code: 
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      center_crop,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  UpperCAmelCase_       :				str		   =      logging.get_logger(__name__)
  
  
  if is_vision_available():
  			import PIL
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Tuple    				=							['''pixel_values''']
  
  
  
  
  	def __init__(    self	:    Any				,			__A	:    bool = True				,			__A	:    Dict[str, int] = None				,			__A	:    PILImageResampling = PILImageResampling.BICUBIC				,			__A	:    bool = True				,			__A	:    Dict[str, int] = None				,			__A	:    bool = True				,			__A	:    Union[int, float] = 1 / 255				,			__A	:    bool = True				,			__A	:    Optional[Union[float, List[float]]] = None				,			__A	:    Optional[Union[float, List[float]]] = None				,			__A	:    bool = True				,			**__A	:    Optional[Any]				,			):
  		super().__init__(**__A       )
  		__A   : Dict								= size if size is not None else {"""shortest_edge""": 224}
  		__A   : Tuple								= get_size_dict(__A				,			default_to_square=__A       )
  		__A   : Optional[int]								= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
  		__A   : Dict								= get_size_dict(__A       )
  
  		__A   : Optional[int]								= do_resize
  		__A   : str								= size
  		__A   : List[str]								= resample
  		__A   : List[str]								= do_center_crop
  		__A   : List[Any]								= crop_size
  		__A   : Dict								= do_rescale
  		__A   : Any								= rescale_factor
  		__A   : Union[str, Any]								= do_normalize
  		__A   : List[str]								= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  		__A   : Any								= image_std if image_std is not None else OPENAI_CLIP_STD
  		__A   : List[Any]								= do_convert_rgb
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]				,			__A	:    np.ndarray				,			__A	:    Dict[str, int]				,			__A	:    PILImageResampling = PILImageResampling.BICUBIC				,			__A	:    Optional[Union[str, ChannelDimension]] = None				,			**__A	:    List[str]				,			):
  		__A   : Union[str, Any]								= get_size_dict(__A				,			default_to_square=__A       )
  		__A   : List[Any]								= get_resize_output_image_size(
  		    __A				,			size=(size["""height"""], size["""width"""])				,			default_to_square=__A       )
  		return resize(__A				,			size=__A				,			resample=__A				,			data_format=__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    np.ndarray				,			__A	:    Dict[str, int]				,			__A	:    Optional[Union[str, ChannelDimension]] = None				,			**__A	:    str				,			):
  		__A   : Dict								= get_size_dict(__A       )
  		return center_crop(__A				,			size=(size["""height"""], size["""width"""])				,			data_format=__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    np.ndarray				,			__A	:    Union[int, float]				,			__A	:    Optional[Union[str, ChannelDimension]] = None				,			**__A	:    Optional[Any]				,			):
  		return rescale(__A				,			scale=__A				,			data_format=__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    np.ndarray				,			__A	:    Union[float, List[float]]				,			__A	:    Union[float, List[float]]				,			__A	:    Optional[Union[str, ChannelDimension]] = None				,			**__A	:    Dict				,			):
  		return normalize(__A				,			mean=__A				,			std=__A				,			data_format=__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    ImageInput				,			__A	:    bool = None				,			__A	:    Dict[str, int] = None				,			__A	:    PILImageResampling = None				,			__A	:    bool = None				,			__A	:    int = None				,			__A	:    bool = None				,			__A	:    float = None				,			__A	:    bool = None				,			__A	:    Optional[Union[float, List[float]]] = None				,			__A	:    Optional[Union[float, List[float]]] = None				,			__A	:    bool = None				,			__A	:    Optional[Union[str, TensorType]] = None				,			__A	:    Optional[ChannelDimension] = ChannelDimension.FIRST				,			**__A	:    int				,			):
  		__A   : Optional[int]								= do_resize if do_resize is not None else self.do_resize
  		__A   : List[str]								= size if size is not None else self.size
  		__A   : int								= get_size_dict(__A				,			default_to_square=__A       )
  		__A   : List[Any]								= resample if resample is not None else self.resample
  		__A   : Optional[Any]								= do_center_crop if do_center_crop is not None else self.do_center_crop
  		__A   : List[Any]								= crop_size if crop_size is not None else self.crop_size
  		__A   : List[Any]								= get_size_dict(__A       )
  		__A   : str								= do_rescale if do_rescale is not None else self.do_rescale
  		__A   : Optional[int]								= rescale_factor if rescale_factor is not None else self.rescale_factor
  		__A   : Any								= do_normalize if do_normalize is not None else self.do_normalize
  		__A   : Optional[Any]								= image_mean if image_mean is not None else self.image_mean
  		__A   : List[Any]								= image_std if image_std is not None else self.image_std
  		__A   : Union[str, Any]								= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
  		__A   : Tuple								= make_list_of_images(__A       )
  
