



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 DeiTImageProcessor


class 	lowerCamelCase__ (		unittest.TestCase   ):


						def __init__( self						:  Any      ,       _a						:  Dict      ,       _a						:  Optional[Any]=7      ,       _a						:  Optional[Any]=3      ,       _a						:  Any=1_8      ,       _a						:  Union[str, Any]=3_0      ,       _a						:  int=4_0_0      ,       _a						:  Optional[int]=True      ,       _a						:  int=None      ,       _a						:  Union[str, Any]=True      ,       _a						:  int=None      ,       _a						:  Union[str, Any]=True      ,       _a						:  List[str]=[0.5, 0.5, 0.5]      ,       _a						:  Optional[Any]=[0.5, 0.5, 0.5]      ,       ):
													a__:       Union[str, Any]											=size if size is not None else {"height": 2_0, "width": 2_0}
													a__:       Optional[int]											=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

													a__:       Tuple											=parent
													a__:       Tuple											=batch_size
													a__:       List[str]											=num_channels
													a__:       List[str]											=image_size
													a__:       Any											=min_resolution
													a__:       Optional[Any]											=max_resolution
													a__:       Optional[int]											=do_resize
													a__:       List[str]											=size
													a__:       Tuple											=do_center_crop
													a__:       int											=crop_size
													a__:       Tuple											=do_normalize
													a__:       Any											=image_mean
													a__:       str											=image_std







						def   _lowerCamelCase			( self						:  List[str]			):
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													}


@require_torch
@require_vision
class 	lowerCamelCase__ (		_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= DeiTImageProcessor if is_vision_available() else None
						_lowerCAmelCase								= True


						def   _lowerCamelCase			( self						:  List[str]			):
													a__:       List[str]											=DeiTImageProcessingTester(self			)


						@property
						def   _lowerCamelCase			( self						:  int			):
													return self.image_processor_tester.prepare_image_processor_dict()


						def   _lowerCamelCase			( self						:  str			):
													a__:       Any											=self.image_processing_class(**self.image_processor_dict			)
													self.assertTrue(hasattr(_a      ,       "do_resize"			)			)
													self.assertTrue(hasattr(_a      ,       "size"			)			)
													self.assertTrue(hasattr(_a      ,       "do_center_crop"			)			)
													self.assertTrue(hasattr(_a      ,       "center_crop"			)			)
													self.assertTrue(hasattr(_a      ,       "do_normalize"			)			)
													self.assertTrue(hasattr(_a      ,       "image_mean"			)			)
													self.assertTrue(hasattr(_a      ,       "image_std"			)			)


						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       Tuple											=self.image_processing_class.from_dict(self.image_processor_dict			)
													self.assertEqual(image_processor.size      ,       {"height": 2_0, "width": 2_0}			)
													self.assertEqual(image_processor.crop_size      ,       {"height": 1_8, "width": 1_8}			)

													a__:       str											=self.image_processing_class.from_dict(self.image_processor_dict      ,       size=4_2      ,       crop_size=8_4			)
													self.assertEqual(image_processor.size      ,       {"height": 4_2, "width": 4_2}			)
													self.assertEqual(image_processor.crop_size      ,       {"height": 8_4, "width": 8_4}			)


						def   _lowerCamelCase			( self						:  Any			):
													pass


						def   _lowerCamelCase			( self						:  Tuple			):
													# Initialize image_processing
													a__:       str											=self.image_processing_class(**self.image_processor_dict			)
													# create random PIL images
													a__:       str											=prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_a			)
													for image in image_inputs:
																				self.assertIsInstance(_a      ,       Image.Image			)

													# Test not batched input
													a__:       Tuple											=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__:       Dict											=image_processing(_a      ,       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   _lowerCamelCase			( self						:  int			):
													# Initialize image_processing
													a__:       Tuple											=self.image_processing_class(**self.image_processor_dict			)
													# create random numpy tensors
													a__:       Optional[Any]											=prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_a      ,       numpify=_a			)
													for image in image_inputs:
																				self.assertIsInstance(_a      ,       np.ndarray			)

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

													# Test batched
													a__:       Union[str, Any]											=image_processing(_a      ,       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   _lowerCamelCase			( self						:  Dict			):
													# Initialize image_processing
													a__:       Dict											=self.image_processing_class(**self.image_processor_dict			)
													# create random PyTorch tensors
													a__:       Any											=prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_a      ,       torchify=_a			)
													for image in image_inputs:
																				self.assertIsInstance(_a      ,       torch.Tensor			)

													# Test not batched input
													a__:       Optional[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__:       Optional[Any]											=image_processing(_a      ,       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"],
													    )      ,       )










from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase						  =     TypeVar('''T''')


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  int      ,       _a						:  T			):
													a__:       int											=data  # Assign data
													a__:       Node[T] | None											=None  # Initialize next as null
													a__:       Node[T] | None											=None  # Initialize prev as null


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  Optional[Any]			):
													a__:       Node[T] | None											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  T			):
													if self.head is None:
																				a__:       Dict											=Node(_a			)
													else:
																				a__:       int											=Node(_a			)
																				a__:       Optional[Any]											=new_node
																				a__:       int											=self.head
																				a__:       int											=None
																				a__:       Any											=new_node


						def   _lowerCamelCase			( self						:  Tuple			):
													if self.head is None:
																				return None
													else:
																				assert self.head is not None
																				a__:       Optional[int]											=self.head.data
																				a__:       Union[str, Any]											=self.head.next
																				if self.head is not None:
																											a__:       str											=None
																				return temp


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


						def __len__( self						:  Union[str, Any]			):
													a__:       Tuple											=self.head
													a__:       int											=0
													while temp is not None:
																				count += 1
																				a__:       List[str]											=temp.next
													return count


						def   _lowerCamelCase			( self						:  int			):
													return self.head is None







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													print("stack elements are:"			)
													a__:       Optional[int]											=self.head
													while temp is not None:
																				print(temp.data      ,       end="->"			)
																				a__:       Union[str, Any]											=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())





