

"""simple docstring"""






import functools
from typing import Any





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      bool:

 # Validation
 if not isinstance(__UpperCamelCase      ,   __UpperCamelCase						) or len(__UpperCamelCase						) == 0:
  raise ValueError('''the string should be not empty string'''						)

 if not isinstance(__UpperCamelCase      ,   __UpperCamelCase						) or not all(
     isinstance(__UpperCamelCase      ,   __UpperCamelCase						) and len(__UpperCamelCase						) > 0 for item in words						):
  raise ValueError('''the words should be a list of non-empty strings'''						)

 # Build trie
 __magic_name__     =      {}
 __magic_name__     =      '''WORD_KEEPER'''

 for word in words:
  __magic_name__     =      trie
  for c in word:
   if c not in trie_node:
    __magic_name__     =      {}

   __magic_name__     =      trie_node[c]

  __magic_name__     =      True

 __magic_name__     =      len(__UpperCamelCase						)

 # Dynamic programming method
 @functools.cache
 def is_breakable(__UpperCamelCase						) -> bool:
  if index == len_string:
   return True

  __magic_name__     =      trie
  for i in range(__UpperCamelCase      ,   __UpperCamelCase						):
   __magic_name__     =      trie_node.get(string[i]      ,   __UpperCamelCase						)

   if trie_node is None:
    return False

   if trie_node.get(__UpperCamelCase      ,   __UpperCamelCase						) and is_breakable(i + 1						):
    return True

  return False

 return is_breakable(0						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()









"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 32 ,						UpperCamelCase_=PILImageResampling.BILINEAR ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize
      __magic_name__     =      do_rescale
      __magic_name__     =      size_divisor
      __magic_name__     =      resample
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      get_image_size(UpperCamelCase_       )
      # Rounds the height and width down to the closest multiple of size_divisor
      __magic_name__     =      height // size_divisor * size_divisor
      __magic_name__     =      width // size_divisor * size_divisor
      __magic_name__     =      resize(UpperCamelCase_ ,						(new_h, new_w) ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      return rescale(image=UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_=None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      size_divisor if size_divisor is not None else self.size_divisor
      __magic_name__     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
       raise ValueError('''size_divisor is required for resizing'''       )

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       raise ValueError('''Invalid image(s)'''       )

      # All transformations expect numpy arrays.
      __magic_name__     =      [to_numpy_array(UpperCamelCase_       ) for img in images]

      if do_resize:
       __magic_name__     =      [self.resize(UpperCamelCase_ ,						size_divisor=UpperCamelCase_ ,						resample=UpperCamelCase_       ) for image in images]

      if do_rescale:
       __magic_name__     =      [self.rescale(UpperCamelCase_ ,						scale=1 / 255       ) for image in images]

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

