

import argparse
import struct
import unittest





class A__    :



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bytes       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    data

      # Initialize hash values
      _lowerCAmelCase   :					Optional[Any]        =    [
          0X6_a_0_9_e_6_6_7,
          0Xb_b_6_7_a_e_8_5,
          0X3_c_6_e_f_3_7_2,
          0Xa_5_4_f_f_5_3_a,
          0X5_1_0_e_5_2_7_f,
          0X9_b_0_5_6_8_8_c,
          0X1_f_8_3_d_9_a_b,
          0X5_b_e_0_c_d_1_9,
      ]

      # Initialize round constants
      _lowerCAmelCase   :					str        =    [
          0X4_2_8_a_2_f_9_8,
          0X7_1_3_7_4_4_9_1,
          0Xb_5_c_0_f_b_c_f,
          0Xe_9_b_5_d_b_a_5,
          0X3_9_5_6_c_2_5_b,
          0X5_9_f_1_1_1_f_1,
          0X9_2_3_f_8_2_a_4,
          0Xa_b_1_c_5_e_d_5,
          0Xd_8_0_7_a_a_9_8,
          0X1_2_8_3_5_b_0_1,
          0X2_4_3_1_8_5_b_e,
          0X5_5_0_c_7_d_c_3,
          0X7_2_b_e_5_d_7_4,
          0X8_0_d_e_b_1_f_e,
          0X9_b_d_c_0_6_a_7,
          0Xc_1_9_b_f_1_7_4,
          0Xe_4_9_b_6_9_c_1,
          0Xe_f_b_e_4_7_8_6,
          0X0_f_c_1_9_d_c_6,
          0X2_4_0_c_a_1_c_c,
          0X2_d_e_9_2_c_6_f,
          0X4_a_7_4_8_4_a_a,
          0X5_c_b_0_a_9_d_c,
          0X7_6_f_9_8_8_d_a,
          0X9_8_3_e_5_1_5_2,
          0Xa_8_3_1_c_6_6_d,
          0Xb_0_0_3_2_7_c_8,
          0Xb_f_5_9_7_f_c_7,
          0Xc_6_e_0_0_b_f_3,
          0Xd_5_a_7_9_1_4_7,
          0X0_6_c_a_6_3_5_1,
          0X1_4_2_9_2_9_6_7,
          0X2_7_b_7_0_a_8_5,
          0X2_e_1_b_2_1_3_8,
          0X4_d_2_c_6_d_f_c,
          0X5_3_3_8_0_d_1_3,
          0X6_5_0_a_7_3_5_4,
          0X7_6_6_a_0_a_b_b,
          0X8_1_c_2_c_9_2_e,
          0X9_2_7_2_2_c_8_5,
          0Xa_2_b_f_e_8_a_1,
          0Xa_8_1_a_6_6_4_b,
          0Xc_2_4_b_8_b_7_0,
          0Xc_7_6_c_5_1_a_3,
          0Xd_1_9_2_e_8_1_9,
          0Xd_6_9_9_0_6_2_4,
          0Xf_4_0_e_3_5_8_5,
          0X1_0_6_a_a_0_7_0,
          0X1_9_a_4_c_1_1_6,
          0X1_e_3_7_6_c_0_8,
          0X2_7_4_8_7_7_4_c,
          0X3_4_b_0_b_c_b_5,
          0X3_9_1_c_0_c_b_3,
          0X4_e_d_8_a_a_4_a,
          0X5_b_9_c_c_a_4_f,
          0X6_8_2_e_6_f_f_3,
          0X7_4_8_f_8_2_e_e,
          0X7_8_a_5_6_3_6_f,
          0X8_4_c_8_7_8_1_4,
          0X8_c_c_7_0_2_0_8,
          0X9_0_b_e_f_f_f_a,
          0Xa_4_5_0_6_c_e_b,
          0Xb_e_f_9_a_3_f_7,
          0Xc_6_7_1_7_8_f_2,
      ]

      _lowerCAmelCase   :					Any        =    self.preprocessing(self.data       )
      self.final_hash()

 @staticmethod
 def    __magic_name__       (		A_ :	bytes       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    B"\x80" + (B"\x00" * (6_3 - (len(A_       ) + 8) % 6_4))
      _lowerCAmelCase   :					str        =    struct.pack(">Q"     ,						(len(A_       ) * 8)       )
      return data + padding + big_endian_integer

