

'''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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['pixel_values']






  def __init__(      self   :     Optional[int]					,lowercase__   :     bool = True					,lowercase__   :     Optional[Dict[str, int]] = None					,lowercase__   :     PILImageResampling = PILImageResampling.BICUBIC					,lowercase__   :     bool = True					,lowercase__   :     bool = True					,lowercase__   :     Union[int, float] = 1 / 2_5_5					,lowercase__   :     Dict[str, int] = None					,lowercase__   :     bool = True					,lowercase__   :     Optional[Union[float, List[float]]] = None					,lowercase__   :     Optional[Union[float, List[float]]] = None					,**lowercase__   :     Tuple					,):
   super().__init__(**lowercase__			)
   __lowercase							  = size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase							  = get_size_dict(lowercase__			)
   __lowercase							  = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase							  = get_size_dict(lowercase__					,default_to_square=lowercase__					,param_name='''crop_size'''			)

   __lowercase							  = do_resize
   __lowercase							  = do_rescale
   __lowercase							  = do_normalize
   __lowercase							  = do_center_crop
   __lowercase							  = crop_size
   __lowercase							  = size
   __lowercase							  = resample
   __lowercase							  = rescale_factor
   __lowercase							  = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
   __lowercase							  = image_std if image_std is not None else IMAGENET_DEFAULT_STD






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     np.ndarray					,lowercase__   :     Dict[str, int]					,lowercase__   :     PILImageResampling = PILImageResampling.BILINEAR					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     Optional[Any]					,):
   __lowercase							  = get_size_dict(lowercase__			)

   if "shortest_edge" in size:
    __lowercase							  = get_resize_output_image_size(lowercase__					,size=size['''shortest_edge''']					,default_to_square=lowercase__			)
    # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
   elif "height" in size and "width" in size:
    __lowercase							  = (size['''height'''], size['''width'''])
   else:
    raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"			)
   return resize(lowercase__					,size=lowercase__					,resample=lowercase__					,data_format=lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     np.ndarray					,lowercase__   :     Dict[str, int]					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     int					,):
   __lowercase							  = get_size_dict(lowercase__			)
   if "height" not in size or "width" not in size:
    raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"			)
   return center_crop(lowercase__					,size=(size['''height'''], size['''width'''])					,data_format=lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     np.ndarray					,lowercase__   :     float					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     Any			):
   return rescale(lowercase__					,scale=lowercase__					,data_format=lowercase__					,**lowercase__			)






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     ImageInput					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Dict[str, int] = None					,lowercase__   :     PILImageResampling = None					,lowercase__   :     bool = None					,lowercase__   :     int = None					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Optional[float] = None					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Optional[Union[float, List[float]]] = None					,lowercase__   :     Optional[Union[float, List[float]]] = None					,lowercase__   :     Optional[Union[str, TensorType]] = None					,lowercase__   :     Union[str, ChannelDimension] = ChannelDimension.FIRST					,**lowercase__   :     Tuple					,):
   __lowercase							  = do_resize if do_resize is not None else self.do_resize
   __lowercase							  = do_rescale if do_rescale is not None else self.do_rescale
   __lowercase							  = do_normalize if do_normalize is not None else self.do_normalize
   __lowercase							  = do_center_crop if do_center_crop is not None else self.do_center_crop
   __lowercase							  = crop_size if crop_size is not None else self.crop_size
   __lowercase							  = get_size_dict(lowercase__					,param_name='''crop_size'''					,default_to_square=lowercase__			)
   __lowercase							  = resample if resample is not None else self.resample
   __lowercase							  = rescale_factor if rescale_factor is not None else self.rescale_factor
   __lowercase							  = image_mean if image_mean is not None else self.image_mean
   __lowercase							  = image_std if image_std is not None else self.image_std

   __lowercase							  = size if size is not None else self.size
   __lowercase							  = get_size_dict(lowercase__			)

   if not is_batched(lowercase__			):
    __lowercase							  = [images]

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

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

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

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

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

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

   __lowercase							  = [to_channel_dimension_format(lowercase__					,lowercase__			) for image in images]

   __lowercase							  = {'''pixel_values''': images}
   return BatchFeature(data=lowercase__					,tensor_type=lowercase__			)




'''simple docstring'''






lowerCAmelCase__											=	'''Input must be a string of 8 numbers plus letter'''
lowerCAmelCase__											=	'''TRWAGMYFPDXBNJZSQVHLCKE'''


def 		_A     (    A__      ):






 """simple docstring"""



 if not isinstance(A__     ,				A__      ):
  __lowercase							  = F"Expected string as input, found {type(A__      ).__name__}"
  raise TypeError(A__      )

 __lowercase							  = spanish_id.replace('''-'''     ,				''''''      ).upper()
 if len(A__      ) != 9:
  raise ValueError(A__      )

 try:
  __lowercase							  = int(spanish_id_clean[0:8]      )
  __lowercase							  = spanish_id_clean[8]
 except ValueError as ex:
  raise ValueError(A__      ) from ex

 if letter.isdigit():
  raise ValueError(A__      )

 return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

