from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__snake_case    ):





   """simple docstring"""




   lowerCAmelCase      :    Tuple  			=   ['''pixel_values''']



   def __init__(	self					:  List[Any]		,  _lowercase					:  Union[str, Any] = True		,  _lowercase					:  List[str] = None		,  _lowercase					:  List[str] = PILImageResampling.BICUBIC		,  _lowercase					:  Tuple = True		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Union[str, Any] = True		,  _lowercase					:  Any = 1 / 255		,  _lowercase					:  Dict = True		,  _lowercase					:  str = IMAGENET_DEFAULT_MEAN		,  _lowercase					:  Optional[int] = IMAGENET_DEFAULT_STD		,  **_lowercase					:  List[str]		,  ):



    """simple docstring"""



    super().__init__(**__UpperCamelCase							)
    _UpperCamelCase:     Dict							= size if size is not None else {'''shortest_edge''': 224}
    _UpperCamelCase:     Optional[int]							= get_size_dict(__UpperCamelCase		,  default_to_square=__UpperCamelCase							)
    _UpperCamelCase:     Optional[int]							= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
    _UpperCamelCase:     str							= get_size_dict(__UpperCamelCase		,  param_name='''crop_size'''							)

    _UpperCamelCase:     Union[str, Any]							= do_resize
    _UpperCamelCase:     List[str]							= size
    _UpperCamelCase:     List[str]							= resample
    _UpperCamelCase:     Optional[int]							= do_center_crop
    _UpperCamelCase:     Optional[int]							= crop_size
    _UpperCamelCase:     Union[str, Any]							= do_rescale
    _UpperCamelCase:     Any							= rescale_factor
    _UpperCamelCase:     List[str]							= do_normalize
    _UpperCamelCase:     Dict							= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
    _UpperCamelCase:     Optional[int]							= image_std if image_std is not None else IMAGENET_DEFAULT_STD



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  List[str]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[int] = PILImageResampling.BICUBIC		,  _lowercase					:  Union[str, Any] = None		,  **_lowercase					:  int		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= get_size_dict(__UpperCamelCase		,  default_to_square=__UpperCamelCase							)
    # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
    if "shortest_edge" in size:
     _UpperCamelCase:     str							= int((256 / 224) * size['''shortest_edge''']							)
     _UpperCamelCase:     Dict							= get_resize_output_image_size(__UpperCamelCase		,  size=__UpperCamelCase		,  default_to_square=__UpperCamelCase							)
     _UpperCamelCase:     Optional[Any]							= {'''height''': output_size[0], '''width''': output_size[1]}
    if "height" not in size_dict or "width" not in size_dict:
     raise ValueError(
         f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""							)
    return resize(
        __UpperCamelCase		,  size=(size_dict['''height'''], size_dict['''width'''])		,  resample=__UpperCamelCase		,  data_format=__UpperCamelCase		,  **__UpperCamelCase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Dict		,  _lowercase					:  Union[str, Any] = None		,  **_lowercase					:  int		,  ):



    """simple docstring"""



    _UpperCamelCase:     Any							= get_size_dict(__UpperCamelCase							)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""							)
    return center_crop(__UpperCamelCase		,  size=(size['''height'''], size['''width'''])		,  data_format=__UpperCamelCase		,  **__UpperCamelCase							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  List[Any]		,  _lowercase					:  Any		,  _lowercase					:  Dict = None		,  **_lowercase					:  List[str]		,  ):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Tuple		,  _lowercase					:  Tuple		,  _lowercase					:  str		,  _lowercase					:  Optional[int] = None		,  **_lowercase					:  str		,  ):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  List[Any]		,  _lowercase					:  Tuple = None		,  _lowercase					:  Dict = None		,  _lowercase					:  Union[str, Any] = None		,  _lowercase					:  List[str] = None		,  _lowercase					:  List[Any] = None		,  _lowercase					:  Dict = None		,  _lowercase					:  Optional[Any] = None		,  _lowercase					:  Optional[Any] = None		,  _lowercase					:  List[str] = None		,  _lowercase					:  List[Any] = None		,  _lowercase					:  List[Any] = None		,  _lowercase					:  List[str] = ChannelDimension.FIRST		,  **_lowercase					:  str		,  ):



    """simple docstring"""



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

    _UpperCamelCase:     int							= size if size is not None else self.size
    _UpperCamelCase:     Dict							= get_size_dict(__UpperCamelCase		,  default_to_square=__UpperCamelCase							)
    _UpperCamelCase:     Dict							= crop_size if crop_size is not None else self.crop_size
    _UpperCamelCase:     Any							= get_size_dict(__UpperCamelCase		,  param_name='''crop_size'''							)

    _UpperCamelCase:     List[Any]							= 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:
     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.'''							)

    # All transformations expect numpy arrays.
    _UpperCamelCase:     int							= [to_numpy_array(__UpperCamelCase							) for image in images]

    if do_resize:
     _UpperCamelCase:     List[Any]							= [self.resize(__UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase							) for image in images]

    if do_center_crop:
     _UpperCamelCase:     List[str]							= [self.center_crop(__UpperCamelCase		,  __UpperCamelCase							) for image in images]

    if do_rescale:
     _UpperCamelCase:     Optional[Any]							= [self.rescale(__UpperCamelCase		,  __UpperCamelCase							) for image in images]

    if do_normalize:
     _UpperCamelCase:     Optional[int]							= [self.normalize(__UpperCamelCase		,  __UpperCamelCase		,  __UpperCamelCase							) for image in images]

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

    _UpperCamelCase:     Union[str, Any]							= {'''pixel_values''': images}
    return BatchFeature(data=__UpperCamelCase		,  tensor_type=__UpperCamelCase							)
def       lowerCAmelCase_     (		lowercase:							int = 600_851_475_143		)    ->				int:







 '''simple docstring'''

 try:
  _UpperCamelCase:     Optional[int]							= int(lowercase		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 _UpperCamelCase:     Dict							= 1
 _UpperCamelCase:     Tuple							= 2
 while i * i <= n:
  while n % i == 0:
   _UpperCamelCase:     List[Any]							= i
   n //= i
  i += 1
 if n > 1:
  _UpperCamelCase:     Any							= n
 return int(lowercase		)


if __name__ == "__main__":
  print(f"""{solution() = }""")