






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     :							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:
							lowercase     :							Union[str, Any]	     =			object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf




class 						UpperCAmelCase_  :


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE )       -> str:
								snake_case_ :							Optional[int]         =				value






						def __str__(		self )       -> Union[str, Any]:
								return self.to_string()






						def 					_lowerCAmelCase				(		self )       -> List[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 )       -> 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 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




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






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								return self._value




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE )       -> Any:
								super().__init__(_SCREAMING_SNAKE_CASE )

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

								snake_case_ :							str         =				None
								snake_case_ :							List[str]         =				None
								snake_case_ :							Any         =				None

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






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None )       -> Any:
								from IPython.display import Image, display

								display(Image(self.to_string() ) )






						def 					_lowerCAmelCase				(		self )       -> str:
								if self._raw is not None:
										return self._raw

								if self._path is not None:
										snake_case_ :							Union[str, Any]         =				Image.open(self._path )
										return self._raw






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								if self._path is not None:
										return self._path

								if self._raw is not None:
										snake_case_ :							List[Any]         =				tempfile.mkdtemp()
										snake_case_ :							Dict         =				os.path.join(_SCREAMING_SNAKE_CASE						, str(uuid.uuida() ) + ".png" )
										self._raw.save(self._path )

										return self._path

								if self._tensor is not None:
										snake_case_ :							Optional[int]         =				self._tensor.cpu().detach().numpy()

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

										snake_case_ :							Optional[Any]         =				tempfile.mkdtemp()
										snake_case_ :							List[Any]         =				os.path.join(_SCREAMING_SNAKE_CASE						, str(uuid.uuida() ) + ".png" )

										img.save(self._path )

										return self._path




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=1_6000 )       -> str:
								super().__init__(_SCREAMING_SNAKE_CASE )

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

								snake_case_ :							str         =				None
								snake_case_ :							str         =				None

								snake_case_ :							Union[str, Any]         =				samplerate

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






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None )       -> Optional[Any]:
								from IPython.display import Audio, display

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






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								if self._tensor is not None:
										return self._tensor

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






						def 					_lowerCAmelCase				(		self )       -> Any:
								if self._path is not None:
										return self._path

								if self._tensor is not None:
										snake_case_ :							Optional[int]         =				tempfile.mkdtemp()
										snake_case_ :							str         =				os.path.join(_SCREAMING_SNAKE_CASE						, str(uuid.uuida() ) + ".wav" )
										sf.write(self._path						, self._tensor						, samplerate=self.samplerate )
										return self._path




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

if is_vision_available():
							lowercase     :							Any	     =			AgentImage

def    lowerCAmelCase__		(			*_a				:	List[Any]     ,					**_a				:	Dict			):
		snake_case_ :							Union[str, Any]         =				[(arg.to_raw() if isinstance(_a     ,					_a			) else arg) for arg in args]
		snake_case_ :							int         =				{k: (v.to_raw() if isinstance(_a     ,					_a			) else v) for k, v in kwargs.items()}
		return args, kwargs

def    lowerCAmelCase__		(			_a				:	Union[str, Any]     ,					_a				:	Optional[int]=None			):
		if isinstance(_a     ,					_a			):
				snake_case_ :							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:
										snake_case_ :							int         =				AGENT_TYPE_MAPPING[output_types[i]](_a			)
								else:
										snake_case_ :							Any         =				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			):
												snake_case_ :							Optional[Any]         =				_v(_a			)
								if k not in decoded_outputs:
										snake_case_ :							int         =				AgentType[v]

		elif isinstance(_a     ,					(list, tuple)			):
				snake_case_ :							int         =				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
								snake_case_ :							Any         =				False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(_a     ,					_k			):
												decoded_outputs.append(_v(_a			)			)
												snake_case_ :							Optional[Any]         =				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
						snake_case_ :							int         =				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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any	     =			logging.get_logger(__name__)

lowercase     :							int	     =			{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    'graphormer'
						A   :      List[str]						 =    ['past_key_values']






						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1						, _SCREAMING_SNAKE_CASE = 512 * 9						, _SCREAMING_SNAKE_CASE = 512 * 3						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = 128						, _SCREAMING_SNAKE_CASE = 5						, _SCREAMING_SNAKE_CASE = 1024						, _SCREAMING_SNAKE_CASE = "multi_hop"						, _SCREAMING_SNAKE_CASE = 512						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 12						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 768						, _SCREAMING_SNAKE_CASE = 32						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.1						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = "gelu"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = 0.0						, _SCREAMING_SNAKE_CASE = 8						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE = True						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE=1						, _SCREAMING_SNAKE_CASE=2						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								snake_case_ :							str         =				num_classes
								snake_case_ :							Union[str, Any]         =				num_atoms
								snake_case_ :							Dict         =				num_in_degree
								snake_case_ :							Union[str, Any]         =				num_out_degree
								snake_case_ :							List[Any]         =				num_edges
								snake_case_ :							Union[str, Any]         =				num_spatial
								snake_case_ :							int         =				num_edge_dis
								snake_case_ :							List[Any]         =				edge_type
								snake_case_ :							Any         =				multi_hop_max_dist
								snake_case_ :							Tuple         =				spatial_pos_max
								snake_case_ :							Union[str, Any]         =				max_nodes
								snake_case_ :							str         =				num_hidden_layers
								snake_case_ :							str         =				embedding_dim
								snake_case_ :							List[str]         =				embedding_dim
								snake_case_ :							Dict         =				ffn_embedding_dim
								snake_case_ :							Any         =				num_attention_heads
								snake_case_ :							List[str]         =				dropout
								snake_case_ :							List[str]         =				attention_dropout
								snake_case_ :							Optional[Any]         =				layerdrop
								snake_case_ :							Optional[int]         =				encoder_normalize_before
								snake_case_ :							Union[str, Any]         =				pre_layernorm
								snake_case_ :							int         =				apply_graphormer_init
								snake_case_ :							List[Any]         =				activation_fn
								snake_case_ :							str         =				embed_scale
								snake_case_ :							int         =				freeze_embeddings
								snake_case_ :							Optional[Any]         =				num_trans_layers_to_freeze
								snake_case_ :							int         =				share_input_output_embed
								snake_case_ :							int         =				traceable
								snake_case_ :							Tuple         =				q_noise
								snake_case_ :							Tuple         =				qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								snake_case_ :							List[Any]         =				kdim
								snake_case_ :							Tuple         =				vdim
								snake_case_ :							Any         =				self_attention
								snake_case_ :							Any         =				bias

								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE						, bos_token_id=_SCREAMING_SNAKE_CASE						, eos_token_id=_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE						, )
