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, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  __SCREAMING_SNAKE_CASE     :   Tuple					=						logging.get_logger(__name__)
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   List[str]     	=			['pixel_values']
  
  
  
  
     def __init__(		self							:Dict				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Dict[str, int] = None				,  UpperCamelCase__							:PILImageResampling = PIL.Image.NEAREST				,  UpperCamelCase__							:bool = False				,  UpperCamelCase__							:Dict[str, int] = None				,  UpperCamelCase__							:Union[int, float] = 1 / 255				,  UpperCamelCase__							:bool = False				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:bool = True				,  **UpperCamelCase__							:Optional[Any]				,  ):
            super().__init__(**UpperCamelCase__       )
            _a          =				size if size is not None else {"height": 346, "width": 346}
            _a          =				get_size_dict(UpperCamelCase__       )
            _a          =				crop_size if crop_size is not None else {"height": 289, "width": 289}
            _a          =				get_size_dict(UpperCamelCase__				,  param_name="crop_size"       )
  
            _a          =				do_resize
            _a          =				size
            _a          =				resample
            _a          =				do_center_crop
            _a          =				crop_size
            _a          =				do_rescale
            _a          =				rescale_factor
            _a          =				rescale_offset
            _a          =				do_normalize
            _a          =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _a          =				image_std if image_std is not None else IMAGENET_STANDARD_STD
            _a          =				include_top
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:Dict[str, int]				,  UpperCamelCase__							:PILImageResampling = PIL.Image.NEAREST				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:List[Any]				,  ):
            _a          =				get_size_dict(UpperCamelCase__       )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'       )
            return resize(
                UpperCamelCase__				,  size=(size["height"], size["width"])				,  resample=UpperCamelCase__				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:Dict[str, int]				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:Optional[int]				,  ):
            _a          =				get_size_dict(UpperCamelCase__       )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'       )
            return center_crop(UpperCamelCase__				,  size=(size["height"], size["width"])				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:Union[int, float]				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:Union[str, Any]				,  ):
            if offset:
                   _a          =				(image - 127.5) * scale
                   if data_format is not None:
                          _a          =				to_channel_dimension_format(UpperCamelCase__				,  UpperCamelCase__       )
                   _a          =				rescaled_image.astype(np.floataa       )
            else:
                   _a          =				rescale(UpperCamelCase__				,  scale=UpperCamelCase__				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
            return rescaled_image
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:Union[float, List[float]]				,  UpperCamelCase__							:Union[float, List[float]]				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:List[str]				,  ):
            return normalize(UpperCamelCase__				,  mean=UpperCamelCase__				,  std=UpperCamelCase__				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:ImageInput				,  UpperCamelCase__							:bool = None				,  UpperCamelCase__							:Dict[str, int] = None				,  UpperCamelCase__							:int=None				,  UpperCamelCase__							:bool = None				,  UpperCamelCase__							:Dict[str, int] = None				,  UpperCamelCase__							:bool = None				,  UpperCamelCase__							:float = None				,  UpperCamelCase__							:bool = None				,  UpperCamelCase__							:bool = None				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:bool = None				,  UpperCamelCase__							:Optional[Union[str, TensorType]] = None				,  UpperCamelCase__							:ChannelDimension = ChannelDimension.FIRST				,  **UpperCamelCase__							:List[Any]				,  ):
            _a          =				do_resize if do_resize is not None else self.do_resize
            _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          =				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          =				rescale_offset if rescale_offset is not None else self.rescale_offset
            _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          =				include_top if include_top is not None else self.include_top
  
            _a          =				size if size is not None else self.size
            _a          =				get_size_dict(UpperCamelCase__       )
            _a          =				crop_size if crop_size is not None else self.crop_size
            _a          =				get_size_dict(UpperCamelCase__				,  param_name="crop_size"       )
  
            _a          =				make_list_of_images(UpperCamelCase__       )
  
            if not valid_images(UpperCamelCase__       ):
                   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 or resample is None:
                   raise ValueError("Size and resample 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."       )
  
            # All transformations expect numpy arrays.
            _a          =				[to_numpy_array(UpperCamelCase__       ) for image in images]
  
            if do_resize:
                   _a          =				[self.resize(image=UpperCamelCase__				,  size=UpperCamelCase__				,  resample=UpperCamelCase__       ) for image in images]
  
            if do_center_crop:
                   _a          =				[self.center_crop(image=UpperCamelCase__				,  size=UpperCamelCase__       ) for image in images]
  
            if do_rescale:
                   _a          =				[self.rescale(image=UpperCamelCase__				,  scale=UpperCamelCase__				,  offset=UpperCamelCase__       ) for image in images]
  
            if do_normalize:
                   _a          =				[self.normalize(image=UpperCamelCase__				,  mean=UpperCamelCase__				,  std=UpperCamelCase__       ) for image in images]
  
            if include_top:
                   _a          =				[self.normalize(image=UpperCamelCase__				,  mean=[0, 0, 0]				,  std=UpperCamelCase__       ) for image in images]
  
            _a          =				[to_channel_dimension_format(UpperCamelCase__				,  UpperCamelCase__       ) for image in images]
  
            _a          =				{"pixel_values": images}
            return BatchFeature(data=UpperCamelCase__				,  tensor_type=UpperCamelCase__       )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
      # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
  }
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			'qdqbert'
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:List[Any]=30_522				,  UpperCamelCase__							:Optional[int]=768				,  UpperCamelCase__							:str=12				,  UpperCamelCase__							:Tuple=12				,  UpperCamelCase__							:Any=3_072				,  UpperCamelCase__							:Union[str, Any]="gelu"				,  UpperCamelCase__							:Dict=0.1				,  UpperCamelCase__							:Any=0.1				,  UpperCamelCase__							:Dict=512				,  UpperCamelCase__							:Optional[int]=2				,  UpperCamelCase__							:Any=0.02				,  UpperCamelCase__							:Optional[int]=1E-12				,  UpperCamelCase__							:str=True				,  UpperCamelCase__							:str=1				,  UpperCamelCase__							:List[Any]=0				,  UpperCamelCase__							:List[str]=2				,  **UpperCamelCase__							:Optional[Any]				,  ):
            super().__init__(pad_token_id=UpperCamelCase__				,  bos_token_id=UpperCamelCase__				,  eos_token_id=UpperCamelCase__				,  **UpperCamelCase__       )
  
            _a          =				vocab_size
            _a          =				max_position_embeddings
            _a          =				hidden_size
            _a          =				num_hidden_layers
            _a          =				num_attention_heads
            _a          =				intermediate_size
            _a          =				hidden_act
            _a          =				hidden_dropout_prob
            _a          =				attention_probs_dropout_prob
            _a          =				initializer_range
            _a          =				type_vocab_size
            _a          =				layer_norm_eps
            _a          =				use_cache
  