






from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    data
								lowercase__     				=    None



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    None
								lowercase__     				=    None




				def __iter__(  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.head
								while self.head:
												yield node.data
												lowercase__     				=    node.next
												if node == self.head:
																break




				def __len__(  self		:					Tuple						):

								'''simple docstring'''
								return sum(1 for _ in self						)




				def __repr__(  self		:					List[Any]						):

								'''simple docstring'''
								return "->".join(str(lowerCamelCase						) for item in iter(self						)						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								self.insert_nth(len(self						),						lowerCamelCase						)




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					Any						):

								'''simple docstring'''
								self.insert_nth(0,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					Any						):

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




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								return self.delete_nth(0						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								return self.delete_nth(len(self						) - 1						)




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int = 0						):

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								return len(self						) == 0



def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    CircularLinkedList()
				assert len(lowerCamelCase_       ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(lowerCamelCase_       ) == ""

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

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

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

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

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
							import doctest

							doctest.testmod()












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''shortest_edge''': 20}
								lowercase__     				=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_center_crop
								lowercase__     				=    crop_size




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 20}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 18, '''width''': 18}						)

								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict,						size=42,						crop_size=84						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 84, '''width''': 84}						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    image_processing(image_inputs[0],						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processing(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								# Test not batched input
								lowercase__     				=    image_processing(image_inputs[0],						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processing(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						torch.Tensor						)

								# Test not batched input
								lowercase__     				=    image_processing(image_inputs[0],						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processing(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