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    [
          self.preprocessed_data[x : x + 6_4]
          for x in range(0     ,						len(self.preprocessed_data       )     ,						6_4       )
      ]

      for block in self.blocks:
           # Convert the given block into a list of 4 byte integers
           _lowerCAmelCase   :					List[str]        =    list(struct.unpack(">16L"     ,						A_       )       )
           # add 48 0-ed integers
           words += [0] * 4_8

           _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    self.hashes

           for index in range(0     ,						6_4       ):
                if index > 1_5:
                     # modify the zero-ed indexes at the end of the array
                     _lowerCAmelCase   :					List[str]        =    (
                         self.ror(words[index - 1_5]     ,						7       )
                         ^ self.ror(words[index - 1_5]     ,						1_8       )
                         ^ (words[index - 1_5] >> 3)
                     )
                     _lowerCAmelCase   :					List[str]        =    (
                         self.ror(words[index - 2]     ,						1_7       )
                         ^ self.ror(words[index - 2]     ,						1_9       )
                         ^ (words[index - 2] >> 1_0)
                     )

                     _lowerCAmelCase   :					Any        =    (
                         words[index - 1_6] + sa + words[index - 7] + sa
                     ) % 0X1_0_0_0_0_0_0_0_0

                # Compression
                _lowerCAmelCase   :					Optional[int]        =    self.ror(A_     ,						6       ) ^ self.ror(A_     ,						1_1       ) ^ self.ror(A_     ,						2_5       )
                _lowerCAmelCase   :					Union[str, Any]        =    (e & f) ^ ((~e & 0Xf_f_f_f_f_f_f_f) & g)
                _lowerCAmelCase   :					Optional[Any]        =    (
                    h + sa + ch + self.round_constants[index] + words[index]
                ) % 0X1_0_0_0_0_0_0_0_0
                _lowerCAmelCase   :					str        =    self.ror(A_     ,						2       ) ^ self.ror(A_     ,						1_3       ) ^ self.ror(A_     ,						2_2       )
                _lowerCAmelCase   :					Optional[Any]        =    (a & b) ^ (a & c) ^ (b & c)
                _lowerCAmelCase   :					Optional[int]        =    (sa + maj) % 0X1_0_0_0_0_0_0_0_0

                _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    (
                    g,
                    f,
                    e,
                    ((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
                    c,
                    b,
                    a,
                    ((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
                )

           _lowerCAmelCase   :					Optional[Any]        =    [a, b, c, d, e, f, g, h]

           # Modify final values
           _lowerCAmelCase   :					List[str]        =    [
               ((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
               for index, element in enumerate(self.hashes       )
           ]

      _lowerCAmelCase   :					List[str]        =    "".join([hex(A_       )[2:].zfill(8       ) for value in self.hashes]       )


 def    __magic_name__       (		self :	int     ,						A_ :	int     ,						A_ :	int       ):






      '''simple docstring'''




      return 0Xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      import hashlib

      _lowerCAmelCase   :					Optional[int]        =    bytes("Test String"     ,						"utf-8"       )
      self.assertEqual(SHAaaa(A_       ).hash     ,						hashlib.shaaaa(A_       ).hexdigest()       )







def 	_snake_case		(    )       ->      None:

     """simple docstring"""







     import doctest

     doctest.testmod()

     _lowerCAmelCase   :					List[str]        =    argparse.ArgumentParser()
     parser.add_argument(
         "-s"		,							"--string"		,							dest="input_string"		,							default="Hello World!! Welcome to Cryptography"		,							help="Hash the string"		,							)
     parser.add_argument(
         "-f"		,							"--file"		,							dest="input_file"		,							help="Hash contents of a file"				)

     _lowerCAmelCase   :					Any        =    parser.parse_args()

     _lowerCAmelCase   :					Union[str, Any]        =    args.input_string

     # hash input should be a bytestring
     if args.input_file:
          with open(args.input_file		,							"rb"				) as f:
               _lowerCAmelCase   :					str        =    f.read()
     else:
          _lowerCAmelCase   :					List[Any]        =    bytes(SCREAMING_SNAKE_CASE		,							"utf-8"				)

     print(SHAaaa(SCREAMING_SNAKE_CASE				).hash				)


if __name__ == "__main__":
  main()









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, 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, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	List[str]     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					str        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Union[str, Any]        =    do_center_crop
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return resize(
          A_     ,						size=(size["height"], size["width"])     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

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






      '''simple docstring'''




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

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






      '''simple docstring'''




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


 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[str, Any]=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_ :	Dict     ,						):






      '''simple docstring'''




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

      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					int        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Tuple        =    make_list_of_images(A_       )

      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."       )

      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_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					List[str]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

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

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

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					int        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )






