code: 
  
  
  
  
  
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __lowerCAmelCase							:			Optional[Any]				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Union[str, Any]				={
      "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
      "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
      "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
      "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
      "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
      "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """ernie"""
   def __init__(       self   :str		, lowercase_   :Any=3_05_22		, lowercase_   :int=7_68		, lowercase_   :Union[str, Any]=12		, lowercase_   :Optional[int]=12		, lowercase_   :Tuple=30_72		, lowercase_   :int="gelu"		, lowercase_   :Any=0.1		, lowercase_   :Optional[Any]=0.1		, lowercase_   :Optional[int]=5_12		, lowercase_   :Union[str, Any]=2		, lowercase_   :Dict=3		, lowercase_   :List[Any]=False		, lowercase_   :int=0.0_2		, lowercase_   :int=1E-12		, lowercase_   :Optional[int]=0		, lowercase_   :Optional[int]="absolute"		, lowercase_   :str=True		, lowercase_   :List[Any]=None		, **lowercase_   :Tuple		, )->							Union[str, Any]:
       super().__init__(pad_token_id=lowercase_		, **lowercase_							)
  
       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__       =    task_type_vocab_size
       A__       =    use_task_id
       A__       =    initializer_range
       A__       =    layer_norm_eps
       A__       =    position_embedding_type
       A__       =    use_cache
       A__       =    classifier_dropout
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   @property
   def   UpperCAmelCase_							(       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),
               ("task_type_ids", dynamic_axis),
           ]							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
      A__       =    min(img.size )
  
      if s < target_image_size:
          raise ValueError(F"min dim for image {s} < {target_image_size}" )
  
      A__       =    target_image_size / s
      A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
      A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
      A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
      A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
      return img
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      A__       =    2.0 * x - 1.0
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
      A__       =    x.detach().cpu()
      if process:
          A__       =    post_process_tensor(_lowerCamelCase )
      A__       =    x.numpy()
      if process:
          A__       =    (2_55 * x).astype(np.uinta )
      A__       =    Image.fromarray(_lowerCamelCase )
      if not x.mode == mode:
          A__       =    x.convert(_lowerCamelCase )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
      A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
      A__       =    (x + 1.0) / 2.0
      A__       =    x.permute(1		, 2		, 0 )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    post_process_tensor(x.squeeze() )
      return x.permute(2		, 0		, 1 ).unsqueeze(0 )
  
  
  
  