


"""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 							lowerCamelCase       :


							"""simple docstring"""






							def __init__(						self :     int		, lowerCamelCase_ :     List[Any]				):
													UpperCAmelCase__		=    value






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






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




class 							lowerCamelCase       (			UpperCamelCase__				,					UpperCamelCase__ ):


							"""simple docstring"""






							def 					lowerCAmelCase     (						self :     str				):
													return self._value






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




class 							lowerCamelCase       (			UpperCamelCase__				,					UpperCamelCase__ ):


							"""simple docstring"""






							def __init__(						self :     Optional[int]		, lowerCamelCase_ :     Dict				):
													super().__init__(snake_case_				)

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

													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None

													if isinstance(snake_case_		, snake_case_				):
																			UpperCAmelCase__		=    value
													elif isinstance(snake_case_		, (str, pathlib.Path)				):
																			UpperCAmelCase__		=    value
													elif isinstance(snake_case_		, torch.Tensor				):
																			UpperCAmelCase__		=    value
													else:
																			raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(snake_case_				)}"""				)






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     Optional[Any]=None		, lowerCamelCase_ :     Optional[int]=None				):
													from IPython.display import Image, display

													display(Image(self.to_string()				)				)






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

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






							def 					lowerCAmelCase     (						self :     Optional[Any]				):
													if self._path is not None:
																			return self._path

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

																			return self._path

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

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

																			UpperCAmelCase__		=    tempfile.mkdtemp()
																			UpperCAmelCase__		=    os.path.join(snake_case_		, str(uuid.uuida()				) + '''.png'''				)

																			img.save(self._path				)

																			return self._path




class 							lowerCamelCase       (			UpperCamelCase__ ):


							"""simple docstring"""






							def __init__(						self :     Union[str, Any]		, lowerCamelCase_ :     List[Any]		, lowerCamelCase_ :     Dict=16000				):
													super().__init__(snake_case_				)

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

													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None

													UpperCAmelCase__		=    samplerate

													if isinstance(snake_case_		, (str, pathlib.Path)				):
																			UpperCAmelCase__		=    value
													elif isinstance(snake_case_		, torch.Tensor				):
																			UpperCAmelCase__		=    value
													else:
																			raise ValueError(f"""Unsupported audio type: {type(snake_case_				)}"""				)






							def 					lowerCAmelCase     (						self :     str		, lowerCamelCase_ :     Dict=None		, lowerCamelCase_ :     Optional[int]=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__		=    sf.read(self._path				)
																			UpperCAmelCase__		=    torch.tensor(snake_case_				)
																			return self._tensor






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

													if self._tensor is not None:
																			UpperCAmelCase__		=    tempfile.mkdtemp()
																			UpperCAmelCase__		=    os.path.join(snake_case_		, 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__ (       *_UpperCamelCase	,			**_UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None ):





						"""simple docstring"""

						if isinstance(__a	,			__a ):
												UpperCAmelCase__		=    {}
												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__		=    AGENT_TYPE_MAPPING[output_types[i]](__a )
																								else:
																														UpperCAmelCase__		=    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 ):
																																				UpperCAmelCase__		=    _v(__a )
																								if k not in decoded_outputs:
																														UpperCAmelCase__		=    AgentType[v]

						elif isinstance(__a	,			(list, tuple) ):
												UpperCAmelCase__		=    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
																								UpperCAmelCase__		=    False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(__a	,			_k ):
																																				decoded_outputs.append(_v(__a ) )
																																				UpperCAmelCase__		=    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
																		UpperCAmelCase__		=    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



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """pegasus_x"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     int		, lowerCamelCase_ :     Any=96103		, lowerCamelCase_ :     Union[str, Any]=16384		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     Tuple=16		, lowerCamelCase_ :     Optional[int]=16		, lowerCamelCase_ :     Tuple=4096		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     List[str]=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     List[Any]=True		, lowerCamelCase_ :     str=True		, lowerCamelCase_ :     int="gelu"		, lowerCamelCase_ :     Optional[Any]=1024		, lowerCamelCase_ :     Optional[int]=0.1		, lowerCamelCase_ :     str=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     List[str]=0.02		, lowerCamelCase_ :     Optional[Any]=0		, lowerCamelCase_ :     List[str]=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     str=1		, lowerCamelCase_ :     Optional[Any]=1		, lowerCamelCase_ :     Tuple=32		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=True		, **lowerCamelCase_ :     int		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True

													UpperCAmelCase__		=    num_global_tokens
													UpperCAmelCase__		=    block_size
													UpperCAmelCase__		=    stagger_local_blocks

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






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.encoder_attention_heads






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.d_model