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 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, logging
  
  
  snake_case				:	Optional[int]							   =						logging.get_logger(__name__)
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					['pixel_values']
  
  
  
  
  
  
  
  			def __init__(       self		,     _a = True		,     _a = None		,     _a = PILImageResampling.BILINEAR		,     _a = True		,     _a = 1 / 255		,     _a = True		,     _a = None		,     _a = None		,     **_a		,     ):
  				super().__init__(**_a	)
  				__magic_name__	:	str			     =  size if size is not None else {"height": 224, "width": 224}
  				__magic_name__	:	List[str]			     =  get_size_dict(_a	)
  				__magic_name__	:	List[str]			     =  do_resize
  				__magic_name__	:	int			     =  do_rescale
  				__magic_name__	:	Optional[Any]			     =  do_normalize
  				__magic_name__	:	Union[str, Any]			     =  size
  				__magic_name__	:	List[str]			     =  resample
  				__magic_name__	:	List[Any]			     =  rescale_factor
  				__magic_name__	:	int			     =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  				__magic_name__	:	Optional[Any]			     =  image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a = PILImageResampling.BILINEAR		,     _a = None		,     **_a		,     ):
  				__magic_name__	:	Any			     =  get_size_dict(_a	)
  				if "height" not in size or "width" not in size:
  					raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''	)
  				return resize(
  				    _a		,     size=(size["height"], size["width"])		,     resample=_a		,     data_format=_a		,     **_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a = None		,     **_a	):
  				return rescale(_a		,     scale=_a		,     data_format=_a		,     **_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a = None		,     **_a		,     ):
  				return normalize(_a		,     mean=_a		,     std=_a		,     data_format=_a		,     **_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = ChannelDimension.FIRST		,     **_a		,     ):
  				__magic_name__	:	Tuple			     =  do_resize if do_resize is not None else self.do_resize
  				__magic_name__	:	Union[str, Any]			     =  do_rescale if do_rescale is not None else self.do_rescale
  				__magic_name__	:	Tuple			     =  do_normalize if do_normalize is not None else self.do_normalize
  				__magic_name__	:	Any			     =  resample if resample is not None else self.resample
  				__magic_name__	:	str			     =  rescale_factor if rescale_factor is not None else self.rescale_factor
  				__magic_name__	:	Optional[int]			     =  image_mean if image_mean is not None else self.image_mean
  				__magic_name__	:	List[Any]			     =  image_std if image_std is not None else self.image_std
  
  				__magic_name__	:	Optional[int]			     =  size if size is not None else self.size
  				__magic_name__	:	List[str]			     =  get_size_dict(_a	)
  
  				__magic_name__	:	str			     =  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_rescale and rescale_factor is None:
  					raise ValueError("Rescale factor must be specified if do_rescale is True."	)
  
  				# All transformations expect numpy arrays.
  				__magic_name__	:	Dict			     =  [to_numpy_array(_a	) for image in images]
  
  				if do_resize:
  					__magic_name__	:	Optional[int]			     =  [self.resize(image=_a		,     size=_a		,     resample=_a	) for image in images]
  
  				if do_rescale:
  					__magic_name__	:	int			     =  [self.rescale(image=_a		,     scale=_a	) for image in images]
  
  				if do_normalize:
  					__magic_name__	:	Optional[int]			     =  [self.normalize(image=_a		,     mean=_a		,     std=_a	) for image in images]
  
  				__magic_name__	:	Tuple			     =  [to_channel_dimension_format(_a		,     _a	) for image in images]
  
  				__magic_name__	:	Tuple			     =  {"pixel_values": images}
  				return BatchFeature(data=_a		,     tensor_type=_a	)
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  