
'''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 ViTImageProcessor






class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       Dict   ,     UpperCamelCase__							:       List[Any]   ,     UpperCamelCase__							:       List[Any]=7   ,     UpperCamelCase__							:       int=3   ,     UpperCamelCase__							:       Dict=18   ,     UpperCamelCase__							:       Dict=30   ,     UpperCamelCase__							:       str=400   ,     UpperCamelCase__							:       Optional[Any]=True   ,     UpperCamelCase__							:       Union[str, Any]=None   ,     UpperCamelCase__							:       Optional[int]=True   ,     UpperCamelCase__							:       int=[0.5, 0.5, 0.5]   ,     UpperCamelCase__							:       Any=[0.5, 0.5, 0.5]   ,     )		->       str:






				"""simple docstring"""







				snake_case   :    Tuple			       =		size if size is not None else {'''height''': 18, '''width''': 18}
				snake_case   :    str			       =		parent
				snake_case   :    Union[str, Any]			       =		batch_size
				snake_case   :    List[str]			       =		num_channels
				snake_case   :    List[str]			       =		image_size
				snake_case   :    int			       =		min_resolution
				snake_case   :    Union[str, Any]			       =		max_resolution
				snake_case   :    Tuple			       =		do_resize
				snake_case   :    List[Any]			       =		size
				snake_case   :    List[Any]			       =		do_normalize
				snake_case   :    Tuple			       =		image_mean
				snake_case   :    List[str]			       =		image_std



		def 	lowerCAmelCase							(	self							:       int	)		->       Optional[int]:






				"""simple docstring"""







				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      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		ViTImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       str	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    int			       =		ViTImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       List[str]:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Union[str, Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)



		def 	lowerCAmelCase							(	self							:       str	)		->       int:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size   ,     {'''height''': 18, '''width''': 18}	)

				snake_case   :    Optional[int]			       =		self.image_processing_class.from_dict(self.image_processor_dict   ,     size=42	)
				self.assertEqual(image_processor.size   ,     {'''height''': 42, '''width''': 42}	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Any:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Any			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    List[str]			       =		prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Union[str, Any]			       =		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
				snake_case   :    str			       =		image_processing(UpperCamelCase__   ,     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 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				snake_case   :    Dict			       =		prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase__   ,     numpify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     np.ndarray	)

				# Test not batched input
				snake_case   :    List[Any]			       =		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
				snake_case   :    Optional[int]			       =		image_processing(UpperCamelCase__   ,     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 	lowerCAmelCase							(	self							:       Tuple	)		->       str:






				"""simple docstring"""







				snake_case   :    Tuple			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				snake_case   :    Dict			       =		prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase__   ,     torchify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     torch.Tensor	)

				# Test not batched input
				snake_case   :    List[Any]			       =		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
				snake_case   :    str			       =		image_processing(UpperCamelCase__   ,     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'''],
				    )   ,     )










"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase     			=  TypeVar("""T""")







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self	,A					):
          UpperCAmelCase							=       data  # Assign data
          UpperCAmelCase							=       None  # Initialize next as null
          UpperCAmelCase							=       None  # Initialize prev as null







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self					):
          UpperCAmelCase							=       None






    def 				_UpperCamelCase							( self	,A					):
          if self.head is None:
                UpperCAmelCase							=       Node(A					)
          else:
                UpperCAmelCase							=       Node(A					)
                UpperCAmelCase							=       new_node
                UpperCAmelCase							=       self.head
                UpperCAmelCase							=       None
                UpperCAmelCase							=       new_node






    def 				_UpperCamelCase							( self					):
          if self.head is None:
                return None
          else:
                assert self.head is not None
                UpperCAmelCase							=       self.head.data
                UpperCAmelCase							=       self.head.next
                if self.head is not None:
                      UpperCAmelCase							=       None
                return temp






    def 				_UpperCamelCase							( self					):
          return self.head.data if self.head is not None else None






    def __len__( self					):
          UpperCAmelCase							=       self.head
          UpperCAmelCase							=       0
          while temp is not None:
                count += 1
                UpperCAmelCase							=       temp.next
          return count






    def 				_UpperCamelCase							( self					):
          return self.head is None






    def 				_UpperCamelCase							( self					):
          print("""stack elements are:"""					)
          UpperCAmelCase							=       self.head
          while temp is not None:
                print(temp.data	,end="""->"""					)
                UpperCAmelCase							=       temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  _UpperCamelCase     			=  Stack()

  # Insert 4 at the beginning. So stack becomes 4->None
  print("""Stack operations using Doubly LinkedList""")
  stack.push(4)

  # Insert 5 at the beginning. So stack becomes 4->5->None
  stack.push(5)

  # Insert 6 at the beginning. So stack becomes 4->5->6->None
  stack.push(6)

  # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
  stack.push(7)

  # Print the stack
  stack.print_stack()

  # Print the top element
  print("""\nTop element is """, stack.top())

  # Print the stack size
  print("""Size of the stack is """, len(stack))

  # pop the top element
  stack.pop()

  # pop the top element
  stack.pop()

  # two elements have now been popped off
  stack.print_stack()

  # Print True if the stack is empty else False
  print("""\nstack is empty:""", stack.is_empty())



