'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase       :     int			=    logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			["pixel_values"]





    def __init__(					self     :						List[Any]							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Dict[str, int] = None							,							UpperCAmelCase_     :						PILImageResampling = PILImageResampling.BILINEAR							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						bool = False							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Union[int, float] = 1 / 2_5_5							,							UpperCAmelCase_     :						bool = True							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							**UpperCAmelCase_     :						Dict							,							):


         """simple docstring"""







         super().__init__(**UpperCamelCase_)

         a					:							Union[str, Any]							= size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
         if isinstance(UpperCamelCase_							,							(tuple, list)):
              # The previous feature extractor size parameter was in (width, height) format
              a					:							Any							= size[::-1]
         a					:							Optional[Any]							= get_size_dict(UpperCamelCase_)

         a					:							str							= do_resize
         a					:							Optional[Any]							= size
         a					:							List[str]							= resample
         a					:							List[str]							= do_thumbnail
         a					:							Tuple							= do_align_long_axis
         a					:							Any							= do_pad
         a					:							List[Any]							= do_rescale
         a					:							List[str]							= rescale_factor
         a					:							Union[str, Any]							= do_normalize
         a					:							str							= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         a					:							List[Any]							= image_std if image_std is not None else IMAGENET_STANDARD_STD





    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Dict[str, int]							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None):


         """simple docstring"""







         a					:							int							= get_image_size(UpperCamelCase_)
         a					:							Tuple							= size['height'], size['width']

         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
              a					:							str							= np.rotaa(UpperCamelCase_							,							3)

         if data_format is not None:
              a					:							Any							= to_channel_dimension_format(UpperCamelCase_							,							UpperCamelCase_)

         return image





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Dict[str, int]							,							UpperCAmelCase_     :						bool = False							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							):


         """simple docstring"""







         a					:							List[Any]							= size['height'], size['width']
         a					:							int							= get_image_size(UpperCamelCase_)

         a					:							Optional[int]							= output_width - input_width
         a					:							Optional[Any]							= output_height - input_height

         if random_padding:
              a					:							Any							= np.random.randint(low=0							,							high=delta_height + 1)
              a					:							List[Any]							= np.random.randint(low=0							,							high=delta_width + 1)
         else:
              a					:							Union[str, Any]							= delta_height // 2
              a					:							Dict							= delta_width // 2

         a					:							int							= delta_height - pad_top
         a					:							Dict							= delta_width - pad_left

         a					:							Union[str, Any]							= ((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(UpperCamelCase_							,							UpperCamelCase_							,							data_format=UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							*UpperCAmelCase_     :						Optional[int]							,							**UpperCAmelCase_     :						Any):


         """simple docstring"""







         logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.')
         return self.pad_image(*UpperCamelCase_							,							**UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Dict[str, int]							,							UpperCAmelCase_     :						PILImageResampling = PILImageResampling.BICUBIC							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						Union[str, Any]							,							):


         """simple docstring"""







         a					:							List[str]							= get_image_size(UpperCamelCase_)
         a					:							List[str]							= size['height'], size['width']

         # We always resize to the smallest of either the input or output size.
         a					:							str							= min(UpperCamelCase_							,							UpperCamelCase_)
         a					:							Optional[Any]							= min(UpperCamelCase_							,							UpperCamelCase_)

         if height == input_height and width == input_width:
              return image

         if input_height > input_width:
              a					:							int							= int(input_width * height / input_height)
         elif input_width > input_height:
              a					:							Dict							= int(input_height * width / input_width)

         return resize(
             UpperCamelCase_							,							size=(height, width)							,							resample=UpperCamelCase_							,							reducing_gap=2.0							,							data_format=UpperCamelCase_							,							**UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Dict[str, int]							,							UpperCAmelCase_     :						PILImageResampling = PILImageResampling.BICUBIC							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						List[str]							,							):


         """simple docstring"""







         a					:							List[Any]							= get_size_dict(UpperCamelCase_)
         a					:							Any							= min(size['height']							,							size['width'])
         a					:							Optional[int]							= get_resize_output_image_size(UpperCamelCase_							,							size=UpperCamelCase_							,							default_to_square=UpperCamelCase_)
         a					:							List[str]							= resize(UpperCamelCase_							,							size=UpperCamelCase_							,							resample=UpperCamelCase_							,							data_format=UpperCamelCase_							,							**UpperCamelCase_)
         return resized_image





