



from typing import Any
class 				_snake_case			:




				def __init__(						self					:						Union[str, Any],					__lowercase					:						Any						):
							lowercase__					  =				data
							lowercase__					  =				None




				def __repr__(						self					:						Dict						):
							return F'''Node({self.data})'''
class 				_snake_case			:




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




				def __iter__(						self					:						int						):
							lowercase__					  =				self.head
							while node:
										yield node.data
										lowercase__					  =				node.next




				def __len__(						self					:						Optional[int]						):
							return sum(1 for _ in self						)




				def __repr__(						self					:						Optional[Any]						):
							return "->".join([str(__lowercase						) for item in self]						)




				def __getitem__(						self					:						Dict,					__lowercase					:						int						):
							if not 0 <= index < len(self						):
										raise ValueError("list index out of range."						)
							for i, node in enumerate(self						):
										if i == index:
													return node
							return None




				def __setitem__(						self					:						str,					__lowercase					:						int,					__lowercase					:						Any						):
							if not 0 <= index < len(self						):
										raise ValueError("list index out of range."						)
							lowercase__					  =				self.head
							for _ in range(__lowercase						):
										lowercase__					  =				current.next
							lowercase__					  =				data




				def 							A__      (						self					:						int,					__lowercase					:						Any						):
							self.insert_nth(len(self						),					__lowercase						)




				def 							A__      (						self					:						List[str],					__lowercase					:						Any						):
							self.insert_nth(0,					__lowercase						)




				def 							A__      (						self					:						List[Any],					__lowercase					:						int,					__lowercase					:						Any						):
							if not 0 <= index <= len(self						):
										raise IndexError("list index out of range"						)
							lowercase__					  =				Node(__lowercase						)
							if self.head is None:
										lowercase__					  =				new_node
							elif index == 0:
										lowercase__					  =				self.head  # link new_node to head
										lowercase__					  =				new_node
							else:
										lowercase__					  =				self.head
										for _ in range(index - 1						):
													lowercase__					  =				temp.next
										lowercase__					  =				temp.next
										lowercase__					  =				new_node




				def 							A__      (						self					:						Optional[int]						):  # print every node data
							print(self						)




				def 							A__      (						self					:						Tuple						):
							return self.delete_nth(0						)




				def 							A__      (						self					:						Dict						):  # delete from tail
							return self.delete_nth(len(self						) - 1						)




				def 							A__      (						self					:						int,					__lowercase					:						int = 0						):
							if not 0 <= index <= len(self						) - 1:  # test if index is valid
										raise IndexError("List index out of range."						)
							lowercase__					  =				self.head  # default first node
							if index == 0:
										lowercase__					  =				self.head.next
							else:
										lowercase__					  =				self.head
										for _ in range(index - 1						):
													lowercase__					  =				temp.next
										lowercase__					  =				temp.next
										lowercase__					  =				temp.next.next
							return delete_node.data




				def 							A__      (						self					:						Optional[Any]						):
							return self.head is None




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				None
							lowercase__					  =				self.head

							while current:
										# Store the current node's next node.
										lowercase__					  =				current.next
										# Make the current node's next point backwards
										lowercase__					  =				prev
										# Make the previous node be the current node
										lowercase__					  =				current
										# Make the current node the next node (to progress iteration)
										lowercase__					  =				next_node
							# Return prev in order to put the head at the end
							lowercase__					  =				prev



def __lowerCAmelCase (     ):
			lowercase__					  =				LinkedList()
			assert linked_list.is_empty() is True
			assert str(SCREAMING_SNAKE_CASE_  ) == ""

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

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

			for i in range(10  ):
						assert len(SCREAMING_SNAKE_CASE_  ) == i
						linked_list.insert_nth(SCREAMING_SNAKE_CASE_						,  i + 1  )
			assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1						,  11  )  )

			linked_list.insert_head(0  )
			linked_list.insert_tail(11  )
			assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(0						,  12  )  )

			assert linked_list.delete_head() == 0
			assert linked_list.delete_nth(9  ) == 10
			assert linked_list.delete_tail() == 11
			assert len(SCREAMING_SNAKE_CASE_  ) == 9
			assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1						,  10  )  )

			assert all(linked_list[i] == i + 1 for i in range(0						,  9  )  ) is True

			for i in range(0						,  9  ):
						lowercase__					  =				-i
			assert all(linked_list[i] == -i for i in range(0						,  9  )  ) is True

			linked_list.reverse()
			assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(-8						,  1  )  )



def __lowerCAmelCase (     ):
			lowercase__					  =				[
			    -9,
			    100,
			    Node(7734_5112  ),
			    "dlrow olleH",
			    7,
			    5555,
			    0,
			    -192.5_5555,
			    "Hello, world!",
			    77.9,
			    Node(10  ),
			    None,
			    None,
			    12.20,
			]
			lowercase__					  =				LinkedList()

			for i in test_input:
						linked_list.insert_tail(SCREAMING_SNAKE_CASE_  )

			# Check if it's empty or not
			assert linked_list.is_empty() is False
			assert (
			    str(SCREAMING_SNAKE_CASE_  ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
			    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
			)

