
'''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


UpperCamelCase_   	=	logging.get_logger(__name__)


if is_vision_available():
					import PIL

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Tuple   							=  ['pixel_values']







							def __init__(						self  :  Dict      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[int, float] = 1 / 255      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  bool = True      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )
								__lowerCAmelCase         =							size if size is not None else {"shortest_edge": 224}
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
								__lowerCAmelCase         =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__      ,				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       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  List[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
								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(snake_case__      ,				size=size["shortest_edge"]      ,				default_to_square=snake_case__ )
								return resize(snake_case__      ,				size=snake_case__      ,				resample=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							get_size_dict(snake_case__ )
								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(snake_case__      ,				size=(size["height"], size["width"])      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[int, float]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Optional[int]      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  str      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  ImageInput      ,				snake_case__  :  bool = None      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  PILImageResampling = None      ,				snake_case__  :  bool = None      ,				snake_case__  :  int = None      ,				snake_case__  :  bool = None      ,				snake_case__  :  float = None      ,				snake_case__  :  bool = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  Optional[Union[float, List[float]]] = None      ,				snake_case__  :  bool = None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				snake_case__  :  Optional[ChannelDimension] = ChannelDimension.FIRST      ,				**snake_case__  :  int      ,				):
								"""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(snake_case__      ,				param_name="size"      ,				default_to_square=snake_case__ )
								__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(snake_case__      ,				param_name="crop_size"      ,				default_to_square=snake_case__ )
								__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(snake_case__ )

								if not valid_images(snake_case__ ):
									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(snake_case__ ) for image in images]

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

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

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

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

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

								__lowerCAmelCase         =							[to_channel_dimension_format(snake_case__      ,				snake_case__ ) for image in images]

								__lowerCAmelCase         =							{"pixel_values": images}
								return BatchFeature(data=snake_case__      ,				tensor_type=snake_case__ )








from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class       a		:







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							data
								__lowerCAmelCase         =							None

class       a		:







							def __init__(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							None







							def __iter__(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.head
								while self.head:
									yield node.data
									__lowerCAmelCase         =							node.next
									if node == self.head:
										break







							def __len__(						self  :  Dict ):
								"""simple docstring"""







								return sum(1 for _ in self )







							def __repr__(						self  :  str ):
								"""simple docstring"""







								return "->".join(str(snake_case__ ) for item in iter(self ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(len(self )      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(0      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  int      ,				snake_case__  :  Any ):
								"""simple docstring"""







								if index < 0 or index > len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							Node(snake_case__ )
								if self.head is None:
									__lowerCAmelCase         =							new_node  # first node points itself
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								elif index == 0:  # insert at head
									__lowerCAmelCase         =							self.head
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							new_node
									if index == len(self ) - 1:  # insert at tail
										__lowerCAmelCase         =							new_node







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								return self.delete_nth(0 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.delete_nth(len(self ) - 1 )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int = 0 ):
								"""simple docstring"""







								if not 0 <= index < len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							self.head
								if self.head == self.tail:  # just one node
									__lowerCAmelCase         =							__lowerCAmelCase         =							None
								elif index == 0:  # delete head node
									__lowerCAmelCase         =							self.tail.next.next
									__lowerCAmelCase         =							self.head.next
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next.next
									if index == len(self ) - 1:  # delete at tail
										__lowerCAmelCase         =							temp
								return delete_node.data







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return len(self ) == 0






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase			,							i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0			,							7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2			,							3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()






