




'''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


A     =logging.get_logger(__name__)


class 			_a  (	__a ):
       __a				:     Union[str, Any]   				=					["""pixel_values"""]
       def __init__(	self						:	Any   ,				lowercase						:	bool = True   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	PILImageResampling = PIL.Image.BICUBIC   ,				lowercase						:	bool = True   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	Union[int, float] = 1 / 255   ,				lowercase						:	bool = True   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				**lowercase						:	Optional[Any]   ,				):



           '''simple docstring'''

           super().__init__(**lowercase				)
           UpperCAmelCase			       =		size if size is not None else {'''height''': 256, '''width''': 256}
           UpperCAmelCase			       =		get_size_dict(lowercase				)
           UpperCAmelCase			       =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				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			       =		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
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	PILImageResampling = PIL.Image.BICUBIC   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	List[Any]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		get_size_dict(lowercase				)
           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(
               lowercase   ,				size=(size['''height'''], size['''width'''])   ,				resample=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Any   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		get_size_dict(lowercase				)
           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(lowercase   ,				size=(size['''height'''], size['''width'''])   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Union[int, float]   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

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



           '''simple docstring'''

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







       def     A      (	self						:	Any   ,				lowercase						:	ImageInput   ,				lowercase						:	bool = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	Dict=None   ,				lowercase						:	bool = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	bool = None   ,				lowercase						:	float = None   ,				lowercase						:	bool = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				lowercase						:	ChannelDimension = ChannelDimension.FIRST   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		make_list_of_images(lowercase				)

           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 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(lowercase				) for image in images]

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

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

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

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

           UpperCAmelCase			       =		[to_channel_dimension_format(lowercase   ,				lowercase				) for image in images]

           UpperCAmelCase			       =		{'''pixel_values''': images}
           return BatchFeature(data=lowercase   ,				tensor_type=lowercase				)










'''simple docstring'''







class 			_a  :
       def __init__(	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		{}  # Mapping from char to TrieNode
           UpperCAmelCase			       =		False
       def     A      (	self						:	int   ,				lowercase						:	list[str]				):



           '''simple docstring'''

           for word in words:
               self.insert(lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self
           for char in word:
               if char not in curr.nodes:
                   UpperCAmelCase			       =		TrieNode()
               UpperCAmelCase			       =		curr.nodes[char]
           UpperCAmelCase			       =		True
       def     A      (	self						:	Optional[int]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self
           for char in word:
               if char not in curr.nodes:
                   return False
               UpperCAmelCase			       =		curr.nodes[char]
           return curr.is_leaf







       def     A      (	self						:	str   ,				lowercase						:	str				):



           '''simple docstring'''

           def _delete(lowercase						:	TrieNode   ,				lowercase						:	str   ,				lowercase						:	int				) -> bool:
               if index == len(lowercase				):
                   # If word does not exist
                   if not curr.is_leaf:
                       return False
                   UpperCAmelCase			       =		False
                   return len(curr.nodes				) == 0
               UpperCAmelCase			       =		word[index]
               UpperCAmelCase			       =		curr.nodes.get(lowercase				)
               # If char not in current trie node
               if not char_node:
                   return False
               # Flag to check if node can be deleted
               UpperCAmelCase			       =		_delete(lowercase   ,				lowercase   ,				index + 1				)
               if delete_curr:
                   del curr.nodes[char]
                   return len(curr.nodes				) == 0
               return delete_curr

           _delete(self   ,				lowercase   ,				0				)






def 						snake_case_							(_a	:							TrieNode    ,   _a	:							str     ):
    if node.is_leaf:
        print(_a    ,   end=''' '''     )

    for key, value in node.nodes.items():
        print_words(_a    ,   word + key     )





def 						snake_case_							():
    UpperCAmelCase			       =		'''banana bananas bandana band apple all beast'''.split()
    UpperCAmelCase			       =		TrieNode()
    root.insert_many(_a     )
    # print_words(root, "")
    assert all(root.find(_a     ) for word in words     )
    assert root.find('''banana'''     )
    assert not root.find('''bandanas'''     )
    assert not root.find('''apps'''     )
    assert root.find('''apple'''     )
    assert root.find('''all'''     )
    root.delete('''all'''     )
    assert not root.find('''all'''     )
    root.delete('''banana'''     )
    assert not root.find('''banana'''     )
    assert root.find('''bananas'''     )
    return True





def 						snake_case_							(_a	:							str    ,   _a	:							bool     ):
    print(str(_a     )    ,   '''works!''' if passes else '''doesn\'t work :('''     )





def 						snake_case_							():
    assert test_trie()





def 						snake_case_							():
    print_results('''Testing trie functionality'''    ,   test_trie()     )


if __name__ == "__main__":
   main()




