






"""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,
    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


SCREAMING_SNAKE_CASE						:						str					   =       logging.get_logger(__name__)


if is_vision_available():
					import PIL





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					["""pixel_values"""]


	def __init__(      self  ,					__a = True  ,					__a = None  ,					__a = PILImageResampling.BICUBIC  ,					__a = True  ,					__a = None  ,					__a = True  ,					__a = 1 / 255  ,					__a = True  ,					__a = None  ,					__a = None  ,					__a = True  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)
					A__							 =       size if size is not None else {'shortest_edge': 224}
					A__							 =       get_size_dict(__a  ,					default_to_square=__a						)
					A__							 =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
					A__							 =       get_size_dict(__a  ,					default_to_square=__a  ,					param_name='crop_size'						)

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


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


					"""simple docstring"""

					A__							 =       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()}'''						)
					A__							 =       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       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       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       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

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


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


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      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"""

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

					A__							 =       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:
									A__							 =       [convert_to_rgb(__a						) for image in images]

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

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

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

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

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

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

					A__							 =       {'pixel_values': images}
					return BatchFeature(data=__a  ,					tensor_type=__a						)










"""simple docstring"""






class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       name
					A__							 =       val


	def __str__(      self						):


					"""simple docstring"""

					return f'''{self.__class__.__name__}({self.name}, {self.val})'''






	def __lt__(      self  ,					__a						):


					"""simple docstring"""

					return self.val < other.val





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       {}
					A__							 =       {}
					A__							 =       self.build_heap(__a						)


	def __getitem__(      self  ,					__a						):


					"""simple docstring"""

					return self.get_value(__a						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return (idx - 1) // 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 1


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.heap_dict[key]


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       len(__a						) - 1
					A__							 =       self.get_parent_idx(__a						)

					for idx, i in enumerate(__a						):
									A__							 =       idx
									A__							 =       i.val

					for i in range(__a  ,					-1  ,					-1						):
									self.sift_down(__a  ,					__a						)
					return array


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					while True:
									A__							 =       self.get_left_child_idx(__a						)  # noqa: E741
									A__							 =       self.get_right_child_idx(__a						)

									A__							 =       idx
									if l < len(__a						) and array[l] < array[idx]:
													A__							 =       l
									if r < len(__a						) and array[r] < array[smallest]:
													A__							 =       r

									if smallest != idx:
													A__ ,			A__							 =       array[smallest], array[idx]
													(
													    (
													    A__
													) ,			(
													    A__
													) ,			
													)							 =       (
													    self.idx_of_element[array[smallest]],
													    self.idx_of_element[array[idx]],
													)
													A__							 =       smallest
									else:
													break


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       self.get_parent_idx(__a						)
					while p >= 0 and self.heap[p] > self.heap[idx]:
									A__ ,			A__							 =       self.heap[idx], self.heap[p]
									A__ ,			A__							 =       (
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									A__							 =       p
									A__							 =       self.get_parent_idx(__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.heap[0]


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__ ,			A__							 =       self.heap[-1], self.heap[0]
					A__ ,			A__							 =       (
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

					A__							 =       self.heap.pop()
					del self.idx_of_element[x]
					self.sift_down(0  ,					self.heap						)
					return x


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					self.heap.append(__a						)
					A__							 =       len(self.heap						) - 1
					A__							 =       node.val
					self.sift_up(len(self.heap						) - 1						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.heap						) == 0






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					A__							 =       new_value
					A__							 =       new_value
					self.sift_up(self.idx_of_element[node]						)





SCREAMING_SNAKE_CASE						:						List[str]					   =       Node('''R''', -1)
SCREAMING_SNAKE_CASE						:						Tuple					   =       Node('''B''', 6)
SCREAMING_SNAKE_CASE						:						List[Any]					   =       Node('''A''', 3)
SCREAMING_SNAKE_CASE						:						int					   =       Node('''X''', 1)
SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE						:						str					   =       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()


