



class 							_a							:

			def __init__(self,      SCREAMING_SNAKE_CASE_=None				)					->      List[Any]:
						UpperCAmelCase_:		Any       			=    data
						UpperCAmelCase_:		Optional[int]       			=    None

			def __repr__(self				)					->      Union[str, Any]:
						UpperCAmelCase_:		Any       			=    """"""
						UpperCAmelCase_:		Optional[Any]       			=    self
						while temp:
									string_rep += f'<{temp.data}> ---> '
									UpperCAmelCase_:		int       			=    temp.next
						string_rep += "<END>"
						return string_rep







def    lowerCAmelCase_  (lowerCAmelCase__     ):





			"""simple docstring"""


			if not elements_list:
						raise Exception("""The Elements List is empty"""     )

			# Set first element as Head
			UpperCAmelCase_:		List[str]       			=    Node(elements_list[0]     )
			UpperCAmelCase_:		List[str]       			=    head
			# Loop through elements from position 1
			for data in elements_list[1:]:
						UpperCAmelCase_:		Optional[Any]       			=    Node(_UpperCamelCase     )
						UpperCAmelCase_:		Optional[Any]       			=    current.next
			return head


a			:     Dict     = [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('Creating Linked List from List.')
a			:     Union[str, Any]     = make_linked_list(list_data)
print('Linked List:')

print(linked_list)











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 ViTImageProcessor







class 							_a							(      unittest.TestCase				):

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=18,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->      Union[str, Any]:
						UpperCAmelCase_:		Any       			=    size if size is not None else {"""height""": 18, """width""": 18}
						UpperCAmelCase_:		str       			=    parent
						UpperCAmelCase_:		int       			=    batch_size
						UpperCAmelCase_:		Tuple       			=    num_channels
						UpperCAmelCase_:		Optional[int]       			=    image_size
						UpperCAmelCase_:		Optional[int]       			=    min_resolution
						UpperCAmelCase_:		Optional[int]       			=    max_resolution
						UpperCAmelCase_:		Optional[Any]       			=    do_resize
						UpperCAmelCase_:		Optional[int]       			=    size
						UpperCAmelCase_:		int       			=    do_normalize
						UpperCAmelCase_:		List[str]       			=    image_mean
						UpperCAmelCase_:		Tuple       			=    image_std

			def     __snake_case					(self				)					->      Tuple:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}







@require_torch
@require_vision
class 							_a							(      _lowerCAmelCase   ,		unittest.TestCase				):
			A					=  ViTImageProcessor if is_vision_available() else None

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		str       			=    ViTImageProcessingTester(self				)

			@property
			def     __snake_case					(self				)					->      List[str]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def     __snake_case					(self				)					->      int:
						UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_mean"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_std"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_normalize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_resize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """size"""				)				)

			def     __snake_case					(self				)					->      Union[str, Any]:
						UpperCAmelCase_:		List[str]       			=    self.image_processing_class.from_dict(self.image_processor_dict				)
						self.assertEqual(image_processor.size,      {"""height""": 18, """width""": 18}				)

						UpperCAmelCase_:		Union[str, Any]       			=    self.image_processing_class.from_dict(self.image_processor_dict,      size=42				)
						self.assertEqual(image_processor.size,      {"""height""": 42, """width""": 42}				)

			def     __snake_case					(self				)					->      Optional[Any]:
						pass

			def     __snake_case					(self				)					->      List[str]:
						# Initialize image_processing
						UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						UpperCAmelCase_:		List[Any]       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image				)

						# Test not batched input
						UpperCAmelCase_:		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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

						# Test batched
						UpperCAmelCase_:		str       			=    image_processing(SCREAMING_SNAKE_CASE_,      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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

			def     __snake_case					(self				)					->      Dict:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						UpperCAmelCase_:		int       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray				)

						# Test not batched input
						UpperCAmelCase_:		Dict       			=    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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

						# Test batched
						UpperCAmelCase_:		Optional[Any]       			=    image_processing(SCREAMING_SNAKE_CASE_,      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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

			def     __snake_case					(self				)					->      Tuple:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						UpperCAmelCase_:		Any       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor				)

						# Test not batched input
						UpperCAmelCase_:		Dict       			=    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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

						# Test batched
						UpperCAmelCase_:		str       			=    image_processing(SCREAMING_SNAKE_CASE_,      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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )






