



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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


UpperCAmelCase_							:      int      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Optional[Any]				=	["""pixel_values"""]



	def __init__(       self			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = PILImageResampling.BICUBIC			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = 1 / 255			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = True			,	**UpperCAmelCase__			,	):
							super().__init__(**UpperCAmelCase__		)
							A__							=       size if size is not None else {"height": 384, "width": 384}
							A__							=       get_size_dict(UpperCAmelCase__			,	default_to_square=UpperCAmelCase__		)

							A__							=       do_resize
							A__							=       size
							A__							=       resample
							A__							=       do_rescale
							A__							=       rescale_factor
							A__							=       do_normalize
							A__							=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							A__							=       image_std if image_std is not None else OPENAI_CLIP_STD
							A__							=       do_convert_rgb



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = PILImageResampling.BICUBIC			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							A__							=       get_size_dict(UpperCAmelCase__			,	default_to_square=UpperCAmelCase__		)
							if "height" not in size or "width" not in size:
													raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
							A__							=       (size["height"], size["width"])
							return resize(UpperCAmelCase__			,	size=UpperCAmelCase__			,	resample=UpperCAmelCase__			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							return rescale(UpperCAmelCase__			,	scale=UpperCAmelCase__			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							return normalize(UpperCAmelCase__			,	mean=UpperCAmelCase__			,	std=UpperCAmelCase__			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = ChannelDimension.FIRST			,	**UpperCAmelCase__			,	):
							A__							=       do_resize if do_resize is not None else self.do_resize
							A__							=       resample if resample is not None else self.resample
							A__							=       do_rescale if do_rescale is not None else self.do_rescale
							A__							=       rescale_factor if rescale_factor is not None else self.rescale_factor
							A__							=       do_normalize if do_normalize is not None else self.do_normalize
							A__							=       image_mean if image_mean is not None else self.image_mean
							A__							=       image_std if image_std is not None else self.image_std
							A__							=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							A__							=       size if size is not None else self.size
							A__							=       get_size_dict(UpperCAmelCase__			,	default_to_square=UpperCAmelCase__		)

							A__							=       make_list_of_images(UpperCAmelCase__		)

							if not valid_images(UpperCAmelCase__		):
													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 or resample is None:
													raise ValueError("Size and resample 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_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."		)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													A__							=       [convert_to_rgb(UpperCAmelCase__		) for image in images]

							# All transformations expect numpy arrays.
							A__							=       [to_numpy_array(UpperCAmelCase__		) for image in images]

							if do_resize:
													A__							=       [self.resize(image=UpperCAmelCase__			,	size=UpperCAmelCase__			,	resample=UpperCAmelCase__		) for image in images]

							if do_rescale:
													A__							=       [self.rescale(image=UpperCAmelCase__			,	scale=UpperCAmelCase__		) for image in images]

							if do_normalize:
													A__							=       [self.normalize(image=UpperCAmelCase__			,	mean=UpperCAmelCase__			,	std=UpperCAmelCase__		) for image in images]

							A__							=       [to_channel_dimension_format(UpperCAmelCase__			,	UpperCAmelCase__		) for image in images]

							A__							=       BatchFeature(data={"pixel_values": images}			,	tensor_type=UpperCAmelCase__		)

							return encoded_outputs










from __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





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

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





