

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase           =					logging.get_logger(__name__)


if is_vision_available():
       import PIL







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	int    =   ['pixel_values']



   def __init__(				self,    lowercase_ = True,    lowercase_ = None,    lowercase_ = PILImageResampling.BICUBIC,    lowercase_ = True,    lowercase_ = None,    lowercase_ = True,    lowercase_ = 1 / 255,    lowercase_ = True,    lowercase_ = None,    lowercase_ = None,    lowercase_ = True,    **lowercase_,    )							->    None:






     """simple docstring"""






     super().__init__(**lowercase_       )
     a__  =size if size is not None else {'''shortest_edge''': 224}
     a__  =get_size_dict(lowercase_,    default_to_square=lowercase_       )
     a__  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     a__  =get_size_dict(lowercase_,    default_to_square=lowercase_,    param_name='''crop_size'''       )

     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 if image_mean is not None else OPENAI_CLIP_MEAN
     a__  =image_std if image_std is not None else OPENAI_CLIP_STD
     a__  =do_convert_rgb



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_ = PILImageResampling.BICUBIC,    lowercase_ = None,    **lowercase_,    )							->    np.ndarray:






     """simple docstring"""






     a__  =get_size_dict(lowercase_,    default_to_square=lowercase_       )
     if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
     a__  =get_resize_output_image_size(lowercase_,    size=size['''shortest_edge'''],    default_to_square=lowercase_       )
     return resize(lowercase_,    size=lowercase_,    resample=lowercase_,    data_format=lowercase_,    **lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_ = None,    **lowercase_,    )							->    np.ndarray:






     """simple docstring"""






     a__  =get_size_dict(lowercase_       )
     if "height" not in size or "width" not in size:
       raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
     return center_crop(lowercase_,    size=(size['''height'''], size['''width''']),    data_format=lowercase_,    **lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_ = None,    **lowercase_,    )							->    List[str]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_ = None,    **lowercase_,    )							->    np.ndarray:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = ChannelDimension.FIRST,    **lowercase_,    )							->    PIL.Image.Image:






     """simple docstring"""






     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(lowercase_,    param_name='''size''',    default_to_square=lowercase_       )
     a__  =resample if resample is not None else self.resample
     a__  =do_center_crop if do_center_crop is not None else self.do_center_crop
     a__  =crop_size if crop_size is not None else self.crop_size
     a__  =get_size_dict(lowercase_,    param_name='''crop_size''',    default_to_square=lowercase_       )
     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__  =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     a__  =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:
       raise ValueError('''Size 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.'''       )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       a__  =[convert_to_rgb(lowercase_       ) for image in images]

     # All transformations expect numpy arrays.
     a__  =[to_numpy_array(lowercase_       ) for image in images]

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

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

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

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

     a__  =[to_channel_dimension_format(lowercase_,    lowercase_       ) for image in images]

     a__  ={'''pixel_values''': images}
     return BatchFeature(data=lowercase_,    tensor_type=lowercase_       )



import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =data
     a__  =[0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]



   @staticmethod
   def  _UpperCAmelCase (				lowercase_,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     return ((n << b) | (n >> (32 - b))) & 0Xffffffff



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =b'''\x80''' + b'''\x00''' * (63 - (len(self.data       ) + 8) % 64)
     a__  =self.data + padding + struct.pack('''>Q''',    8 * len(self.data       )       )
     return padded_data



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     return [
         self.padded_data[i : i + 64] for i in range(0,    len(self.padded_data       ),    64       )
     ]



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[Any]:






     """simple docstring"""






     a__  =list(struct.unpack('''>16L''',    lowercase_       )       ) + [0] * 64
     for i in range(16,    80       ):
       a__  =self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),    1       )
     return w



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.padding()
     a__  =self.split_blocks()
     for block in self.blocks:
       a__  =self.expand_block(lowercase_       )
       a__,      a__,      a__,      a__,      a__  =self.h
       for i in range(0,    80       ):
         if 0 <= i < 20:
           a__  =(b & c) | ((~b) & d)
           a__  =0X5a827999
         elif 20 <= i < 40:
           a__  =b ^ c ^ d
           a__  =0X6ed9eba1
         elif 40 <= i < 60:
           a__  =(b & c) | (b & d) | (c & d)
           a__  =0X8f1bbcdc
         elif 60 <= i < 80:
           a__  =b ^ c ^ d
           a__  =0Xca62c1d6
         a__,      a__,      a__,      a__,      a__  =(
             self.rotate(lowercase_,    5       ) + f + e + k + expanded_block[i] & 0Xffffffff,
             a,
             self.rotate(lowercase_,    30       ),
             c,
             d,
         )
       a__  =(
           self.h[0] + a & 0Xffffffff,
           self.h[1] + b & 0Xffffffff,
           self.h[2] + c & 0Xffffffff,
           self.h[3] + d & 0Xffffffff,
           self.h[4] + e & 0Xffffffff,
       )
     return ("{:08x}" * 5).format(*self.h       )





def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =b'''Test String'''
  assert SHAaHash(_A							).final_hash() == hashlib.shaa(_A							).hexdigest()  # noqa: S324




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =argparse.ArgumentParser(description='''Process some strings or files'''							)
  parser.add_argument(
      '''--string'''	,      dest='''input_string'''	,      default='''Hello World!! Welcome to Cryptography'''	,      help='''Hash the string'''	,      )
  parser.add_argument('''--file'''	,      dest='''input_file'''	,      help='''Hash contents of a file'''							)
  a__  =parser.parse_args()
  a__  =args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file	,      '''rb'''							) as f:
      a__  =f.read()
  else:
    a__  =bytes(_A	,      '''utf-8'''							)
  print(SHAaHash(_A							).final_hash()							)


if __name__ == "__main__":
       main()
       import doctest

       doctest.testmod()
