



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       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Optional[Any]       =						["pixel_values"]


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Dict[str, int]] = None  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[int, float] = 1 / 255  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,**lowerCamelCase_:     Dict  ,)	->      None:
       super().__init__(**lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					size if size is not None else {"""height""": 224, """width""": 224}
       UpperCAmelCase_     :       str  		=					get_size_dict(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					do_resize
       UpperCAmelCase_     :       Optional[Any]  		=					do_rescale
       UpperCAmelCase_     :       Optional[Any]  		=					do_normalize
       UpperCAmelCase_     :       int  		=					size
       UpperCAmelCase_     :       List[Any]  		=					resample
       UpperCAmelCase_     :       Optional[Any]  		=					rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase_     :       Any  		=					image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Dict[str, int]  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      np.ndarray:
       UpperCAmelCase_     :       Dict  		=					get_size_dict(lowerCamelCase_							)
       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(
           lowerCamelCase_  ,size=(size["""height"""], size["""width"""])  ,resample=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     float  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     List[Any]							)	->      np.ndarray:
       return rescale(lowerCamelCase_  ,scale=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Union[float, List[float]]  ,lowerCamelCase_:     Union[float, List[float]]  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     List[Any]  ,)	->      np.ndarray:
       return normalize(lowerCamelCase_  ,mean=lowerCamelCase_  ,std=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)






   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     ImageInput  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Dict[str, int] = None  ,lowerCamelCase_:     PILImageResampling = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[float] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,lowerCamelCase_:     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**lowerCamelCase_:     int  ,)	->      List[Any]:
       UpperCAmelCase_     :       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_     :       Dict  		=					do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_     :       int  		=					resample if resample is not None else self.resample
       UpperCAmelCase_     :       str  		=					rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_     :       Dict  		=					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_     :       Tuple  		=					size if size is not None else self.size
       UpperCAmelCase_     :       Union[str, Any]  		=					get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					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_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

       # All transformations expect numpy arrays.
       UpperCAmelCase_     :       Optional[Any]  		=					[to_numpy_array(lowerCamelCase_							) for image in images]

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

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

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

       UpperCAmelCase_     :       Any  		=					[to_channel_dimension_format(lowerCamelCase_  ,lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       List[str]  		=					{"""pixel_values""": images}
       return BatchFeature(data=lowerCamelCase_  ,tensor_type=lowerCamelCase_							)








"""simple docstring"""
from __future__ import annotations






def 			lowerCamelCase_    (		_lowerCamelCase							):
 lowerCamelCase__	:     int
 lowerCamelCase__	:     list[str]								=		list(_lowerCamelCase							)
 lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		_vector[:-1], int(_vector[-1]							)
 lowerCamelCase__	:     list[int]								=		[int(_lowerCamelCase							) for digit in __vector]

 vector.reverse()
 for i, digit in enumerate(_lowerCamelCase							):
  if i & 1 == 0:
   lowerCamelCase__	:     int								=		digit * 2
   if doubled > 9:
    doubled -= 9
   check_digit += doubled
  else:
   check_digit += digit

 return check_digit % 10 == 0


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      assert is_luhn("79927398713")
      assert not is_luhn("79927398714")