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Union[int, float]							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         return rescale(UpperCamelCase_							,							scale=UpperCamelCase_							,							data_format=UpperCamelCase_							,							**UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						str							,							UpperCAmelCase_     :						np.ndarray							,							UpperCAmelCase_     :						Union[float, List[float]]							,							UpperCAmelCase_     :						Union[float, List[float]]							,							UpperCAmelCase_     :						Optional[Union[str, ChannelDimension]] = None							,							**UpperCAmelCase_     :						Optional[Any]							,							):


         """simple docstring"""







         return normalize(UpperCamelCase_							,							mean=UpperCamelCase_							,							std=UpperCamelCase_							,							data_format=UpperCamelCase_							,							**UpperCamelCase_)







    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						ImageInput							,							UpperCAmelCase_     :						bool = None							,							UpperCAmelCase_     :						Dict[str, int] = None							,							UpperCAmelCase_     :						PILImageResampling = None							,							UpperCAmelCase_     :						bool = None							,							UpperCAmelCase_     :						bool = None							,							UpperCAmelCase_     :						bool = None							,							UpperCAmelCase_     :						bool = False							,							UpperCAmelCase_     :						bool = None							,							UpperCAmelCase_     :						float = None							,							UpperCAmelCase_     :						bool = None							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							UpperCAmelCase_     :						Optional[Union[float, List[float]]] = None							,							UpperCAmelCase_     :						Optional[Union[str, TensorType]] = None							,							UpperCAmelCase_     :						Optional[ChannelDimension] = ChannelDimension.FIRST							,							**UpperCAmelCase_     :						Optional[Any]							,							):


         """simple docstring"""







         a					:							List[Any]							= do_resize if do_resize is not None else self.do_resize
         a					:							str							= size if size is not None else self.size
         if isinstance(UpperCamelCase_							,							(tuple, list)):
              # Previous feature extractor had size in (width, height) format
              a					:							int							= size[::-1]
         a					:							Union[str, Any]							= get_size_dict(UpperCamelCase_)
         a					:							int							= resample if resample is not None else self.resample
         a					:							List[Any]							= do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         a					:							Dict							= do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         a					:							Union[str, Any]							= do_pad if do_pad is not None else self.do_pad
         a					:							Union[str, Any]							= do_rescale if do_rescale is not None else self.do_rescale
         a					:							int							= rescale_factor if rescale_factor is not None else self.rescale_factor
         a					:							int							= do_normalize if do_normalize is not None else self.do_normalize
         a					:							int							= image_mean if image_mean is not None else self.image_mean
         a					:							List[Any]							= image_std if image_std is not None else self.image_std

         a					:							Union[str, Any]							= 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:
              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_pad and size is None:
              raise ValueError('Size must be specified if do_pad 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					:							Any							= [to_numpy_array(UpperCamelCase_) for image in images]

         if do_align_long_axis:
              a					:							Optional[Any]							= [self.align_long_axis(UpperCamelCase_							,							size=UpperCamelCase_) for image in images]

         if do_resize:
              a					:							str							= [self.resize(image=UpperCamelCase_							,							size=UpperCamelCase_							,							resample=UpperCamelCase_) for image in images]

         if do_thumbnail:
              a					:							Union[str, Any]							= [self.thumbnail(image=UpperCamelCase_							,							size=UpperCamelCase_) for image in images]

         if do_pad:
              a					:							Optional[Any]							= [self.pad_image(image=UpperCamelCase_							,							size=UpperCamelCase_							,							random_padding=UpperCamelCase_) for image in images]

         if do_rescale:
              a					:							Optional[Any]							= [self.rescale(image=UpperCamelCase_							,							scale=UpperCamelCase_) for image in images]

         if do_normalize:
              a					:							List[str]							= [self.normalize(image=UpperCamelCase_							,							mean=UpperCamelCase_							,							std=UpperCamelCase_) for image in images]

         a					:							List[str]							= [to_channel_dimension_format(UpperCamelCase_							,							UpperCamelCase_) for image in images]

         a					:							Tuple							= {'pixel_values': images}
         return BatchFeature(data=UpperCamelCase_							,							tensor_type=UpperCamelCase_)






'''simple docstring'''

from __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						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					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

						self.depth_first_search(UpperCamelCase_  ,	node.value       )
						self.path_sum(node.left       )
						self.path_sum(node.right       )

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
