




"""simple docstring"""







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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)


if is_vision_available():
		import PIL

class 							snake_case_ (				_a ):







			"""simple docstring"""




			A_										=					['pixel_values']







			def __init__(		self					,				lowerCamelCase_ = True					,				lowerCamelCase_ = None					,				lowerCamelCase_ = PILImageResampling.BICUBIC					,				lowerCamelCase_ = True					,				lowerCamelCase_ = None					,				lowerCamelCase_ = True					,				lowerCamelCase_ = 1 / 2_5_5					,				lowerCamelCase_ = True					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = True					,				**lowerCamelCase_					,				)							->      None:
							super().__init__(**lowerCamelCase_)
							UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 2_2_4}
							UpperCamelCase										=  get_size_dict(lowerCamelCase_					,				default_to_square=lowerCamelCase_)
							UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							UpperCamelCase										=  get_size_dict(lowerCamelCase_)

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







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = PILImageResampling.BICUBIC					,				lowerCamelCase_ = None					,				**lowerCamelCase_					,				)							->      np.ndarray:
							UpperCamelCase										=  get_size_dict(lowerCamelCase_					,				default_to_square=lowerCamelCase_)
							UpperCamelCase										=  get_resize_output_image_size(
							    lowerCamelCase_					,				size=(size['''height'''], size['''width'''])					,				default_to_square=lowerCamelCase_)
							return resize(lowerCamelCase_					,				size=lowerCamelCase_					,				resample=lowerCamelCase_					,				data_format=lowerCamelCase_					,				**lowerCamelCase_)







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				**lowerCamelCase_					,				)							->      np.ndarray:
							UpperCamelCase										=  get_size_dict(lowerCamelCase_)
							return center_crop(lowerCamelCase_					,				size=(size['''height'''], size['''width'''])					,				data_format=lowerCamelCase_					,				**lowerCamelCase_)







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				**lowerCamelCase_					,				)							->      Dict:
							return rescale(lowerCamelCase_					,				scale=lowerCamelCase_					,				data_format=lowerCamelCase_					,				**lowerCamelCase_)







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				**lowerCamelCase_					,				)							->      np.ndarray:
							return normalize(lowerCamelCase_					,				mean=lowerCamelCase_					,				std=lowerCamelCase_					,				data_format=lowerCamelCase_					,				**lowerCamelCase_)







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = ChannelDimension.FIRST					,				**lowerCamelCase_					,				)							->      PIL.Image.Image:
							UpperCamelCase										=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase										=  size if size is not None else self.size
							UpperCamelCase										=  get_size_dict(lowerCamelCase_					,				default_to_square=lowerCamelCase_)
							UpperCamelCase										=  resample if resample is not None else self.resample
							UpperCamelCase										=  do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase										=  crop_size if crop_size is not None else self.crop_size
							UpperCamelCase										=  get_size_dict(lowerCamelCase_)
							UpperCamelCase										=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase										=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase										=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase										=  image_mean if image_mean is not None else self.image_mean
							UpperCamelCase										=  image_std if image_std is not None else self.image_std
							UpperCamelCase										=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCamelCase										=  make_list_of_images(lowerCamelCase_)

							if not valid_images(lowerCamelCase_):
											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_center_crop and crop_size is None:
											raise ValueError('''Crop size must be specified if do_center_crop 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:
											UpperCamelCase										=  [convert_to_rgb(lowerCamelCase_) for image in images]

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

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

							if do_center_crop:
											UpperCamelCase										=  [self.center_crop(image=lowerCamelCase_					,				size=lowerCamelCase_) for image in images]

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

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

							UpperCamelCase										=  [to_channel_dimension_format(lowerCamelCase_					,				lowerCamelCase_) for image in images]

							UpperCamelCase										=  {'''pixel_values''': images}
							return BatchFeature(data=lowerCamelCase_					,				tensor_type=lowerCamelCase_)





"""simple docstring"""







class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_=None)							->      Tuple:
       UpperCamelCase										=  data
       UpperCamelCase										=  None







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





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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

    # Set first element as Head
    UpperCamelCase										=  Node(elements_list[0]						)
    UpperCamelCase										=  head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase										=  Node(_lowercase						)
        UpperCamelCase										=  current.next
    return head


SCREAMING_SNAKE_CASE_      =					[1, 3, 5, 32, 44, 12, 43]
print(f'List: {list_data}')
print('Creating Linked List from List.')
SCREAMING_SNAKE_CASE_      =					make_linked_list(list_data)
print('Linked List:')


print(linked_list)