




'''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


_lowerCAmelCase				     = logging.get_logger(__name__)



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    ["pixel_values"]
				def __init__(self			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = PILImageResampling.BICUBIC			,  _UpperCAmelCase = True			,  _UpperCAmelCase = 1 / 2_5_5			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  **_UpperCAmelCase			,  )     ->	None:
							super().__init__(**_UpperCAmelCase      )
							__UpperCamelCase				:      List[str]									=       crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
							__UpperCamelCase				:      Any									=       get_size_dict(_UpperCAmelCase			,  param_name="crop_size"      )
							__UpperCamelCase				:      Optional[Any]									=       size if size is not None else {"height": 2_2_4, "width": 2_2_4}
							__UpperCamelCase				:      Tuple									=       get_size_dict(_UpperCAmelCase      )

							__UpperCamelCase				:      str									=       do_center_crop
							__UpperCamelCase				:      Union[str, Any]									=       crop_size
							__UpperCamelCase				:      Any									=       do_resize
							__UpperCamelCase				:      Optional[int]									=       size
							__UpperCamelCase				:      List[str]									=       resample
							__UpperCamelCase				:      int									=       do_rescale
							__UpperCamelCase				:      str									=       rescale_factor
							__UpperCamelCase				:      int									=       do_normalize
							__UpperCamelCase				:      Any									=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							__UpperCamelCase				:      Optional[Any]									=       image_std if image_std is not None else IMAGENET_DEFAULT_STD
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  **_UpperCAmelCase			,  )     ->	np.ndarray:
							__UpperCamelCase				:      Any									=       self.size if size is None else size
							__UpperCamelCase				:      List[Any]									=       get_size_dict(_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       get_size_dict(_UpperCAmelCase			,  param_name="crop_size"      )

							__UpperCamelCase		, __UpperCamelCase				:      Optional[int]									=       get_image_size(_UpperCAmelCase      )
							__UpperCamelCase				:      int									=       min(_UpperCAmelCase			,  _UpperCAmelCase      )
							__UpperCamelCase				:      Dict									=       (size["height"] / crop_size["height"]) * min_dim
							__UpperCamelCase				:      Optional[Any]									=       (size["width"] / crop_size["width"]) * min_dim
							return center_crop(_UpperCAmelCase			,  size=(cropped_height, cropped_width)			,  data_format=_UpperCAmelCase			,  **_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = PIL.Image.BICUBIC			,  _UpperCAmelCase = None			,  **_UpperCAmelCase			,  )     ->	np.ndarray:
							__UpperCamelCase				:      int									=       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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = None			,  **_UpperCAmelCase			,  )     ->	Tuple:
							return rescale(_UpperCAmelCase			,  scale=_UpperCAmelCase			,  data_format=_UpperCAmelCase			,  **_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = None			,  **_UpperCAmelCase			,  )     ->	np.ndarray:
							return normalize(_UpperCAmelCase			,  mean=_UpperCAmelCase			,  std=_UpperCAmelCase			,  data_format=_UpperCAmelCase			,  **_UpperCAmelCase      )





				def 	a_	(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			,  )     ->	PIL.Image.Image:
							__UpperCamelCase				:      List[Any]									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__UpperCamelCase				:      int									=       crop_size if crop_size is not None else self.crop_size
							__UpperCamelCase				:      int									=       get_size_dict(_UpperCAmelCase			,  param_name="crop_size"      )
							__UpperCamelCase				:      Union[str, Any]									=       do_resize if do_resize is not None else self.do_resize
							__UpperCamelCase				:      Union[str, Any]									=       size if size is not None else self.size
							__UpperCamelCase				:      Dict									=       get_size_dict(_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       resample if resample is not None else self.resample
							__UpperCamelCase				:      Tuple									=       do_rescale if do_rescale is not None else self.do_rescale
							__UpperCamelCase				:      str									=       rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCamelCase				:      Optional[Any]									=       do_normalize if do_normalize is not None else self.do_normalize
							__UpperCamelCase				:      Any									=       image_mean if image_mean is not None else self.image_mean
							__UpperCamelCase				:      List[str]									=       image_std if image_std is not None else self.image_std

							__UpperCamelCase				:      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.
							__UpperCamelCase				:      str									=       [to_numpy_array(_UpperCAmelCase      ) for image in images]

							if do_center_crop:
										__UpperCamelCase				:      Optional[Any]									=       [self.center_crop(_UpperCAmelCase			,  _UpperCAmelCase			,  size=_UpperCAmelCase      ) for image in images]

							if do_resize:
										__UpperCamelCase				:      List[Any]									=       [self.resize(image=_UpperCAmelCase			,  size=_UpperCAmelCase			,  resample=_UpperCAmelCase      ) for image in images]

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

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

							__UpperCamelCase				:      Optional[int]									=       [to_channel_dimension_format(_UpperCAmelCase			,  _UpperCAmelCase      ) for image in images]

							__UpperCamelCase				:      Dict									=       {"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

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

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

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





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


if __name__ == "__main__":
				import doctest

				doctest.testmod()



