



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 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, logging


UpperCamelCase_									=   logging.get_logger(__name__)







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Optional[Any]       =						["pixel_values"]


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Dict[str, int]] = None  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[int, float] = 1 / 255  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,**lowerCamelCase_:     Dict  ,)	->      None:
       super().__init__(**lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					size if size is not None else {"""height""": 224, """width""": 224}
       UpperCAmelCase_     :       str  		=					get_size_dict(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					do_resize
       UpperCAmelCase_     :       Optional[Any]  		=					do_rescale
       UpperCAmelCase_     :       Optional[Any]  		=					do_normalize
       UpperCAmelCase_     :       int  		=					size
       UpperCAmelCase_     :       List[Any]  		=					resample
       UpperCAmelCase_     :       Optional[Any]  		=					rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase_     :       Any  		=					image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Dict[str, int]  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      np.ndarray:
       UpperCAmelCase_     :       Dict  		=					get_size_dict(lowerCamelCase_							)
       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 resize(
           lowerCamelCase_  ,size=(size["""height"""], size["""width"""])  ,resample=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


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


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






   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     ImageInput  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Dict[str, int] = None  ,lowerCamelCase_:     PILImageResampling = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[float] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,lowerCamelCase_:     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**lowerCamelCase_:     int  ,)	->      List[Any]:
       UpperCAmelCase_     :       str  		=					do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_     :       Dict  		=					do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_     :       Dict  		=					do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_     :       int  		=					resample if resample is not None else self.resample
       UpperCAmelCase_     :       str  		=					rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_     :       int  		=					image_std if image_std is not None else self.image_std

       UpperCAmelCase_     :       Tuple  		=					size if size is not None else self.size
       UpperCAmelCase_     :       Union[str, Any]  		=					get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					make_list_of_images(lowerCamelCase_							)

       if not valid_images(lowerCamelCase_							):
           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."""							)

       # All transformations expect numpy arrays.
       UpperCAmelCase_     :       Optional[Any]  		=					[to_numpy_array(lowerCamelCase_							) for image in images]

       if do_resize:
           UpperCAmelCase_     :       int  		=					[self.resize(image=lowerCamelCase_  ,size=lowerCamelCase_  ,resample=lowerCamelCase_							) for image in images]

       if do_rescale:
           UpperCAmelCase_     :       Optional[Any]  		=					[self.rescale(image=lowerCamelCase_  ,scale=lowerCamelCase_							) for image in images]

       if do_normalize:
           UpperCAmelCase_     :       str  		=					[self.normalize(image=lowerCamelCase_  ,mean=lowerCamelCase_  ,std=lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       Any  		=					[to_channel_dimension_format(lowerCamelCase_  ,lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       List[str]  		=					{"""pixel_values""": images}
       return BatchFeature(data=lowerCamelCase_  ,tensor_type=lowerCamelCase_							)




'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher






def __UpperCamelCase (      )   ->  None:







     '''simple docstring'''






     _a            =		"Prehistoric Men.txt"
     _a            =		"Output.txt"
     _a            =		int(input("Enter key: "					)					)
     _a            =		input("Encrypt/Decrypt [e/d]: "					)

     if not os.path.exists(__lowerCamelCase					):
          print(F"File {input_file} does not exist. Quitting..."					)
          sys.exit()
     if os.path.exists(__lowerCamelCase					):
          print(F"Overwrite {output_file}? [y/n]"					)
          _a            =		input("> "					)
          if not response.lower().startswith("y"					):
               sys.exit()

     _a            =		time.time()
     if mode.lower().startswith("e"					):
          with open(__lowerCamelCase					) as f:
               _a            =		f.read()
          _a            =		trans_cipher.encrypt_message(__lowerCamelCase   ,	__lowerCamelCase					)
     elif mode.lower().startswith("d"					):
          with open(__lowerCamelCase					) as f:
               _a            =		f.read()
          _a            =		trans_cipher.decrypt_message(__lowerCamelCase   ,	__lowerCamelCase					)

     with open(__lowerCamelCase   ,	"w"					) as output_obj:
          output_obj.write(__lowerCamelCase					)

     _a            =		round(time.time() - start_time   ,	2					)
     print(("Done (", total_time, "seconds )")					)


if __name__ == "__main__":
   main()

