



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


A__						:						Any          =      logging.get_logger(__name__)


if is_vision_available():
						import PIL



class        __snake_case		(  UpperCamelCase_		):
							_a       =				['''pixel_values''']




							def __init__( self  : Union[str, Any]       ,       A_  : bool = True       ,       A_  : Dict[str, int] = None       ,       A_  : PILImageResampling = PILImageResampling.BILINEAR       ,       A_  : bool = True       ,       A_  : bool = False       ,       A_  : bool = True       ,       A_  : bool = True       ,       A_  : Union[int, float] = 1 / 2_5_5       ,       A_  : bool = True       ,       A_  : Optional[Union[float, List[float]]] = None       ,       A_  : Optional[Union[float, List[float]]] = None       ,       **A_  : Any       ,       ):
									super().__init__(**A_)

									lowerCAmelCase_    :  Optional[int]             =				size if size is not None else {'''height''': 2_5_6_0, '''width''': 1_9_2_0}
									if isinstance(A_       ,       (tuple, list)):
											# The previous feature extractor size parameter was in (width, height) format
											lowerCAmelCase_    :  List[str]             =				size[::-1]
									lowerCAmelCase_    :  Tuple             =				get_size_dict(A_)

									lowerCAmelCase_    :  Optional[Any]             =				do_resize
									lowerCAmelCase_    :  Union[str, Any]             =				size
									lowerCAmelCase_    :  str             =				resample
									lowerCAmelCase_    :  Union[str, Any]             =				do_thumbnail
									lowerCAmelCase_    :  List[Any]             =				do_align_long_axis
									lowerCAmelCase_    :  List[str]             =				do_pad
									lowerCAmelCase_    :  str             =				do_rescale
									lowerCAmelCase_    :  Dict             =				rescale_factor
									lowerCAmelCase_    :  Optional[Any]             =				do_normalize
									lowerCAmelCase_    :  Dict             =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowerCAmelCase_    :  str             =				image_std if image_std is not None else IMAGENET_STANDARD_STD




							def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : np.ndarray       ,       A_  : Dict[str, int]       ,       A_  : Optional[Union[str, ChannelDimension]] = None):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				get_image_size(A_)
									lowerCAmelCase_      ,						lowerCAmelCase_    :  List[Any]             =				size['''height'''], size['''width''']

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
											lowerCAmelCase_    :  List[Any]             =				np.rotaa(A_       ,       3)

									if data_format is not None:
											lowerCAmelCase_    :  Union[str, Any]             =				to_channel_dimension_format(A_       ,       A_)

									return image




							def      UpperCAmelCase__    ( self  : str       ,       A_  : np.ndarray       ,       A_  : Dict[str, int]       ,       A_  : bool = False       ,       A_  : Optional[Union[str, ChannelDimension]] = None       ,       ):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  str             =				size['''height'''], size['''width''']
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Union[str, Any]             =				get_image_size(A_)

									lowerCAmelCase_    :  Optional[int]             =				output_width - input_width
									lowerCAmelCase_    :  int             =				output_height - input_height

									if random_padding:
											lowerCAmelCase_    :  Dict             =				np.random.randint(low=0       ,       high=delta_height + 1)
											lowerCAmelCase_    :  int             =				np.random.randint(low=0       ,       high=delta_width + 1)
									else:
											lowerCAmelCase_    :  Union[str, Any]             =				delta_height // 2
											lowerCAmelCase_    :  Tuple             =				delta_width // 2

									lowerCAmelCase_    :  Dict             =				delta_height - pad_top
									lowerCAmelCase_    :  int             =				delta_width - pad_left

									lowerCAmelCase_    :  str             =				((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(A_       ,       A_       ,       data_format=A_)




							def      UpperCAmelCase__    ( self  : int       ,       *A_  : Union[str, Any]       ,       **A_  : List[Any]):
									logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.''')
									return self.pad_image(*A_       ,       **A_)




							def      UpperCAmelCase__    ( self  : Dict       ,       A_  : np.ndarray       ,       A_  : Dict[str, int]       ,       A_  : PILImageResampling = PILImageResampling.BICUBIC       ,       A_  : Optional[Union[str, ChannelDimension]] = None       ,       **A_  : Optional[int]       ,       ):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				get_image_size(A_)
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Union[str, Any]             =				size['''height'''], size['''width''']