  		if not valid_images(__A       ):
  			raise ValueError(
  			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
  			    """torch.Tensor, tf.Tensor or jax.ndarray."""       )
  
  		if do_resize and size is None:
  			raise ValueError("""Size must be specified if do_resize is True."""       )
  
  		if do_center_crop and crop_size is None:
  			raise ValueError("""Crop size must be specified if do_center_crop is True."""       )
  
  		if do_rescale and rescale_factor is None:
  			raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )
  
  		if do_normalize and (image_mean is None or image_std is None):
  			raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )
  
  		# PIL RGBA images are converted to RGB
  		if do_convert_rgb:
  			__A   : str								= [convert_to_rgb(__A       ) for image in images]
  
  		# All transformations expect numpy arrays.
  		__A   : Optional[Any]								= [to_numpy_array(__A       ) for image in images]
  
  		if do_resize:
  			__A   : int								= [self.resize(image=__A				,			size=__A				,			resample=__A       ) for image in images]
  
  		if do_center_crop:
  			__A   : Tuple								= [self.center_crop(image=__A				,			size=__A       ) for image in images]
  
  		if do_rescale:
  			__A   : List[Any]								= [self.rescale(image=__A				,			scale=__A       ) for image in images]
  
  		if do_normalize:
  			__A   : Optional[Any]								= [self.normalize(image=__A				,			mean=__A				,			std=__A       ) for image in images]
  
  		__A   : Union[str, Any]								= [to_channel_dimension_format(__A				,			__A       ) for image in images]
  
  		__A   : Optional[int]								= {"""pixel_values""": images}
  		return BatchFeature(data=__A				,			tensor_type=__A       )
  
  
  
  
  
  
style_context: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Any		   =      {
      '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''plbart'''
  	_lowercase		:      str    				=							['''past_key_values''']
  	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
  		__A   : List[Any]								= vocab_size
  		__A   : str								= max_position_embeddings
  		__A   : Union[str, Any]								= d_model
  		__A   : List[str]								= encoder_ffn_dim
  		__A   : Optional[Any]								= encoder_layers
  		__A   : Tuple								= encoder_attention_heads
  		__A   : List[Any]								= decoder_ffn_dim
  		__A   : Optional[int]								= decoder_layers
  		__A   : Any								= decoder_attention_heads
  		__A   : Optional[int]								= dropout
  		__A   : Any								= attention_dropout
  		__A   : Tuple								= activation_dropout
  		__A   : List[Any]								= activation_function
  		__A   : Tuple								= init_std
  		__A   : List[Any]								= encoder_layerdrop
  		__A   : str								= decoder_layerdrop
  		__A   : Union[str, Any]								= classifier_dropout
  		__A   : Optional[Any]								= use_cache
  		__A   : List[str]								= encoder_layers
  		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
  		super().__init__(
  		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		return OrderedDict(
  		    [
  		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
  		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
  		    ]       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		if self.use_past:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  		else:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  
  
  
  
  
  