'''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, 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, is_vision_available, logging


if is_vision_available():
     import PIL


_UpperCamelCase				:		List[str]       			=		logging.get_logger(__name__)

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  ['''pixel_values''']





   def __init__(							self:    Union[str, Any]			,a:    bool = True			,a:    Dict[str, int] = None			,a:    PILImageResampling = PIL.Image.NEAREST			,a:    bool = False			,a:    Dict[str, int] = None			,a:    Union[int, float] = 1 / 255			,a:    bool = False			,a:    bool = True			,a:    bool = True			,a:    Optional[Union[float, List[float]]] = None			,a:    Optional[Union[float, List[float]]] = None			,a:    bool = True			,**a:    List[Any]			,):
    super().__init__(**a	)
    __UpperCAmelCase         =					size if size is not None else {'height': 346, 'width': 346}
    __UpperCAmelCase         =					get_size_dict(a	)
    __UpperCAmelCase         =					crop_size if crop_size is not None else {'height': 289, 'width': 289}
    __UpperCAmelCase         =					get_size_dict(a			,param_name='crop_size'	)

    __UpperCAmelCase         =					do_resize
    __UpperCAmelCase         =					size
    __UpperCAmelCase         =					resample
    __UpperCAmelCase         =					do_center_crop
    __UpperCAmelCase         =					crop_size
    __UpperCAmelCase         =					do_rescale
    __UpperCAmelCase         =					rescale_factor
    __UpperCAmelCase         =					rescale_offset
    __UpperCAmelCase         =					do_normalize
    __UpperCAmelCase         =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __UpperCAmelCase         =					image_std if image_std is not None else IMAGENET_STANDARD_STD
    __UpperCAmelCase         =					include_top





   def 			snake_case	(							self:    int			,a:    np.ndarray			,a:    Dict[str, int]			,a:    PILImageResampling = PIL.Image.NEAREST			,a:    Optional[Union[str, ChannelDimension]] = None			,**a:    List[Any]			,):
    __UpperCAmelCase         =					get_size_dict(a	)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""	)
    return resize(
        a			,size=(size['height'], size['width'])			,resample=a			,data_format=a			,**a	)





   def 			snake_case	(							self:    int			,a:    np.ndarray			,a:    Dict[str, int]			,a:    Optional[Union[str, ChannelDimension]] = None			,**a:    List[str]			,):
    __UpperCAmelCase         =					get_size_dict(a	)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""	)
    return center_crop(a			,size=(size['height'], size['width'])			,data_format=a			,**a	)





   def 			snake_case	(							self:    str			,a:    np.ndarray			,a:    Union[int, float]			,a:    bool = True			,a:    Optional[Union[str, ChannelDimension]] = None			,**a:    Optional[Any]			,):
    if offset:
     __UpperCAmelCase         =					(image - 127.5) * scale
     if data_format is not None:
      __UpperCAmelCase         =					to_channel_dimension_format(a			,a	)
     __UpperCAmelCase         =					rescaled_image.astype(np.floataa	)
    else:
     __UpperCAmelCase         =					rescale(a			,scale=a			,data_format=a			,**a	)
    return rescaled_image





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





   def 			snake_case	(							self:    List[str]			,a:    ImageInput			,a:    bool = None			,a:    Dict[str, int] = None			,a:    Any=None			,a:    bool = None			,a:    Dict[str, int] = None			,a:    bool = None			,a:    float = None			,a:    bool = None			,a:    bool = None			,a:    Optional[Union[float, List[float]]] = None			,a:    Optional[Union[float, List[float]]] = None			,a:    bool = None			,a:    Optional[Union[str, TensorType]] = None			,a:    ChannelDimension = ChannelDimension.FIRST			,**a:    List[str]			,):
    __UpperCAmelCase         =					do_resize if do_resize is not None else self.do_resize
    __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         =					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         =					rescale_offset if rescale_offset is not None else self.rescale_offset
    __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         =					include_top if include_top is not None else self.include_top

    __UpperCAmelCase         =					size if size is not None else self.size
    __UpperCAmelCase         =					get_size_dict(a	)
    __UpperCAmelCase         =					crop_size if crop_size is not None else self.crop_size
    __UpperCAmelCase         =					get_size_dict(a			,param_name='crop_size'	)

    __UpperCAmelCase         =					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 or resample is None:
     raise ValueError('Size and resample 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.'	)

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

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

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

    if do_rescale:
     __UpperCAmelCase         =					[self.rescale(image=a			,scale=a			,offset=a	) for image in images]

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

    if include_top:
     __UpperCAmelCase         =					[self.normalize(image=a			,mean=[0, 0, 0]			,std=a	) for image in images]

    __UpperCAmelCase         =					[to_channel_dimension_format(a			,a	) for image in images]

    __UpperCAmelCase         =					{'pixel_values': images}
    return BatchFeature(data=a			,tensor_type=a	)




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     list			):

 for insert_index, insert_value in enumerate(collection[1:]			):
  __UpperCAmelCase         =					insert_index
  while insert_index >= 0 and insert_value < collection[insert_index]:
   __UpperCAmelCase         =					collection[insert_index]
   insert_index -= 1
  if insert_index != temp_index:
   __UpperCAmelCase         =					insert_value
 return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _UpperCamelCase				:		List[Any]       			=		input('Enter numbers separated by a comma:\n').strip()
     _UpperCamelCase				:		List[Any]       			=		[int(item) for item in user_input.split(',')]
     print(f"{insertion_sort(unsorted) = }")



