






import string







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Dict       				=  ""
     for i in sequence:
          UpperCamelCase__			:   Tuple       				=  ord(__lowerCAmelCase			)
          if 65 <= extract <= 90:
               output += chr(155 - extract			)
          elif 97 <= extract <= 122:
               output += chr(219 - extract			)
          else:
               output += i
     return output







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Any       				=  string.ascii_letters
     UpperCamelCase__			:   List[str]       				=  string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
     return "".join(
         letters_reversed[letters.index(__lowerCAmelCase			)] if c in letters else c for c in sequence			)







def 		SCREAMING_SNAKE_CASE							(					) ->	None:
     from timeit import timeit

     print("Running performance benchmarks..."			)
     UpperCamelCase__			:   Union[str, Any]       				=  "from string import printable ; from __main__ import atbash, atbash_slow"
     print(f'> atbash_slow(): {timeit("atbash_slow(printable)"					,    setup=__lowerCAmelCase			)} seconds'			)
     print(f'>      atbash(): {timeit("atbash(printable)"					,    setup=__lowerCAmelCase			)} seconds'			)


if __name__ == "__main__":
 for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
  print(F"""{example} encrypted in atbash: {atbash(example)}""")
 benchmark()







from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


lowerCamelCase	:							Any				=logging.get_logger(__name__)





class 			__a  (    A__       ):
 _lowerCAmelCase       :       List[str]        = ['''pixel_values''']







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BILINEAR							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Union[int, float] = 1 / 2_55							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   **SCREAMING_SNAKE_CASE					:   Tuple							,   ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  size if size is not None else {"shortest_edge": 2_24}
      UpperCamelCase__			:   Any       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
      UpperCamelCase__			:   int       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   Dict       				=  do_resize
      UpperCamelCase__			:   List[str]       				=  size
      UpperCamelCase__			:   int       				=  resample
      UpperCamelCase__			:   Optional[int]       				=  do_rescale
      UpperCamelCase__			:   List[Any]       				=  rescale_factor
      UpperCamelCase__			:   Union[str, Any]       				=  do_center_crop
      UpperCamelCase__			:   int       				=  crop_size
      UpperCamelCase__			:   Optional[int]       				=  do_flip_channel_order







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


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      if "shortest_edge" not in size:
           raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'						)
      UpperCamelCase__			:   Any       				=  get_resize_output_image_size(SCREAMING_SNAKE_CASE							,   size=size["shortest_edge"]							,   default_to_square=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 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  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()}'						)
      return center_crop(SCREAMING_SNAKE_CASE							,   size=(size["height"], size["width"])							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[int, float]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      return rescale(SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None						):


      '''simple docstring'''






      return flip_channel_order(SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   ImageInput							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, 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					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, TensorType]] = None							,   SCREAMING_SNAKE_CASE					:   ChannelDimension = ChannelDimension.FIRST							,   **SCREAMING_SNAKE_CASE					:   Dict							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__			:   List[str]       				=  resample if resample is not None else self.resample
      UpperCamelCase__			:   Any       				=  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__			:   Tuple       				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__			:   Dict       				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__			:   Any       				=  (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      UpperCamelCase__			:   Optional[int]       				=  size if size is not None else self.size
      UpperCamelCase__			:   List[str]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   int       				=  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:
           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."						)

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."						)

      # All transformations expect numpy arrays.
      UpperCamelCase__			:   Tuple       				=  [to_numpy_array(SCREAMING_SNAKE_CASE						) for image in images]

      if do_resize:
           UpperCamelCase__			:   Optional[int]       				=  [self.resize(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE						) for image in images]

      if do_center_crop:
           UpperCamelCase__			:   Any       				=  [self.center_crop(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE						) for image in images]

      if do_rescale:
           UpperCamelCase__			:   str       				=  [self.rescale(image=SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE						) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           UpperCamelCase__			:   Any       				=  [self.flip_channel_order(image=SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Optional[int]       				=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Optional[int]       				=  {"pixel_values": images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE							,   tensor_type=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[Tuple] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  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						):
                UpperCamelCase__			:   Optional[Any]       				=  target_sizes.numpy()

           UpperCamelCase__			:   Any       				=  []

           for idx in range(len(SCREAMING_SNAKE_CASE						)						):
                UpperCamelCase__			:   Optional[Any]       				=  torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0						)							,   size=target_sizes[idx]							,   mode="bilinear"							,   align_corners=SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   List[str]       				=  resized_logits[0].argmax(dim=0						)
                semantic_segmentation.append(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__			:   List[str]       				=  logits.argmax(dim=1						)
           UpperCamelCase__			:   Dict       				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

      return semantic_segmentation