			# Delete the head
			lowercase__					  =				linked_list.delete_head()
			assert result == -9
			assert (
			    str(SCREAMING_SNAKE_CASE_  ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None->None->12.2"
			)

			# Delete the tail
			lowercase__					  =				linked_list.delete_tail()
			assert result == 12.2
			assert (
			    str(SCREAMING_SNAKE_CASE_  ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None->None"
			)

			# Delete a node in specific location in linked list
			lowercase__					  =				linked_list.delete_nth(10  )
			assert result is None
			assert (
			    str(SCREAMING_SNAKE_CASE_  ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None"
			)

			# Add a Node instance to its head
			linked_list.insert_head(Node("Hello again, world!"  )  )
			assert (
			    str(SCREAMING_SNAKE_CASE_  )
			    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
			    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
			)

			# Add None to its tail
			linked_list.insert_tail(SCREAMING_SNAKE_CASE_  )
			assert (
			    str(SCREAMING_SNAKE_CASE_  )
			    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
			    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
			)

			# Reverse the linked list
			linked_list.reverse()
			assert (
			    str(SCREAMING_SNAKE_CASE_  )
			    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
			    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
			)



def __lowerCAmelCase (     ):
			from doctest import testmod

			testmod()

			lowercase__					  =				LinkedList()
			linked_list.insert_head(input("Inserting 1st at head "  ).strip()  )
			linked_list.insert_head(input("Inserting 2nd at head "  ).strip()  )
			print("\nPrint list:"  )
			linked_list.print_list()
			linked_list.insert_tail(input("\nInserting 1st at tail "  ).strip()  )
			linked_list.insert_tail(input("Inserting 2nd at tail "  ).strip()  )
			print("\nPrint list:"  )
			linked_list.print_list()
			print("\nDelete head"  )
			linked_list.delete_head()
			print("Delete tail"  )
			linked_list.delete_tail()
			print("\nPrint list:"  )
			linked_list.print_list()
			print("\nReverse linked list"  )
			linked_list.reverse()
			print("\nPrint list:"  )
			linked_list.print_list()
			print("\nString representation of linked list:"  )
			print(SCREAMING_SNAKE_CASE_  )
			print("\nReading/changing Node data using indexing:"  )
			print(f'''Element at Position 1: {linked_list[1]}'''  )
			lowercase__					  =				input("Enter New Value: "  ).strip()
			print("New list:"  )
			print(SCREAMING_SNAKE_CASE_  )
			print(f'''length of linked_list is : {len(SCREAMING_SNAKE_CASE_  )}'''  )


if __name__ == "__main__":
							main()









import unittest

import numpy as np
import requests

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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							lowercase_								=     False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						List[Any],					__lowercase					:						int,					__lowercase					:						Optional[int]=7,					__lowercase					:						List[str]=3,					__lowercase					:						Tuple=18,					__lowercase					:						List[Any]=30,					__lowercase					:						Tuple=400,					__lowercase					:						Any=None,					__lowercase					:						Optional[int]=True,					__lowercase					:						List[str]=True,					__lowercase					:						Union[str, Any]=None,					):
							lowercase__					  =				size if size is not None else {"height": 20, "width": 20}
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				size
							lowercase__					  =				do_normalize
							lowercase__					  =				do_convert_rgb
							lowercase__					  =				[512, 1024, 2048, 4096]
							lowercase__					  =				patch_size if patch_size is not None else {"height": 16, "width": 16}




				def 							A__      (						self					:						List[str]						):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




				def 							A__      (						self					:						Any						):
							lowercase__					  =				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
							lowercase__					  =				Image.open(requests.get(__lowercase,					stream=__lowercase						).raw						).convert("RGB"						)
							return raw_image
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,	)
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Any     		=PixaStructImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						Any						):
							lowercase__					  =				PixaStructImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						Union[str, Any]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_convert_rgb"						)						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				self.image_processor_tester.prepare_dummy_image()

							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							lowercase__					  =				2048

							lowercase__					  =				image_processor(__lowercase,					return_tensors="pt",					max_patches=__lowercase						)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),					torch.tensor(0.0606						),					atol=1e-3,					rtol=1e-3						)						)




				def 							A__      (						self					:						Union[str, Any]						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




				def 							A__      (						self					:						int						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							lowercase__					  =				True

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										with self.assertRaises(__lowercase						):
													lowercase__					  =				image_processor(
													    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches

										lowercase__					  =				"Hello"

										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase,					header_text=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase,					header_text=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




				def 							A__      (						self					:						Tuple						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					np.ndarray						)

							lowercase__					  =				(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




				def 							A__      (						self					:						Any						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					torchify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					torch.Tensor						)

							# Test not batched input
							lowercase__					  =				(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,	)
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=PixaStructImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				PixaStructImageProcessingTester(self,					num_channels=4						)
							lowercase__					  =				3




				@property
				def 							A__      (						self					:						Union[str, Any]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_convert_rgb"						)						)




				def 							A__      (						self					:						Union[str, Any]						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




