



'''simple docstring'''

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      __magic_name__						(   unittest.TestCase       ):







							def __init__(							self :	Any     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	List[str]=7     ,_UpperCAmelCase :	Optional[int]=3     ,_UpperCAmelCase :	Optional[int]=18     ,_UpperCAmelCase :	str=30     ,_UpperCAmelCase :	int=400     ,_UpperCAmelCase :	List[Any]=True     ,_UpperCAmelCase :	List[str]=None     ,_UpperCAmelCase :	List[str]=True     ,_UpperCAmelCase :	Optional[int]=None     ,):
									_a					:				Tuple				=							size if size is not None else {"""shortest_edge""": 20}
									_a					:				Union[str, Any]				=							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
									_a					:				List[str]				=							parent
									_a					:				Union[str, Any]				=							batch_size
									_a					:				Dict				=							num_channels
									_a					:				Dict				=							image_size
									_a					:				Optional[Any]				=							min_resolution
									_a					:				Tuple				=							max_resolution
									_a					:				int				=							do_resize
									_a					:				int				=							size
									_a					:				List[str]				=							do_center_crop
									_a					:				Any				=							crop_size







							def 							__lowercase      (							self :	Union[str, Any]		):
									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      __magic_name__						(   __UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Tuple           = MobileNetVaImageProcessor if is_vision_available() else None







							def 							__lowercase      (							self :	List[str]		):
									_a					:				Optional[int]				=							MobileNetVaImageProcessingTester(self		)







							@property
							def 							__lowercase      (							self :	Dict		):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase      (							self :	Any		):
									_a					:				Dict				=							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		):
									_a					:				str				=							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}		)

									_a					:				Union[str, Any]				=							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]		):
									pass







							def 							__lowercase      (							self :	Optional[int]		):
									# Initialize image_processing
									_a					:				Dict				=							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a					:				int				=							prepare_image_inputs(self.image_processor_tester     ,equal_resolution=_lowerCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_lowerCAmelCase     ,Image.Image		)

									# Test not batched input
									_a					:				List[str]				=							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
									_a					:				Optional[Any]				=							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 :	int		):
									# Initialize image_processing
									_a					:				Dict				=							self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									_a					:				Optional[Any]				=							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
									_a					:				int				=							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
									_a					:				int				=							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 :	Dict		):
									# Initialize image_processing
									_a					:				Dict				=							self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									_a					:				str				=							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
									_a					:				int				=							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
									_a					:				Tuple				=							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'],
									    )     ,)









'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      __magic_name__						:







							def __init__(							self :	Dict     ,_UpperCAmelCase :	Any		):
									_a					:				Any				=							data
									_a					:				Node | None				=							None

class      __magic_name__						:







							def __init__(							self :	Any		):
									_a					:				int				=							None
									_a					:				Optional[int]				=							None







							def __iter__(							self :	Optional[int]		):
									_a					:				List[Any]				=							self.head
									while self.head:
											yield node.data
											_a					:				str				=							node.next
											if node == self.head:
													break







							def __len__(							self :	Any		):
									return sum(1 for _ in self		)







							def __repr__(							self :	int		):
									return "->".join(str(_UpperCAmelCase		) for item in iter(self		)		)







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Any		):
									self.insert_nth(len(self		)     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Any		):
									self.insert_nth(0     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Any		):
									if index < 0 or index > len(self		):
											raise IndexError('list index out of range.'		)
									_a					:				List[str]				=							Node(_UpperCAmelCase		)
									if self.head is None:
											_a					:				Tuple				=							new_node  # first node points itself
											_a					:				int				=							new_node
									elif index == 0:  # insert at head
											_a					:				Any				=							self.head
											_a					:				Tuple				=							new_node
									else:
											_a					:				Any				=							self.head
											for _ in range(index - 1		):
													_a					:				int				=							temp.next
											_a					:				Optional[int]				=							temp.next
											_a					:				int				=							new_node
											if index == len(self		) - 1:  # insert at tail
													_a					:				Optional[int]				=							new_node







							def 							__lowercase      (							self :	List[Any]		):
									return self.delete_nth(0		)







							def 							__lowercase      (							self :	Dict		):
									return self.delete_nth(len(self		) - 1		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	int = 0		):
									if not 0 <= index < len(self		):
											raise IndexError('list index out of range.'		)
									_a					:				Optional[int]				=							self.head
									if self.head == self.tail:  # just one node
											_a					:				Optional[int]				=							None
									elif index == 0:  # delete head node
											_a					:				Dict				=							self.tail.next.next
											_a					:				Dict				=							self.head.next
									else:
											_a					:				List[Any]				=							self.head
											for _ in range(index - 1		):
													_a					:				Union[str, Any]				=							temp.next
											_a					:				Optional[int]				=							temp.next
											_a					:				List[str]				=							temp.next.next
											if index == len(self		) - 1:  # delete at tail
													_a					:				int				=							temp
									return delete_node.data







							def 							__lowercase      (							self :	int		):
									return len(self		) == 0



def 					__lowerCamelCase					(				)      ->				None:
		_a					:				int				=							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()