									# We always resize to the smallest of either the input or output size.
									lowerCAmelCase_    :  List[str]             =				min(A_       ,       A_)
									lowerCAmelCase_    :  str             =				min(A_       ,       A_)

									if height == input_height and width == input_width:
											return image

									if input_height > input_width:
											lowerCAmelCase_    :  Union[str, Any]             =				int(input_width * height / input_height)
									elif input_width > input_height:
											lowerCAmelCase_    :  Dict             =				int(input_height * width / input_width)

									return resize(
									    A_       ,       size=(height, width)       ,       resample=A_       ,       reducing_gap=2.0       ,       data_format=A_       ,       **A_)




							def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : np.ndarray       ,       A_  : Dict[str, int]       ,       A_  : PILImageResampling = PILImageResampling.BICUBIC       ,       A_  : Optional[Union[str, ChannelDimension]] = None       ,       **A_  : int       ,       ):
									lowerCAmelCase_    :  Optional[int]             =				get_size_dict(A_)
									lowerCAmelCase_    :  int             =				min(size['''height''']       ,       size['''width'''])
									lowerCAmelCase_    :  int             =				get_resize_output_image_size(A_       ,       size=A_       ,       default_to_square=A_)
									lowerCAmelCase_    :  Any             =				resize(A_       ,       size=A_       ,       resample=A_       ,       data_format=A_       ,       **A_)
									return resized_image




							def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : np.ndarray       ,       A_  : Union[int, float]       ,       A_  : Optional[Union[str, ChannelDimension]] = None       ,       **A_  : List[str]       ,       ):
									return rescale(A_       ,       scale=A_       ,       data_format=A_       ,       **A_)




							def      UpperCAmelCase__    ( self  : Any       ,       A_  : np.ndarray       ,       A_  : Union[float, List[float]]       ,       A_  : Union[float, List[float]]       ,       A_  : Optional[Union[str, ChannelDimension]] = None       ,       **A_  : str       ,       ):
									return normalize(A_       ,       mean=A_       ,       std=A_       ,       data_format=A_       ,       **A_)







							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : ImageInput       ,       A_  : bool = None       ,       A_  : Dict[str, int] = None       ,       A_  : PILImageResampling = None       ,       A_  : bool = None       ,       A_  : bool = None       ,       A_  : bool = None       ,       A_  : bool = False       ,       A_  : bool = None       ,       A_  : float = None       ,       A_  : bool = None       ,       A_  : Optional[Union[float, List[float]]] = None       ,       A_  : Optional[Union[float, List[float]]] = None       ,       A_  : Optional[Union[str, TensorType]] = None       ,       A_  : Optional[ChannelDimension] = ChannelDimension.FIRST       ,       **A_  : int       ,       ):
									lowerCAmelCase_    :  Tuple             =				do_resize if do_resize is not None else self.do_resize
									lowerCAmelCase_    :  Optional[Any]             =				size if size is not None else self.size
									if isinstance(A_       ,       (tuple, list)):
											# Previous feature extractor had size in (width, height) format
											lowerCAmelCase_    :  Dict             =				size[::-1]
									lowerCAmelCase_    :  int             =				get_size_dict(A_)
									lowerCAmelCase_    :  str             =				resample if resample is not None else self.resample
									lowerCAmelCase_    :  List[str]             =				do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									lowerCAmelCase_    :  List[str]             =				do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									lowerCAmelCase_    :  List[str]             =				do_pad if do_pad is not None else self.do_pad
									lowerCAmelCase_    :  List[str]             =				do_rescale if do_rescale is not None else self.do_rescale
									lowerCAmelCase_    :  List[str]             =				rescale_factor if rescale_factor is not None else self.rescale_factor
									lowerCAmelCase_    :  List[Any]             =				do_normalize if do_normalize is not None else self.do_normalize
									lowerCAmelCase_    :  str             =				image_mean if image_mean is not None else self.image_mean
									lowerCAmelCase_    :  str             =				image_std if image_std is not None else self.image_std

