code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  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__      =							logging.get_logger(__name__)
  
  
  if is_vision_available():
  					import PIL
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							List[str]							= ['''pixel_values''']
  			def __init__(     self       : List[str] ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Dict[str, int] = None ,						lowerCAmelCase_       : PILImageResampling = PILImageResampling.BICUBIC ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Dict[str, int] = None ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Union[int, float] = 1 / 255 ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : bool = True ,						**lowerCAmelCase_       : List[str] ,						)			->		None:
  						super().__init__(**lowerCAmelCase_  )
  						__A=  size if size is not None else {'shortest_edge': 224}
  						__A=  get_size_dict(lowerCAmelCase_ ,						default_to_square=lowerCAmelCase_  )
  						__A=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
  						__A=  get_size_dict(lowerCAmelCase_  )
  
  						__A=  do_resize
  						__A=  size
  						__A=  resample
  						__A=  do_center_crop
  						__A=  crop_size
  						__A=  do_rescale
  						__A=  rescale_factor
  						__A=  do_normalize
  						__A=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  						__A=  image_std if image_std is not None else OPENAI_CLIP_STD
  						__A=  do_convert_rgb
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Dict[str, int] ,						lowerCAmelCase_       : PILImageResampling = PILImageResampling.BICUBIC ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : Optional[int] ,						)			->		np.ndarray:
  						__A=  get_size_dict(lowerCAmelCase_ ,						default_to_square=lowerCAmelCase_  )
  						__A=  get_resize_output_image_size(
  						    lowerCAmelCase_ ,						size=(size['height'], size['width']) ,						default_to_square=lowerCAmelCase_  )
  						return resize(lowerCAmelCase_ ,						size=lowerCAmelCase_ ,						resample=lowerCAmelCase_ ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Dict[str, int] ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : Optional[int] ,						)			->		np.ndarray:
  						__A=  get_size_dict(lowerCAmelCase_  )
  						return center_crop(lowerCAmelCase_ ,						size=(size['height'], size['width']) ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Union[int, float] ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : Optional[Any] ,						)			->		Any:
  						return rescale(lowerCAmelCase_ ,						scale=lowerCAmelCase_ ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Union[float, List[float]] ,						lowerCAmelCase_       : Union[float, List[float]] ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : List[Any] ,						)			->		np.ndarray:
  						return normalize(lowerCAmelCase_ ,						mean=lowerCAmelCase_ ,						std=lowerCAmelCase_ ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : ImageInput ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : Dict[str, int] = None ,						lowerCAmelCase_       : PILImageResampling = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : int = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : float = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : Optional[Union[str, TensorType]] = None ,						lowerCAmelCase_       : Optional[ChannelDimension] = ChannelDimension.FIRST ,						**lowerCAmelCase_       : Tuple ,						)			->		PIL.Image.Image:
  						__A=  do_resize if do_resize is not None else self.do_resize
  						__A=  size if size is not None else self.size
  						__A=  get_size_dict(lowerCAmelCase_ ,						default_to_square=lowerCAmelCase_  )
  						__A=  resample if resample is not None else self.resample
  						__A=  do_center_crop if do_center_crop is not None else self.do_center_crop
  						__A=  crop_size if crop_size is not None else self.crop_size
  						__A=  get_size_dict(lowerCAmelCase_  )
  						__A=  do_rescale if do_rescale is not None else self.do_rescale
  						__A=  rescale_factor if rescale_factor is not None else self.rescale_factor
  						__A=  do_normalize if do_normalize is not None else self.do_normalize
  						__A=  image_mean if image_mean is not None else self.image_mean
  						__A=  image_std if image_std is not None else self.image_std
  						__A=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
  						__A=  make_list_of_images(lowerCAmelCase_  )
  
  						if not valid_images(lowerCAmelCase_  ):
  									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=  [convert_to_rgb(lowerCAmelCase_  ) for image in images]
  
  						# All transformations expect numpy arrays.
  						__A=  [to_numpy_array(lowerCAmelCase_  ) for image in images]
  
  						if do_resize:
  									__A=  [self.resize(image=lowerCAmelCase_ ,						size=lowerCAmelCase_ ,						resample=lowerCAmelCase_  ) for image in images]
  
  						if do_center_crop:
  									__A=  [self.center_crop(image=lowerCAmelCase_ ,						size=lowerCAmelCase_  ) for image in images]
  
  						if do_rescale:
  									__A=  [self.rescale(image=lowerCAmelCase_ ,						scale=lowerCAmelCase_  ) for image in images]
  
  						if do_normalize:
  									__A=  [self.normalize(image=lowerCAmelCase_ ,						mean=lowerCAmelCase_ ,						std=lowerCAmelCase_  ) for image in images]
  
  						__A=  [to_channel_dimension_format(lowerCAmelCase_ ,						lowerCAmelCase_  ) for image in images]
  
  						__A=  {'pixel_values': images}
  						return BatchFeature(data=lowerCAmelCase_ ,						tensor_type=lowerCAmelCase_  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  type_vocab_size
  						__A=  initializer_range
  						__A=  layer_norm_eps
  						__A=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						if self.task == "multiple-choice":
  									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
  						else:
  									__A=  {0: 'batch', 1: 'sequence'}
  						return OrderedDict(
  						    [
  						        ('input_ids', dynamic_axis),
  						        ('attention_mask', dynamic_axis),
  						        ('token_type_ids', dynamic_axis),
  						    ]  )
  
  
  
  