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


lowerCAmelCase__	   = logging.get_logger(__name__)


if is_vision_available():
	import PIL

class 					a__						(					__SCREAMING_SNAKE_CASE			):
			"""simple docstring"""







			__lowerCamelCase        =       ['pixel_values']


			def __init__(    self      ,      lowercase = True      ,      lowercase = None      ,      lowercase = PILImageResampling.BICUBIC      ,      lowercase = True      ,      lowercase = None      ,      lowercase = True      ,      lowercase = 1 / 255      ,      lowercase = True      ,      lowercase = None      ,      lowercase = None      ,      lowercase = True      ,      **lowercase      ,      )  ->						List[Any]:
							'''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      ,      lowercase      ,      lowercase      ,      lowercase = PILImageResampling.BICUBIC      ,      lowercase = None      ,      **lowercase      ,      )  ->						Tuple:
							'''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      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						Any:
							'''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      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = ChannelDimension.FIRST      ,      **lowercase      ,      )  ->						str:
							'''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       )




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






							A__        =			data
							A__        =			None

class 					a__						:
			"""simple docstring"""







			def __init__(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			None
							A__        =			None


			def __iter__(    self       )  ->						Iterator[Any]:
							'''simple docstring'''






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


			def __len__(    self       )  ->						int:
							'''simple docstring'''






							return sum(1 for _ in self       )


			def __repr__(    self       )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							self.insert_nth(len(self       )      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							self.insert_nth(0      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						None:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							return self.delete_nth(0       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase = 0       )  ->						Any:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						bool:
							'''simple docstring'''






							return len(self       ) == 0



def 					lowerCAmelCase__							(	)			->      None:




				'''simple docstring'''





				A__        =			CircularLinkedList()
				assert len(SCREAMING_SNAKE_CASE_      ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(SCREAMING_SNAKE_CASE_      ) == ""

				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(SCREAMING_SNAKE_CASE_      ) == i
								circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_	,       i + 1      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )

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

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

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

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()

