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


lowerCAmelCase__					=				logging.get_logger(__name__)
class        a_				(		snake_case__		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['pixel_values']




 def __init__(    self      :						List[str]    ,					lowercase__      :						List[str] = True    ,					lowercase__      :						Tuple = None    ,					lowercase__      :						Dict = PILImageResampling.BILINEAR    ,					lowercase__      :						str = True    ,					lowercase__      :						Optional[int] = 1 / 255    ,					lowercase__      :						Tuple = True    ,					lowercase__      :						Optional[Any] = None    ,					lowercase__      :						List[str] = None    ,					**lowercase__      :						str    ,					):







        '''simple docstring'''



        super().__init__(**__snake_case)
        lowerCAmelCase__  			=      size if size is not None else {'height': 224, 'width': 224}
        lowerCAmelCase__  			=      get_size_dict(__snake_case)
        lowerCAmelCase__  			=      do_resize
        lowerCAmelCase__  			=      do_rescale
        lowerCAmelCase__  			=      do_normalize
        lowerCAmelCase__  			=      size
        lowerCAmelCase__  			=      resample
        lowerCAmelCase__  			=      rescale_factor
        lowerCAmelCase__  			=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase__  			=      image_std if image_std is not None else IMAGENET_STANDARD_STD




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Union[str, Any]    ,					lowercase__      :						Optional[int]    ,					lowercase__      :						Union[str, Any] = PILImageResampling.BILINEAR    ,					lowercase__      :						Dict = None    ,					**lowercase__      :						str    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      get_size_dict(__snake_case)
        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(
            __snake_case    ,					size=(size['height'], size['width'])    ,					resample=__snake_case    ,					data_format=__snake_case    ,					**__snake_case)




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Optional[int]    ,					lowercase__      :						Union[str, Any]    ,					lowercase__      :						int = None    ,					**lowercase__      :						Optional[int]):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						str    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Tuple = None    ,					**lowercase__      :						Any    ,					):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Optional[int]    ,					lowercase__      :						Dict = None    ,					lowercase__      :						List[str] = None    ,					lowercase__      :						int = None    ,					lowercase__      :						List[str] = None    ,					lowercase__      :						List[str] = None    ,					lowercase__      :						Union[str, Any] = None    ,					lowercase__      :						int = None    ,					lowercase__      :						List[str] = None    ,					lowercase__      :						Dict = None    ,					lowercase__      :						Optional[int] = ChannelDimension.FIRST    ,					**lowercase__      :						Optional[Any]    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__  			=      do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__  			=      do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__  			=      resample if resample is not None else self.resample
        lowerCAmelCase__  			=      rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__  			=      image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__  			=      image_std if image_std is not None else self.image_std

        lowerCAmelCase__  			=      size if size is not None else self.size
        lowerCAmelCase__  			=      get_size_dict(__snake_case)

        lowerCAmelCase__  			=      make_list_of_images(__snake_case)

        if not valid_images(__snake_case):
               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.
        lowerCAmelCase__  			=      [to_numpy_array(__snake_case) for image in images]

        if do_resize:
               lowerCAmelCase__  			=      [self.resize(image=__snake_case    ,					size=__snake_case    ,					resample=__snake_case) for image in images]

        if do_rescale:
               lowerCAmelCase__  			=      [self.rescale(image=__snake_case    ,					scale=__snake_case) for image in images]

        if do_normalize:
               lowerCAmelCase__  			=      [self.normalize(image=__snake_case    ,					mean=__snake_case    ,					std=__snake_case) for image in images]

        lowerCAmelCase__  			=      [to_channel_dimension_format(__snake_case    ,					__snake_case) for image in images]

        lowerCAmelCase__  			=      {'pixel_values': images}
        return BatchFeature(data=__snake_case    ,					tensor_type=__snake_case)

def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
