



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a_						:     Union[str, Any]	     =		logging.get_logger(__name__)


if is_vision_available():
     import PIL





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['pixel_values']







     def __init__(self				,			__a = True				,			__a = None				,			__a = PILImageResampling.BICUBIC				,			__a = True				,			__a = None				,			__a = True				,			__a = 1 / 2_55				,			__a = True				,			__a = None				,			__a = None				,			__a = True				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**__a    )
           lowerCamelCase								=		size if size is not None else {"shortest_edge": 2_24}
           lowerCamelCase								=		get_size_dict(__a				,			default_to_square=__a    )
           lowerCamelCase								=		crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
           lowerCamelCase								=		get_size_dict(__a				,			default_to_square=__a				,			param_name="crop_size"    )

           lowerCamelCase								=		do_resize
           lowerCamelCase								=		size
           lowerCamelCase								=		resample
           lowerCamelCase								=		do_center_crop
           lowerCamelCase								=		crop_size
           lowerCamelCase								=		do_rescale
           lowerCamelCase								=		rescale_factor
           lowerCamelCase								=		do_normalize
           lowerCamelCase								=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowerCamelCase								=		image_std if image_std is not None else OPENAI_CLIP_STD
           lowerCamelCase								=		do_convert_rgb







     def 						_a      (self				,			__a				,			__a				,			__a = PILImageResampling.BICUBIC				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		get_size_dict(__a				,			default_to_square=__a    )
           if "shortest_edge" not in size:
                 raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
           lowerCamelCase								=		get_resize_output_image_size(__a				,			size=size["shortest_edge"]				,			default_to_square=__a    )
           return resize(__a				,			size=__a				,			resample=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		get_size_dict(__a    )
           if "height" not in size or "width" not in size:
                 raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
           return center_crop(__a				,			size=(size["height"], size["width"])				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



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







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



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







     def 						_a      (self				,			__a				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = ChannelDimension.FIRST				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		do_resize if do_resize is not None else self.do_resize
           lowerCamelCase								=		size if size is not None else self.size
           lowerCamelCase								=		get_size_dict(__a				,			param_name="size"				,			default_to_square=__a    )
           lowerCamelCase								=		resample if resample is not None else self.resample
           lowerCamelCase								=		do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCamelCase								=		crop_size if crop_size is not None else self.crop_size
           lowerCamelCase								=		get_size_dict(__a				,			param_name="crop_size"				,			default_to_square=__a    )
           lowerCamelCase								=		do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase								=		rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase								=		do_normalize if do_normalize is not None else self.do_normalize
           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								=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowerCamelCase								=		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_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."    )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 lowerCamelCase								=		[convert_to_rgb(__a    ) for image in images]

           # All transformations expect numpy arrays.
           lowerCamelCase								=		[to_numpy_array(__a    ) for image in images]

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

           if do_center_crop:
                 lowerCamelCase								=		[self.center_crop(image=__a				,			size=__a    ) for image in images]

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

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

           lowerCamelCase								=		[to_channel_dimension_format(__a				,			__a    ) for image in images]

           lowerCamelCase								=		{"pixel_values": images}
           return BatchFeature(data=__a				,			tensor_type=__a    )




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

a_						:     Optional[int]	     =		(3, 9, -1_1, 0, 7, 5, 1, -1)
a_						:     str	     =		(4, 6, 2, 0, 8, 1_0, 3, -2)





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		None
           for i in sorted(__a				,			reverse=__a    ):
                 lowerCamelCase								=		Node(__a				,			self.head    )







     def __iter__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.head
           while node:
                 yield node.data
                 lowerCamelCase								=		node.next_node







     def __len__(self    ):






           '''simple docstring'''



           return sum(1 for _ in self    )







     def __str__(self    ):






           '''simple docstring'''



           return " -> ".join([str(__a    ) for node in self]    )



def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     a_						:     Any	     =		SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))