



'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_UpperCamelCase											=		logging.get_logger(__name__)

def 			a_	(				_lowerCAmelCase			) -> List[List[ImageInput]]:
  if isinstance(_lowerCAmelCase			,(list, tuple)			) and isinstance(videos[0]			,(list, tuple)			) and is_valid_image(videos[0][0]			):
    return videos

  elif isinstance(_lowerCAmelCase			,(list, tuple)			) and is_valid_image(videos[0]			):
    return [videos]

  elif is_valid_image(_lowerCAmelCase			):
    return [[videos]]

  raise ValueError(F'Could not make batched video from {videos}'			)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""pixel_values"""]




  def __init__(					self :      int     ,      _a :      bool = True     ,      _a :      Dict[str, int] = None     ,      _a :      PILImageResampling = PILImageResampling.BILINEAR     ,      _a :      bool = True     ,      _a :      Dict[str, int] = None     ,      _a :      bool = True     ,      _a :      Union[int, float] = 1 / 255     ,      _a :      bool = True     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      **_a :      List[str]     ,      )   ->   None:
    super().__init__(**_a  )
    __lowerCamelCase			:			Tuple       			=					size if size is not None else {'shortest_edge': 224}
    __lowerCamelCase			:			Any       			=					get_size_dict(_a     ,      default_to_square=_a  )
    __lowerCamelCase			:			int       			=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase			:			Tuple       			=					get_size_dict(_a     ,      param_name='crop_size'  )

    __lowerCamelCase			:			List[str]       			=					do_resize
    __lowerCamelCase			:			int       			=					size
    __lowerCamelCase			:			Optional[int]       			=					do_center_crop
    __lowerCamelCase			:			Dict       			=					crop_size
    __lowerCamelCase			:			Optional[Any]       			=					resample
    __lowerCamelCase			:			Dict       			=					do_rescale
    __lowerCamelCase			:			Optional[int]       			=					rescale_factor
    __lowerCamelCase			:			str       			=					do_normalize
    __lowerCamelCase			:			str       			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowerCamelCase			:			str       			=					image_std if image_std is not None else IMAGENET_STANDARD_STD




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      PILImageResampling = PILImageResampling.BILINEAR     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Tuple     ,      )   ->   np.ndarray:
    __lowerCamelCase			:			Optional[Any]       			=					get_size_dict(_a     ,      default_to_square=_a  )
    if "shortest_edge" in size:
      __lowerCamelCase			:			Optional[int]       			=					get_resize_output_image_size(_a     ,      size['shortest_edge']     ,      default_to_square=_a  )
    elif "height" in size and "width" in size:
      __lowerCamelCase			:			Union[str, Any]       			=					(size['height'], size['width'])
    else:
      raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'  )
    return resize(_a     ,      size=_a     ,      resample=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Dict     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Union[str, Any]     ,      )   ->   np.ndarray:
    __lowerCamelCase			:			str       			=					get_size_dict(_a  )
    if "height" not in size or "width" not in size:
      raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'  )
    return center_crop(_a     ,      size=(size['height'], size['width'])     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      int     ,      _a :      np.ndarray     ,      _a :      Union[int, float]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Optional[int]     ,      )   ->   str:
    return rescale(_a     ,      scale=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Any     ,      _a :      np.ndarray     ,      _a :      Union[float, List[float]]     ,      _a :      Union[float, List[float]]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Optional[int]     ,      )   ->   np.ndarray:
    return normalize(_a     ,      mean=_a     ,      std=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      ImageInput     ,      _a :      bool = None     ,      _a :      Dict[str, int] = None     ,      _a :      PILImageResampling = None     ,      _a :      bool = None     ,      _a :      Dict[str, int] = None     ,      _a :      bool = None     ,      _a :      float = None     ,      _a :      bool = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[ChannelDimension] = ChannelDimension.FIRST     ,      )   ->   np.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.
    __lowerCamelCase			:			Union[str, Any]       			=					to_numpy_array(_a  )

    if do_resize:
      __lowerCamelCase			:			Dict       			=					self.resize(image=_a     ,      size=_a     ,      resample=_a  )

    if do_center_crop:
      __lowerCamelCase			:			Dict       			=					self.center_crop(_a     ,      size=_a  )

    if do_rescale:
      __lowerCamelCase			:			List[str]       			=					self.rescale(image=_a     ,      scale=_a  )

    if do_normalize:
      __lowerCamelCase			:			str       			=					self.normalize(image=_a     ,      mean=_a     ,      std=_a  )

    __lowerCamelCase			:			Union[str, Any]       			=					to_channel_dimension_format(_a     ,      _a  )
    return image




  def 		_lowercase	(					self :      str     ,      _a :      ImageInput     ,      _a :      bool = None     ,      _a :      Dict[str, int] = None     ,      _a :      PILImageResampling = None     ,      _a :      bool = None     ,      _a :      Dict[str, int] = None     ,      _a :      bool = None     ,      _a :      float = None     ,      _a :      bool = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[str, TensorType]] = None     ,      _a :      ChannelDimension = ChannelDimension.FIRST     ,      **_a :      Union[str, Any]     ,      )   ->   PIL.Image.Image:
    __lowerCamelCase			:			List[str]       			=					do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase			:			Optional[Any]       			=					resample if resample is not None else self.resample
    __lowerCamelCase			:			Optional[Any]       			=					do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowerCamelCase			:			List[str]       			=					do_rescale if do_rescale is not None else self.do_rescale
    __lowerCamelCase			:			int       			=					rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase			:			Tuple       			=					do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase			:			Optional[Any]       			=					image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase			:			str       			=					image_std if image_std is not None else self.image_std

    __lowerCamelCase			:			str       			=					size if size is not None else self.size
    __lowerCamelCase			:			List[str]       			=					get_size_dict(_a     ,      default_to_square=_a  )
    __lowerCamelCase			:			Tuple       			=					crop_size if crop_size is not None else self.crop_size
    __lowerCamelCase			:			Optional[int]       			=					get_size_dict(_a     ,      param_name='crop_size'  )

    if not valid_images(_a  ):
      raise ValueError(
          'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
          'torch.Tensor, tf.Tensor or jax.ndarray.'  )

    __lowerCamelCase			:			Optional[int]       			=					make_batched(_a  )

    __lowerCamelCase			:			Union[str, Any]       			=					[
        [
            self._preprocess_image(
                image=_a     ,      do_resize=_a     ,      size=_a     ,      resample=_a     ,      do_center_crop=_a     ,      crop_size=_a     ,      do_rescale=_a     ,      rescale_factor=_a     ,      do_normalize=_a     ,      image_mean=_a     ,      image_std=_a     ,      data_format=_a     ,      )
            for img in video
        ]
        for video in videos
    ]

    __lowerCamelCase			:			List[Any]       			=					{'pixel_values': videos}
    return BatchFeature(data=_a     ,      tensor_type=_a  )











'''simple docstring'''





import string

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Union[str, Any]       			=					''
  for i in sequence:
    __lowerCamelCase			:			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 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					string.ascii_letters
  __lowerCamelCase			:			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 			a_	(				) -> None:
  from timeit import timeit

  print('Running performance benchmarks...'			)
  __lowerCamelCase			:			Tuple       			=					'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()






