code: 
  
  
  
  
  
  '''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__				: Optional[Any]									=    logging.get_logger(__name__)
  
  if is_vision_available():
  			import PIL.Image
  			from PIL import Image
  			from PIL.Image import Image as ImageType
  else:
  			a__				: int									=    object
  
  if is_torch_available():
  			import torch
  
  if is_soundfile_availble():
  			import soundfile as sf
  
  
  
  
  
  
  class 							__snake_case    :
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_		)			->		Union[str, Any]:
  		snake_case__  					=      value
  
  
  
  
  
  	def __str__(		self		)			->		List[Any]:
  		return self.to_string()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		logger.error(
  		    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'		)
  		return self._value
  
  
  
  
  
  	def      _snake_case      (		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    (				__magic_name__					,					__magic_name__       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		return self._value
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		return self._value
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__					,					__magic_name__       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_		)			->		Optional[Any]:
  		super().__init__(UpperCamelCase_		)
  
  		if not is_vision_available():
  			raise ImportError('PIL must be installed in order to handle images.'		)
  
  		snake_case__  					=      None
  		snake_case__  					=      None
  		snake_case__  					=      None
  
  		if isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  			snake_case__  					=      value
  		elif isinstance(UpperCamelCase_			,					(str, pathlib.Path)		):
  			snake_case__  					=      value
  		elif isinstance(UpperCamelCase_			,					torch.Tensor		):
  			snake_case__  					=      value
  		else:
  			raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(UpperCamelCase_		)}'''		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_=None			,					UpperCamelCase_=None		)			->		List[str]:
  		from IPython.display import Image, display
  
  		display(Image(self.to_string()		)		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		if self._raw is not None:
  			return self._raw
  
  		if self._path is not None:
  			snake_case__  					=      Image.open(self._path		)
  			return self._raw
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		if self._path is not None:
  			return self._path
  
  		if self._raw is not None:
  			snake_case__  					=      tempfile.mkdtemp()
  			snake_case__  					=      os.path.join(UpperCamelCase_			,					str(uuid.uuida()		) + '.png'		)
  			self._raw.save(self._path		)
  
  			return self._path
  
  		if self._tensor is not None:
  			snake_case__  					=      self._tensor.cpu().detach().numpy()
  
  			# There is likely simpler than load into image into save
  			snake_case__  					=      Image.fromarray((array * 255).astype(np.uinta		)		)
  
  			snake_case__  					=      tempfile.mkdtemp()
  			snake_case__  					=      os.path.join(UpperCamelCase_			,					str(uuid.uuida()		) + '.png'		)
  
  			img.save(self._path		)
  
  			return self._path
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_=1_6000		)			->		Optional[Any]:
  		super().__init__(UpperCamelCase_		)
  
  		if not is_soundfile_availble():
  			raise ImportError('soundfile must be installed in order to handle audio.'		)
  
  		snake_case__  					=      None
  		snake_case__  					=      None
  
  		snake_case__  					=      samplerate
  
  		if isinstance(UpperCamelCase_			,					(str, pathlib.Path)		):
  			snake_case__  					=      value
  		elif isinstance(UpperCamelCase_			,					torch.Tensor		):
  			snake_case__  					=      value
  		else:
  			raise ValueError(F'''Unsupported audio type: {type(UpperCamelCase_		)}'''		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_=None			,					UpperCamelCase_=None		)			->		str:
  		from IPython.display import Audio, display
  
  		display(Audio(self.to_string()			,					rate=self.samplerate		)		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		if self._tensor is not None:
  			return self._tensor
  
  		if self._path is not None:
  			snake_case__					,     snake_case__  					=      sf.read(self._path		)
  			snake_case__  					=      torch.tensor(UpperCamelCase_		)
  			return self._tensor
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		if self._path is not None:
  			return self._path
  
  		if self._tensor is not None:
  			snake_case__  					=      tempfile.mkdtemp()
  			snake_case__  					=      os.path.join(UpperCamelCase_			,					str(uuid.uuida()		) + '.wav'		)
  			sf.write(self._path			,					self._tensor			,					samplerate=self.samplerate		)
  			return self._path
  
  
  
  
  
  a__				: Tuple									=    {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
  a__				: List[str]									=    {str: AgentText}
  
  if is_vision_available():
  			a__				: Any									=    AgentImage
  
  
  
  
  def 	__lowerCamelCase (  *UpperCAmelCase_				,							**UpperCAmelCase_       )      ->Optional[Any]:
  	snake_case__  					=      [(arg.to_raw() if isinstance(UpperCAmelCase_				,							UpperCAmelCase_       ) else arg) for arg in args]
  	snake_case__  					=      {k: (v.to_raw() if isinstance(UpperCAmelCase_				,							UpperCAmelCase_       ) else v) for k, v in kwargs.items()}
  	return args, kwargs
  
  
  