									lowerCAmelCase_    :  Any             =				make_list_of_images(A_)

									if not valid_images(A_):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

									if do_resize and size is None:
											raise ValueError('''Size must be specified if do_resize is True.''')

									if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

									if do_pad and size is None:
											raise ValueError('''Size must be specified if do_pad is True.''')

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

									# All transformations expect numpy arrays.
									lowerCAmelCase_    :  Any             =				[to_numpy_array(A_) for image in images]

									if do_align_long_axis:
											lowerCAmelCase_    :  Dict             =				[self.align_long_axis(A_       ,       size=A_) for image in images]

									if do_resize:
											lowerCAmelCase_    :  Dict             =				[self.resize(image=A_       ,       size=A_       ,       resample=A_) for image in images]

									if do_thumbnail:
											lowerCAmelCase_    :  Tuple             =				[self.thumbnail(image=A_       ,       size=A_) for image in images]

									if do_pad:
											lowerCAmelCase_    :  List[str]             =				[self.pad_image(image=A_       ,       size=A_       ,       random_padding=A_) for image in images]

									if do_rescale:
											lowerCAmelCase_    :  Union[str, Any]             =				[self.rescale(image=A_       ,       scale=A_) for image in images]

									if do_normalize:
											lowerCAmelCase_    :  List[str]             =				[self.normalize(image=A_       ,       mean=A_       ,       std=A_) for image in images]

									lowerCAmelCase_    :  List[Any]             =				[to_channel_dimension_format(A_       ,       A_) for image in images]

									lowerCAmelCase_    :  Any             =				{'''pixel_values''': images}
									return BatchFeature(data=A_       ,       tensor_type=A_)










from __future__ import annotations

from typing import Generic, TypeVar

A__						:						Dict          =      TypeVar('''T''')



class        __snake_case		(  Generic[T]		):




							def __init__( self  : List[Any]       ,       A_  : T):
									lowerCAmelCase_    :  Optional[Any]             =				data  # Assign data
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize next as null
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize prev as null





class        __snake_case		(  Generic[T]		):




							def __init__( self  : Union[str, Any]):
									lowerCAmelCase_    :  Node[T] | None             =				None




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : T):
									if self.head is None:
											lowerCAmelCase_    :  List[Any]             =				Node(A_)
									else:
											lowerCAmelCase_    :  Tuple             =				Node(A_)
											lowerCAmelCase_    :  Optional[int]             =				new_node
											lowerCAmelCase_    :  Optional[int]             =				self.head
											lowerCAmelCase_    :  Optional[Any]             =				None
											lowerCAmelCase_    :  Dict             =				new_node




							def      UpperCAmelCase__    ( self  : List[str]):
									if self.head is None:
											return None
									else:
											assert self.head is not None
											lowerCAmelCase_    :  Tuple             =				self.head.data
											lowerCAmelCase_    :  Any             =				self.head.next
											if self.head is not None:
													lowerCAmelCase_    :  Optional[Any]             =				None
											return temp




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									return self.head.data if self.head is not None else None




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




							def      UpperCAmelCase__    ( self  : Dict):
									return self.head is None







							def      UpperCAmelCase__    ( self  : Dict):
									print('''stack elements are:''')
									lowerCAmelCase_    :  Optional[Any]             =				self.head
									while temp is not None:
											print(temp.data       ,       end='''->''')
											lowerCAmelCase_    :  List[str]             =				temp.next


# Code execution starts here
if __name__ == "__main__":
						# Start with the empty stack
						A__						:						Stack[int]          =      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())





