





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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_     =logging.get_logger(__name__)




class 				_a		(   snake_case__       ):
				UpperCamelCase			=					['''pixel_values''']






				def __init__(				self	:					Optional[int],				lowerCAmelCase__	:					List[Any] = True,				lowerCAmelCase__	:					Optional[int] = None,				lowerCAmelCase__	:					List[Any] = PILImageResampling.BILINEAR,				lowerCAmelCase__	:					List[str] = True,				lowerCAmelCase__	:					Optional[int] = 1 / 2_5_5,				lowerCAmelCase__	:					Any = True,				lowerCAmelCase__	:					int = None,				lowerCAmelCase__	:					Tuple = None,				**lowerCAmelCase__	:					Optional[Any],				)			->   str:




										'''simple docstring'''



										super().__init__(**__snake_case							)
										_UpperCamelCase    :      Any						     =					size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										_UpperCamelCase    :      Tuple						     =					get_size_dict(__snake_case							)
										_UpperCamelCase    :      Tuple						     =					do_resize
										_UpperCamelCase    :      List[Any]						     =					do_rescale
										_UpperCamelCase    :      Dict						     =					do_normalize
										_UpperCamelCase    :      List[str]						     =					size
										_UpperCamelCase    :      str						     =					resample
										_UpperCamelCase    :      Optional[Any]						     =					rescale_factor
										_UpperCamelCase    :      str						     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCamelCase    :      Optional[Any]						     =					image_std if image_std is not None else IMAGENET_STANDARD_STD






				def        snake_case			(				self	:					Union[str, Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					int = PILImageResampling.BILINEAR,				lowerCAmelCase__	:					List[str] = None,				**lowerCAmelCase__	:					Dict,				)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					get_size_dict(__snake_case							)
										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()}"""							)
										return resize(
										    __snake_case,				size=(size['''height'''], size['''width''']),				resample=__snake_case,				data_format=__snake_case,				**__snake_case							)






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					str = None,				**lowerCAmelCase__	:					Dict							)			->   int:




										'''simple docstring'''



										return rescale(__snake_case,				scale=__snake_case,				data_format=__snake_case,				**__snake_case							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					List[Any] = None,				**lowerCAmelCase__	:					str,				)			->   Optional[Any]:




										'''simple docstring'''



										return normalize(__snake_case,				mean=__snake_case,				std=__snake_case,				data_format=__snake_case,				**__snake_case							)






				def        snake_case			(				self	:					Union[str, Any],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Dict = None,				lowerCAmelCase__	:					List[Any] = None,				lowerCAmelCase__	:					List[Any] = None,				lowerCAmelCase__	:					Union[str, Any] = None,				lowerCAmelCase__	:					Optional[Any] = None,				lowerCAmelCase__	:					int = None,				lowerCAmelCase__	:					Union[str, Any] = None,				lowerCAmelCase__	:					Tuple = None,				lowerCAmelCase__	:					Optional[int] = None,				lowerCAmelCase__	:					Optional[Any] = ChannelDimension.FIRST,				**lowerCAmelCase__	:					int,				)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					do_resize if do_resize is not None else self.do_resize
										_UpperCamelCase    :      Dict						     =					do_rescale if do_rescale is not None else self.do_rescale
										_UpperCamelCase    :      List[str]						     =					do_normalize if do_normalize is not None else self.do_normalize
										_UpperCamelCase    :      str						     =					resample if resample is not None else self.resample
										_UpperCamelCase    :      List[Any]						     =					rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCamelCase    :      Union[str, Any]						     =					image_mean if image_mean is not None else self.image_mean
										_UpperCamelCase    :      Any						     =					image_std if image_std is not None else self.image_std

										_UpperCamelCase    :      Union[str, Any]						     =					size if size is not None else self.size
										_UpperCamelCase    :      Optional[int]						     =					get_size_dict(__snake_case							)

										_UpperCamelCase    :      str						     =					make_list_of_images(__snake_case							)

										if not valid_images(__snake_case							):
																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.'''							)

										# All transformations expect numpy arrays.
										_UpperCamelCase    :      Union[str, Any]						     =					[to_numpy_array(__snake_case							) for image in images]

										if do_resize:
																_UpperCamelCase    :      Optional[int]						     =					[self.resize(image=__snake_case,				size=__snake_case,				resample=__snake_case							) for image in images]

										if do_rescale:
																_UpperCamelCase    :      Dict						     =					[self.rescale(image=__snake_case,				scale=__snake_case							) for image in images]

										if do_normalize:
																_UpperCamelCase    :      List[Any]						     =					[self.normalize(image=__snake_case,				mean=__snake_case,				std=__snake_case							) for image in images]

										_UpperCamelCase    :      Any						     =					[to_channel_dimension_format(__snake_case,				__snake_case							) for image in images]

										_UpperCamelCase    :      int						     =					{'''pixel_values''': images}
										return BatchFeature(data=__snake_case,				tensor_type=__snake_case							)










"""simple docstring"""




def  a_       (							_lowercase     ):
						_UpperCamelCase    :      str						     =					len(_lowercase     )

						# Set the gap value to be decreased by a factor of 1.3
						# after each iteration
						_UpperCamelCase    :      str						     =					1.3

						# Continue sorting until the gap is 1
						while gap > 1:
												# Decrease the gap value
												_UpperCamelCase    :      Union[str, Any]						     =					int(gap / shrink     )

												# Sort the elements using insertion sort
												for i in range(_lowercase ,		len(_lowercase     )     ):
																		_UpperCamelCase    :      Optional[Any]						     =					collection[i]
																		_UpperCamelCase    :      int						     =					i
																		while j >= gap and collection[j - gap] > temp:
																								_UpperCamelCase    :      List[str]						     =					collection[j - gap]
																								j -= gap
																		_UpperCamelCase    :      Union[str, Any]						     =					temp

						return collection


if __name__ == "__main__":
						import doctest

						doctest.testmod()