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_=None       )      ->List[Any]:
  	if isinstance(UpperCAmelCase_				,							UpperCAmelCase_       ):
  		snake_case__  					=      {}
  		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__  					=      AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase_       )
  				else:
  					snake_case__  					=      AgentType(UpperCAmelCase_       )
  
  			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(UpperCAmelCase_				,							_k       ):
  						snake_case__  					=      _v(UpperCAmelCase_       )
  				if k not in decoded_outputs:
  					snake_case__  					=      AgentType[v]
  
  	elif isinstance(UpperCAmelCase_				,							(list, tuple)       ):
  		snake_case__  					=      type(UpperCAmelCase_       )()
  		for i, v in enumerate(UpperCAmelCase_       ):
  			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]](UpperCAmelCase_       )       )
  				else:
  					decoded_outputs.append(AgentType(UpperCAmelCase_       )       )
  			else:
  				# If the class does not have defined output, then we map according to the type
  				snake_case__  					=      False
  				for _k, _v in INSTANCE_TYPE_MAPPING.items():
  					if isinstance(UpperCAmelCase_				,							_k       ):
  						decoded_outputs.append(_v(UpperCAmelCase_       )       )
  						snake_case__  					=      True
  
  				if not found:
  					decoded_outputs.append(AgentType(UpperCAmelCase_       )       )
  
  	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__  					=      AGENT_TYPE_MAPPING[output_types[0]](UpperCAmelCase_       )
  
  		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(UpperCAmelCase_				,							_k       ):
  					return _v(UpperCAmelCase_       )
  			return AgentType(UpperCAmelCase_       )
  
  	return decoded_outputs
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  a__				: Optional[Any]									=    logging.get_logger(__name__)
  
  a__				: List[str]									=    {
      '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
      '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
      # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
  }
  
  # soft dependency
  if is_detectrona_available():
  			import detectrona
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''layoutlmv2'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3_0522			,					UpperCamelCase_=768			,					UpperCamelCase_=12			,					UpperCamelCase_=12			,					UpperCamelCase_=3072			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.1			,					UpperCamelCase_=512			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-1_2			,					UpperCamelCase_=0			,					UpperCamelCase_=1024			,					UpperCamelCase_=128			,					UpperCamelCase_=32			,					UpperCamelCase_=True			,					UpperCamelCase_=256			,					UpperCamelCase_=64			,					UpperCamelCase_=True			,					UpperCamelCase_=[7, 7, 256]			,					UpperCamelCase_=128			,					UpperCamelCase_=128			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_=False			,					UpperCamelCase_=None			,					**UpperCamelCase_			,					)			->		Tuple:
  		super().__init__(
  		    vocab_size=UpperCamelCase_			,					hidden_size=UpperCamelCase_			,					num_hidden_layers=UpperCamelCase_			,					num_attention_heads=UpperCamelCase_			,					intermediate_size=UpperCamelCase_			,					hidden_act=UpperCamelCase_			,					hidden_dropout_prob=UpperCamelCase_			,					attention_probs_dropout_prob=UpperCamelCase_			,					max_position_embeddings=UpperCamelCase_			,					type_vocab_size=UpperCamelCase_			,					initializer_range=UpperCamelCase_			,					layer_norm_eps=UpperCamelCase_			,					pad_token_id=UpperCamelCase_			,					**UpperCamelCase_			,					)
  		snake_case__  					=      max_ad_position_embeddings
  		snake_case__  					=      max_rel_pos
  		snake_case__  					=      rel_pos_bins
  		snake_case__  					=      fast_qkv
  		snake_case__  					=      max_rel_ad_pos
  		snake_case__  					=      rel_ad_pos_bins
  		snake_case__  					=      convert_sync_batchnorm
  		snake_case__  					=      image_feature_pool_shape
  		snake_case__  					=      coordinate_size
  		snake_case__  					=      shape_size
  		snake_case__  					=      has_relative_attention_bias
  		snake_case__  					=      has_spatial_attention_bias
  		snake_case__  					=      has_visual_segment_embedding
  		snake_case__  					=      (
  		    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  		)
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		self		)			->		Dict:
  		return {
  		    "MODEL.MASK_ON": True,
  		    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
  		    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
  		    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
  		    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
  		    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
  		    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
  		    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
  		    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
  		    "MODEL.POST_NMS_TOPK_TEST": 1000,
  		    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
  		    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
  		    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
  		    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
  		    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
  		    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
  		    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
  		    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
  		    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
  		    "MODEL.RESNETS.DEPTH": 101,
  		    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
  		    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
  		    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
  		    "MODEL.RESNETS.NUM_GROUPS": 32,
  		    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
  		    "MODEL.RESNETS.STRIDE_IN_1X1": False,
  		}
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      detectrona.config.get_cfg()
  		for k, v in self.detectrona_config_args.items():
  			snake_case__  					=      k.split('.'		)
  			snake_case__  					=      detectrona_config
  			for attribute in attributes[:-1]:
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  			setattr(UpperCamelCase_			,					attributes[-1]			,					UpperCamelCase_		)
  
  		return detectrona_config
  
  
  
  