optional[int]      ):
			return self._value





	def 	__A      (						self				: List[str]      ):
			return self._value







class 							_snake_case     (  lowerCAmelCase_						,    lowerCAmelCase_ ):
	"""simple docstring"""





	def __init__(						self				: Tuple					,						UpperCamelCase_				: Dict      ):
			super().__init__(UpperCamelCase_      )

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

			lowerCAmelCase_   :       Optional[int]    							=None
			lowerCAmelCase_   :       List[Any]    							=None
			lowerCAmelCase_   :       Tuple    							=None

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




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: List[Any]=None					,						UpperCamelCase_				: Optional[int]=None      ):
			from IPython.display import Image, display

			display(Image(self.to_string()      )      )




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

			if self._path is not None:
					lowerCAmelCase_   :       List[Any]    							=Image.open(self._path      )
					return self._raw





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

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

					return self._path

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

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

					lowerCAmelCase_   :       List[Any]    							=tempfile.mkdtemp()
					lowerCAmelCase_   :       List[str]    							=os.path.join(UpperCamelCase_					,						str(uuid.uuida()      ) + '''.png'''      )

					img.save(self._path      )

					return self._path







class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	def __init__(						self				: List[str]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Optional[Any]=16000      ):
			super().__init__(UpperCamelCase_      )

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

			lowerCAmelCase_   :       Tuple    							=None
			lowerCAmelCase_   :       List[str]    							=None

			lowerCAmelCase_   :       Dict    							=samplerate

			if isinstance(UpperCamelCase_					,						(str, pathlib.Path)      ):
					lowerCAmelCase_   :       Optional[int]    							=value
			elif isinstance(UpperCamelCase_					,						torch.Tensor      ):
					lowerCAmelCase_   :       int    							=value
			else:
					raise ValueError(F'Unsupported audio type: {type(UpperCamelCase_      )}'      )




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: int=None					,						UpperCamelCase_				: str=None      ):
			from IPython.display import Audio, display

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




	def 	__A      (						self				: Union[str, Any]      ):
			if self._tensor is not None:
					return self._tensor

			if self._path is not None:
					lowerCAmelCase_							,		lowerCAmelCase_   :       Optional[int]    							=sf.read(self._path      )
					lowerCAmelCase_   :       Dict    							=torch.tensor(UpperCamelCase_      )
					return self._tensor





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

			if self._tensor is not None:
					lowerCAmelCase_   :       int    							=tempfile.mkdtemp()
					lowerCAmelCase_   :       List[Any]    							=os.path.join(UpperCamelCase_					,						str(uuid.uuida()      ) + '''.wav'''      )
					sf.write(self._path					,						self._tensor					,						samplerate=self.samplerate      )
					return self._path







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

if is_vision_available():
							__lowercase	=    AgentImage







def 		SCREAMING_SNAKE_CASE__					(							*_SCREAMING_SNAKE_CASE  ,					**_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       List[str]    							=[(arg.to_raw() if isinstance(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ) else arg) for arg in args]
		lowerCAmelCase_   :       Optional[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 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=None  ):
		if isinstance(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
				lowerCAmelCase_   :       Tuple    							={}
				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:
										lowerCAmelCase_   :       Optional[int]    							=AGENT_TYPE_MAPPING[output_types[i]](_SCREAMING_SNAKE_CASE  )
								else:
										lowerCAmelCase_   :       Dict    							=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  ):
												lowerCAmelCase_   :       str    							=_v(_SCREAMING_SNAKE_CASE  )
								if k not in decoded_outputs:
										lowerCAmelCase_   :       Tuple    							=AgentType[v]

		elif isinstance(_SCREAMING_SNAKE_CASE  ,					(list, tuple)  ):
				lowerCAmelCase_   :       List[str]    							=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
								lowerCAmelCase_   :       int    							=False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(_SCREAMING_SNAKE_CASE  ,					_k  ):
												decoded_outputs.append(_v(_SCREAMING_SNAKE_CASE  )  )
												lowerCAmelCase_   :       int    							=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
						lowerCAmelCase_   :       Dict    							=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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






