import math
from typing import Dict, Iterable, List, Optional, Tuple, 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,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE :	List[Any]							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 def constraint_to_multiple_of(_A							,				_A							,				_A=0							,				_A=None					):
  __a       =  round(val / multiple					) * multiple

  if max_val is not None and x > max_val:
   __a       =  math.floor(val / multiple					) * multiple

  if x < min_val:
   __a       =  math.ceil(val / multiple					) * multiple

  return x

 __a       =  (output_size, output_size) if isinstance(_A							,				_A					) else output_size

 __a       ,     __a       =  get_image_size(_A					)
 __a       ,     __a       =  output_size

 # determine new height and width
 __a       =  output_height / input_height
 __a       =  output_width / input_width

 if keep_aspect_ratio:
  # scale as little as possible
  if abs(1 - scale_width					) < abs(1 - scale_height					):
   # fit width
   __a       =  scale_width
  else:
   # fit height
   __a       =  scale_height

 __a       =  constraint_to_multiple_of(scale_height * input_height							,				multiple=_A					)
 __a       =  constraint_to_multiple_of(scale_width * input_width							,				multiple=_A					)

 return (new_height, new_width)






class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""pixel_values"""]

  def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BILINEAR     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Union[int, float] = 1 / 2_55     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							**__SCREAMING_SNAKE_CASE :      List[Any]     ,							):
   super().__init__(**__SCREAMING_SNAKE_CASE       )
   __a       =  size if size is not None else {"height": 3_84, "width": 3_84}
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   __a       =  do_resize
   __a       =  size
   __a       =  keep_aspect_ratio
   __a       =  ensure_multiple_of
   __a       =  resample
   __a       =  do_rescale
   __a       =  rescale_factor
   __a       =  do_normalize
   __a       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __a       =  image_std if image_std is not None else IMAGENET_STANDARD_STD

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int]     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BICUBIC     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]     ,							):
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   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()}"""       )
   __a       =  get_resize_output_image_size(
       __SCREAMING_SNAKE_CASE     ,							output_size=(size["height"], size["width"])     ,							keep_aspect_ratio=__SCREAMING_SNAKE_CASE     ,							multiple=__SCREAMING_SNAKE_CASE     ,							)
   return resize(__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							resample=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

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

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

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      ImageInput     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      float = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							__SCREAMING_SNAKE_CASE :      ChannelDimension = ChannelDimension.FIRST     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
   __a       =  do_resize if do_resize is not None else self.do_resize
   __a       =  size if size is not None else self.size
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE       )
   __a       =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
   __a       =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
   __a       =  resample if resample is not None else self.resample
   __a       =  do_rescale if do_rescale is not None else self.do_rescale
   __a       =  rescale_factor if rescale_factor is not None else self.rescale_factor
   __a       =  do_normalize if do_normalize is not None else self.do_normalize
   __a       =  image_mean if image_mean is not None else self.image_mean
   __a       =  image_std if image_std is not None else self.image_std

   __a       =  make_list_of_images(__SCREAMING_SNAKE_CASE       )

   if not valid_images(__SCREAMING_SNAKE_CASE       ):
    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_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.
   __a       =  [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

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

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

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

   __a       =  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  {"pixel_values": images}
   return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,							tensor_type=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Tuple] = None       ):
   __a       =  outputs.logits

   # Resize logits and compute semantic segmentation maps
   if target_sizes is not None:
    if len(__SCREAMING_SNAKE_CASE       ) != len(__SCREAMING_SNAKE_CASE       ):
     raise ValueError(
         "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

    if is_torch_tensor(__SCREAMING_SNAKE_CASE       ):
     __a       =  target_sizes.numpy()

    __a       =  []

    for idx in range(len(__SCREAMING_SNAKE_CASE       )       ):
     __a       =  torch.nn.functional.interpolate(
         logits[idx].unsqueeze(dim=0       )     ,							size=target_sizes[idx]     ,							mode="bilinear"     ,							align_corners=__SCREAMING_SNAKE_CASE       )
     __a       =  resized_logits[0].argmax(dim=0       )
     semantic_segmentation.append(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  logits.argmax(dim=1       )
    __a       =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

   return semantic_segmentation



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

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

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

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



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  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   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


