"""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 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


__A         =				logging.get_logger(__name__)


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Dict							=				["pixel_values"]






    def __init__(     self					,    _UpperCAmelCase = True					,    _UpperCAmelCase = None					,    _UpperCAmelCase = True					,    _UpperCAmelCase = None					,    _UpperCAmelCase = PILImageResampling.BICUBIC					,    _UpperCAmelCase = True					,    _UpperCAmelCase = 1 / 255					,    _UpperCAmelCase = True					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       super().__init__(**_UpperCAmelCase							)
       lowercase__:     int      =   crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
       lowercase__:     List[str]      =   get_size_dict(_UpperCAmelCase					,    param_name='''crop_size'''							)
       lowercase__:     List[str]      =   size if size is not None else {'''height''': 224, '''width''': 224}
       lowercase__:     str      =   get_size_dict(_UpperCAmelCase							)

       lowercase__:     Tuple      =   do_center_crop
       lowercase__:     int      =   crop_size
       lowercase__:     str      =   do_resize
       lowercase__:     int      =   size
       lowercase__:     str      =   resample
       lowercase__:     Union[str, Any]      =   do_rescale
       lowercase__:     str      =   rescale_factor
       lowercase__:     int      =   do_normalize
       lowercase__:     List[Any]      =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowercase__:     Union[str, Any]      =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       lowercase__:     Optional[int]      =   self.size if size is None else size
       lowercase__:     int      =   get_size_dict(_UpperCAmelCase							)
       lowercase__:     Optional[int]      =   get_size_dict(_UpperCAmelCase					,    param_name='''crop_size'''							)

       lowercase__,				lowercase__:     Dict      =   get_image_size(_UpperCAmelCase							)
       lowercase__:     Dict      =   min(_UpperCAmelCase					,    _UpperCAmelCase							)
       lowercase__:     Optional[Any]      =   (size['''height'''] / crop_size['''height''']) * min_dim
       lowercase__:     Union[str, Any]      =   (size['''width'''] / crop_size['''width''']) * min_dim
       return center_crop(_UpperCAmelCase					,    size=(cropped_height, cropped_width)					,    data_format=_UpperCAmelCase					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = PIL.Image.BICUBIC					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       lowercase__:     Union[str, Any]      =   get_size_dict(_UpperCAmelCase							)
       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(
           _UpperCAmelCase					,    size=(size['''height'''], size['''width'''])					,    resample=_UpperCAmelCase					,    data_format=_UpperCAmelCase					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       return rescale(_UpperCAmelCase					,    scale=_UpperCAmelCase					,    data_format=_UpperCAmelCase					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       return normalize(_UpperCAmelCase					,    mean=_UpperCAmelCase					,    std=_UpperCAmelCase					,    data_format=_UpperCAmelCase					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = ChannelDimension.FIRST					,    **_UpperCAmelCase					,    ):
       lowercase__:     Tuple      =   do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__:     Optional[Any]      =   crop_size if crop_size is not None else self.crop_size
       lowercase__:     str      =   get_size_dict(_UpperCAmelCase					,    param_name='''crop_size'''							)
       lowercase__:     Optional[Any]      =   do_resize if do_resize is not None else self.do_resize
       lowercase__:     int      =   size if size is not None else self.size
       lowercase__:     int      =   get_size_dict(_UpperCAmelCase							)
       lowercase__:     Union[str, Any]      =   resample if resample is not None else self.resample
       lowercase__:     Dict      =   do_rescale if do_rescale is not None else self.do_rescale
       lowercase__:     Any      =   rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__:     str      =   do_normalize if do_normalize is not None else self.do_normalize
       lowercase__:     Dict      =   image_mean if image_mean is not None else self.image_mean
       lowercase__:     int      =   image_std if image_std is not None else self.image_std

       lowercase__:     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_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.
       lowercase__:     List[str]      =   [to_numpy_array(_UpperCAmelCase							) for image in images]

       if do_center_crop:
          lowercase__:     Dict      =   [self.center_crop(_UpperCAmelCase					,    _UpperCAmelCase					,    size=_UpperCAmelCase							) for image in images]

       if do_resize:
          lowercase__:     Dict      =   [self.resize(image=_UpperCAmelCase					,    size=_UpperCAmelCase					,    resample=_UpperCAmelCase							) for image in images]

       if do_rescale:
          lowercase__:     Union[str, Any]      =   [self.rescale(image=_UpperCAmelCase					,    scale=_UpperCAmelCase							) for image in images]

       if do_normalize:
          lowercase__:     List[Any]      =   [self.normalize(image=_UpperCAmelCase					,    mean=_UpperCAmelCase					,    std=_UpperCAmelCase							) for image in images]

       lowercase__:     List[str]      =   [to_channel_dimension_format(_UpperCAmelCase					,    _UpperCAmelCase							) for image in images]

       lowercase__:     List[Any]      =   {'''pixel_values''': images}
       return BatchFeature(data=_UpperCAmelCase					,    tensor_type=_UpperCAmelCase							)

"""simple docstring"""






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A         =				TypeVar("T", bound=bool)


class 					UpperCAmelCase		(Generic[T]						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase							):
       lowercase__:     T      =   value
       lowercase__:     RandomizedHeapNode[T] | None      =   None
       lowercase__:     RandomizedHeapNode[T] | None      =   None






    @property
    def _snake_case    (     self							):
       return self._value






    @staticmethod
    def _snake_case    (     _UpperCAmelCase					,    _UpperCAmelCase							):
       if not roota:
          return roota

       if not roota:
          return roota

       if roota.value > roota.value:
          lowercase__,				lowercase__:     List[str]      =   roota, roota

       if random.choice([True, False]							):
          lowercase__,				lowercase__:     Any      =   roota.right, roota.left

       lowercase__:     Optional[int]      =   RandomizedHeapNode.merge(roota.left					,    _UpperCAmelCase							)

       return roota



class 					UpperCAmelCase		(Generic[T]						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase = ()							):
       lowercase__:     RandomizedHeapNode[T] | None      =   None

       if data:
          for item in data:
             self.insert(_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     Any      =   RandomizedHeapNode.merge(self._root					,    RandomizedHeapNode(_UpperCAmelCase							)							)






    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   self.top()

       if self._root is None:
          return None

       lowercase__:     int      =   RandomizedHeapNode.merge(self._root.left					,    self._root.right							)

       return result






    def _snake_case    (     self							):
       if not self._root:
          raise IndexError('''Can\'t get top element for the empty heap.'''							)
       return self._root.value






    def _snake_case    (     self							):
       lowercase__:     Dict      =   None






    def _snake_case    (     self							):
       lowercase__:     Optional[int]      =   []
       while self:
          result.append(self.pop()							)

       return result






    def __bool__(     self							):
       return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()
