





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,
    get_resize_output_image_size,
    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, is_vision_available, logging


if is_vision_available():
    import PIL


_UpperCAmelCase       : str			     =       logging.get_logger(__name__)






class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""pixel_values"""]
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   float = None    ,  UpperCAmelCase						:   PILImageResampling = PILImageResampling.BILINEAR    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Union[int, float] = 1 / 255    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  UpperCAmelCase						:   Optional[Union[float, List[float]]] = None    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			None:
     super().__init__(**UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	size if size is not None else {'shortest_edge': 384}
     lowerCamelCase__					:   Optional[int]      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	do_resize
     lowerCamelCase__					:   Union[str, Any]      				=	size
     # Default value set here for backwards compatibility where the value in config is None
     lowerCamelCase__					:   List[str]      				=	crop_pct if crop_pct is not None else 224 / 256
     lowerCamelCase__					:   List[str]      				=	resample
     lowerCamelCase__					:   str      				=	do_rescale
     lowerCamelCase__					:   str      				=	rescale_factor
     lowerCamelCase__					:   Any      				=	do_normalize
     lowerCamelCase__					:   Union[str, Any]      				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__					:   str      				=	image_std if image_std is not None else IMAGENET_STANDARD_STD
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Dict[str, int]    ,  UpperCAmelCase						:   float    ,  UpperCAmelCase						:   PILImageResampling = PILImageResampling.BICUBIC    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   List[str]    ,  )    ->			np.ndarray:
     lowerCamelCase__					:   Union[str, Any]      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
     if "shortest_edge" not in size:
         raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""			)
     lowerCamelCase__					:   Optional[int]      				=	size['shortest_edge']

     if shortest_edge < 384:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         lowerCamelCase__					:   Optional[int]      				=	int(shortest_edge / crop_pct			)
         lowerCamelCase__					:   Optional[Any]      				=	get_resize_output_image_size(UpperCAmelCase    ,  size=UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)
         lowerCamelCase__					:   Tuple      				=	resize(image=UpperCAmelCase    ,  size=UpperCAmelCase    ,  resample=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=UpperCAmelCase    ,  size=(shortest_edge, shortest_edge)    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
     else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             UpperCAmelCase    ,  size=(shortest_edge, shortest_edge)    ,  resample=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Union[int, float]    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   List[Any]    ,  )    ->			str:
     return rescale(UpperCAmelCase    ,  scale=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   np.ndarray    ,  UpperCAmelCase						:   Union[float, List[float]]    ,  UpperCAmelCase						:   Union[float, List[float]]    ,  UpperCAmelCase						:   Optional[Union[str, ChannelDimension]] = None    ,  **UpperCAmelCase						:   Any    ,  )    ->			np.ndarray:
     return normalize(UpperCAmelCase    ,  mean=UpperCAmelCase    ,  std=UpperCAmelCase    ,  data_format=UpperCAmelCase    ,  **UpperCAmelCase			)







 def   A_			(   self						:   int    ,  UpperCAmelCase						:   ImageInput    ,  UpperCAmelCase						:   bool = None    ,  UpperCAmelCase						:   Dict[str, int] = None    ,  UpperCAmelCase						:   float = None    ,  UpperCAmelCase						:   PILImageResampling = None    ,  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						:   ChannelDimension = ChannelDimension.FIRST    ,  **UpperCAmelCase						:   Optional[int]    ,  )    ->			PIL.Image.Image:
     lowerCamelCase__					:   Any      				=	do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__					:   List[Any]      				=	crop_pct if crop_pct is not None else self.crop_pct
     lowerCamelCase__					:   Tuple      				=	resample if resample is not None else self.resample
     lowerCamelCase__					:   Optional[Any]      				=	do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__					:   str      				=	rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__					:   List[str]      				=	do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__					:   Tuple      				=	image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__					:   List[str]      				=	image_std if image_std is not None else self.image_std

     lowerCamelCase__					:   Any      				=	size if size is not None else self.size
     lowerCamelCase__					:   Tuple      				=	get_size_dict(UpperCAmelCase    ,  default_to_square=UpperCAmelCase			)

     lowerCamelCase__					:   List[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 or resample is None:
         raise ValueError('Size and resample must be specified if do_resize is True.'			)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
         raise ValueError('crop_pct must be specified if size < 384.'			)

     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.'			)

     # All transformations expect numpy arrays.
     lowerCamelCase__					:   Union[str, Any]      				=	[to_numpy_array(UpperCAmelCase			) for image in images]

     if do_resize:
         lowerCamelCase__					:   Optional[Any]      				=	[self.resize(image=UpperCAmelCase    ,  size=UpperCAmelCase    ,  crop_pct=UpperCAmelCase    ,  resample=UpperCAmelCase			) for image in images]

     if do_rescale:
         lowerCamelCase__					:   List[str]      				=	[self.rescale(image=UpperCAmelCase    ,  scale=UpperCAmelCase			) for image in images]

     if do_normalize:
         lowerCamelCase__					:   Union[str, Any]      				=	[self.normalize(image=UpperCAmelCase    ,  mean=UpperCAmelCase    ,  std=UpperCAmelCase			) for image in images]

     lowerCamelCase__					:   Dict      				=	[to_channel_dimension_format(UpperCAmelCase    ,  UpperCAmelCase			) for image in images]

     lowerCamelCase__					:   Optional[int]      				=	{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase    ,  tensor_type=UpperCAmelCase			)












from __future__ import annotations

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

_UpperCAmelCase       : Union[str, Any]			     =       (3, 9, -11, 0, 7, 5, 1, -1)
_UpperCAmelCase       : Union[str, Any]			     =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	42
 UpperCAmelCase__									=	42


class      lowerCAmelCase							:
 def __init__(   self						:   Any    ,  UpperCAmelCase						:   Iterable[int]			)    ->			None:
     lowerCamelCase__					:   Node | None      				=	None
     for i in sorted(UpperCAmelCase    ,  reverse=UpperCAmelCase			):
         lowerCamelCase__					:   Tuple      				=	Node(UpperCAmelCase    ,  self.head			)
 def __iter__(   self						:   Optional[int]			)    ->			Iterator[int]:
     lowerCamelCase__					:   str      				=	self.head
     while node:
         yield node.data
         lowerCamelCase__					:   List[str]      				=	node.next_node
 def __len__(   self						:   List[str]			)    ->			int:
     return sum(1 for _ in self			)







 def __str__(   self						:   Any			)    ->			str:
     return " -> ".join([str(UpperCAmelCase			) for node in self]			)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  SortedLinkedList:
    return SortedLinkedList(list(_UpperCAmelCase				) + list(_UpperCAmelCase				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _UpperCAmelCase       : Optional[Any]			     =       SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





