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, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_DEFAULT_MEAN,
      IMAGENET_DEFAULT_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      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__		:       List[str]												=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		snake_case__		):
       __lowerCamelCase										=     ['pixel_values']
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = PILImageResampling.BICUBIC  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = 1 / 255  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	**_lowerCAmelCase  ,	):
            super().__init__(**_lowerCAmelCase		)
            UpperCAmelCase__			:     str										= crop_size if crop_size is not None else {'height': 256, 'width': 256}
            UpperCAmelCase__			:     Optional[Any]										= get_size_dict(_lowerCAmelCase  ,	param_name="""crop_size"""		)
            UpperCAmelCase__			:     int										= size if size is not None else {'height': 224, 'width': 224}
            UpperCAmelCase__			:     Union[str, Any]										= get_size_dict(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     str										= do_center_crop
            UpperCAmelCase__			:     List[str]										= crop_size
            UpperCAmelCase__			:     List[str]										= do_resize
            UpperCAmelCase__			:     Optional[Any]										= size
            UpperCAmelCase__			:     List[str]										= resample
            UpperCAmelCase__			:     Dict										= do_rescale
            UpperCAmelCase__			:     str										= rescale_factor
            UpperCAmelCase__			:     int										= do_normalize
            UpperCAmelCase__			:     List[str]										= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            UpperCAmelCase__			:     List[str]										= image_std if image_std is not None else IMAGENET_DEFAULT_STD
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     int										= self.size if size is None else size
            UpperCAmelCase__			:     Union[str, Any]										= get_size_dict(_lowerCAmelCase		)
            UpperCAmelCase__			:     Union[str, Any]										= get_size_dict(_lowerCAmelCase  ,	param_name="""crop_size"""		)
  
            UpperCAmelCase__			:     Dict										= get_image_size(_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= min(_lowerCAmelCase  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= (size['height'] / crop_size['height']) * min_dim
            UpperCAmelCase__			:     Dict										= (size['width'] / crop_size['width']) * min_dim
            return center_crop(_lowerCAmelCase  ,	size=(cropped_height, cropped_width)  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = PIL.Image.BICUBIC  ,	_lowerCAmelCase = None  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     Any										= get_size_dict(_lowerCAmelCase		)
            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(
                _lowerCAmelCase  ,	size=(size["""height"""], size["""width"""])  ,	resample=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None  ,	**_lowerCAmelCase  ,	):
            return rescale(_lowerCAmelCase  ,	scale=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None  ,	**_lowerCAmelCase  ,	):
            return normalize(_lowerCAmelCase  ,	mean=_lowerCAmelCase  ,	std=_lowerCAmelCase  ,	data_format=_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = ChannelDimension.FIRST  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     Union[str, Any]										= do_center_crop if do_center_crop is not None else self.do_center_crop
            UpperCAmelCase__			:     Dict										= crop_size if crop_size is not None else self.crop_size
            UpperCAmelCase__			:     int										= get_size_dict(_lowerCAmelCase  ,	param_name="""crop_size"""		)
            UpperCAmelCase__			:     Union[str, Any]										= do_resize if do_resize is not None else self.do_resize
            UpperCAmelCase__			:     Union[str, Any]										= size if size is not None else self.size
            UpperCAmelCase__			:     Dict										= get_size_dict(_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= resample if resample is not None else self.resample
            UpperCAmelCase__			:     List[str]										= do_rescale if do_rescale is not None else self.do_rescale
            UpperCAmelCase__			:     Dict										= rescale_factor if rescale_factor is not None else self.rescale_factor
            UpperCAmelCase__			:     List[str]										= do_normalize if do_normalize is not None else self.do_normalize
            UpperCAmelCase__			:     Tuple										= image_mean if image_mean is not None else self.image_mean
            UpperCAmelCase__			:     List[str]										= image_std if image_std is not None else self.image_std
  
            UpperCAmelCase__			:     str										= 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_center_crop and crop_size is None:
                 raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""		)
  
            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."""		)
  
            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("""Image mean and image standard deviation must be specified if do_normalize is True."""		)
  
            # All transformations expect numpy arrays.
            UpperCAmelCase__			:     Optional[Any]										= [to_numpy_array(_lowerCAmelCase		) for image in images]
  
            if do_center_crop:
                 UpperCAmelCase__			:     List[str]										= [self.center_crop(_lowerCAmelCase  ,	_lowerCAmelCase  ,	size=_lowerCAmelCase		) for image in images]
  
            if do_resize:
                 UpperCAmelCase__			:     Union[str, Any]										= [self.resize(image=_lowerCAmelCase  ,	size=_lowerCAmelCase  ,	resample=_lowerCAmelCase		) for image in images]
  
            if do_rescale:
                 UpperCAmelCase__			:     Any										= [self.rescale(image=_lowerCAmelCase  ,	scale=_lowerCAmelCase		) for image in images]
  
            if do_normalize:
                 UpperCAmelCase__			:     Optional[Any]										= [self.normalize(image=_lowerCAmelCase  ,	mean=_lowerCAmelCase  ,	std=_lowerCAmelCase		) for image in images]
  
            UpperCAmelCase__			:     Union[str, Any]										= [to_channel_dimension_format(_lowerCAmelCase  ,	_lowerCAmelCase		) for image in images]
  
            UpperCAmelCase__			:     Dict										= {'pixel_values': images}
            return BatchFeature(data=_lowerCAmelCase  ,	tensor_type=_lowerCAmelCase		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[Any]										= value
            UpperCAmelCase__			:     Node | None										= None
            UpperCAmelCase__			:     Node | None										= None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     42
  
  
  
  
       def __init__(							self		):
            UpperCAmelCase__			:     Any										= 0
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if node is None:
                 return
  
            if path_sum == self.target:
                 self.paths += 1
  
            if node.left:
                 self.depth_first_search(node.left  ,	path_sum + node.left.value		)
            if node.right:
                 self.depth_first_search(node.right  ,	path_sum + node.right.value		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
            if node is None:
                 return 0
            if target is not None:
                 UpperCAmelCase__			:     List[Any]										= target
  
            self.depth_first_search(_lowerCAmelCase  ,	node.value		)
            self.path_sum(node.left		)
            self.path_sum(node.right		)
  
            return self.paths
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
  
  
  
  
  