





'''simple docstring'''




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







class   A_	:




 def __init__(					self       :    int	,			snake_case_       :    str    ):
      _UpperCAmelCase     							=   data
      _UpperCAmelCase     							=   [0X67_45_23_01, 0Xef_cd_ab_89, 0X98_ba_dc_fe, 0X10_32_54_76, 0Xc3_d2_e1_f0]




 @staticmethod
 def      lowercase	(					snake_case_       :    List[str]	,			snake_case_       :    Optional[Any]    ):
      return ((n << b) | (n >> (3_2 - b))) & 0Xff_ff_ff_ff




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   b"\x80" + b"\x00" * (6_3 - (len(self.data    ) + 8) % 6_4)
      _UpperCAmelCase     							=   self.data + padding + struct.pack(">Q"	,			8 * len(self.data    )    )
      return padded_data




 def      lowercase	(					self       :    Optional[int]    ):
      return [
          self.padded_data[i : i + 6_4] for i in range(0	,			len(self.padded_data    )	,			6_4    )
      ]




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   list(struct.unpack(">16L"	,			snake_case_    )    ) + [0] * 6_4
      for i in range(1_6	,			8_0    ):
           _UpperCAmelCase     							=   self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 1_4] ^ w[i - 1_6])	,			1    )
      return w




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   self.padding()
      _UpperCAmelCase     							=   self.split_blocks()
      for block in self.blocks:
           _UpperCAmelCase     							=   self.expand_block(snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   self.h
           for i in range(0	,			8_0    ):
                if 0 <= i < 2_0:
                     _UpperCAmelCase     							=   (b & c) | ((~b) & d)
                     _UpperCAmelCase     							=   0X5a_82_79_99
                elif 2_0 <= i < 4_0:
                     _UpperCAmelCase     							=   b ^ c ^ d
                     _UpperCAmelCase     							=   0X6e_d9_eb_a1
                elif 4_0 <= i < 6_0:
                     _UpperCAmelCase     							=   (b & c) | (b & d) | (c & d)
                     _UpperCAmelCase     							=   0X8f_1b_bc_dc
                elif 6_0 <= i < 8_0:
                     _UpperCAmelCase     							=   b ^ c ^ d
                     _UpperCAmelCase     							=   0Xca_62_c1_d6
                _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   (
                    self.rotate(snake_case_	,			5    ) + f + e + k + expanded_block[i] & 0Xff_ff_ff_ff,
                    a,
                    self.rotate(snake_case_	,			3_0    ),
                    c,
                    d,
                )
           _UpperCAmelCase     							=   (
               self.h[0] + a & 0Xff_ff_ff_ff,
               self.h[1] + b & 0Xff_ff_ff_ff,
               self.h[2] + c & 0Xff_ff_ff_ff,
               self.h[3] + d & 0Xff_ff_ff_ff,
               self.h[4] + e & 0Xff_ff_ff_ff,
           )
      return ("{:08x}" * 5).format(*self.h    )





def 	UpperCAmelCase_							(					)		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   b"Test String"
     assert SHAaHash(__lowercase       ).final_hash() == hashlib.shaa(__lowercase       ).hexdigest()  # noqa: S324
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   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"       )
     _UpperCAmelCase     							=   parser.parse_args()
     _UpperCAmelCase     							=   args.input_string
     # In any case hash input should be a bytestring
     if args.input_file:
          with open(args.input_file			,     "rb"       ) as f:
               _UpperCAmelCase     							=   f.read()
     else:
          _UpperCAmelCase     							=   bytes(__lowercase			,     "utf-8"       )
     print(SHAaHash(__lowercase       ).final_hash()       )


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

  doctest.testmod()





"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 0.9     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	size if size is not None else {'shortest_edge': 224}
        lowercase_	:				Union[str, Any]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase_	:				Optional[int]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				List[str]													=	do_resize
        lowercase_	:				List[Any]													=	size
        lowercase_	:				int													=	crop_pct
        lowercase_	:				Dict													=	resample
        lowercase_	:				List[str]													=	do_center_crop
        lowercase_	:				Union[str, Any]													=	crop_size
        lowercase_	:				List[Any]													=	do_rescale
        lowercase_	:				Tuple													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Union[str, Any]													=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_	:				int													=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				Any													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
        if crop_pct is not None:
             if "shortest_edge" in size:
                  lowercase_	:				Union[str, Any]													=	int(size['shortest_edge'] / crop_pct			)
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_	:				Tuple													=	int(size['height'] / crop_pct			)
                  else:
                       lowercase_	:				Dict													=	(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        else:
             if "shortest_edge" in size:
                  lowercase_	:				Optional[int]													=	get_resize_output_image_size(__UpperCamelCase     ,size=size['shortest_edge']     ,default_to_square=__UpperCamelCase			)
             elif "height" in size and "width" in size:
                  lowercase_	:				Dict													=	(size['height'], size['width'])
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

        return resize(__UpperCamelCase     ,size=__UpperCamelCase     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	get_size_dict(__UpperCamelCase			)
        if "height" not in size or "width" not in size:
             raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''			)
        return center_crop(__UpperCamelCase     ,size=(size['height'], size['width'])     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      str:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


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







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


        '''simple docstring'''


        lowercase_	:				List[Any]													=	do_resize if do_resize is not None else self.do_resize
        lowercase_	:				Optional[int]													=	crop_pct if crop_pct is not None else self.crop_pct
        lowercase_	:				List[str]													=	resample if resample is not None else self.resample
        lowercase_	:				str													=	do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_	:				Tuple													=	do_rescale if do_rescale is not None else self.do_rescale
        lowercase_	:				Union[str, Any]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_	:				str													=	do_normalize if do_normalize is not None else self.do_normalize
        lowercase_	:				str													=	image_mean if image_mean is not None else self.image_mean
        lowercase_	:				Tuple													=	image_std if image_std is not None else self.image_std

        lowercase_	:				Optional[Any]													=	size if size is not None else self.size
        lowercase_	:				Tuple													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else self.crop_size
        lowercase_	:				List[str]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				str													=	make_list_of_images(__UpperCamelCase			)

        if not valid_images(__UpperCamelCase			):
             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_center_crop and crop_pct is None:
             raise ValueError('Crop_pct 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.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

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

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

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

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

        lowercase_	:				Dict													=	[to_channel_dimension_format(__UpperCamelCase     ,__UpperCamelCase			) for image in images]

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

