






'''simple docstring'''



import argparse
import struct
import unittest
class UpperCAmelCase_					:




						"""simple docstring"""




						def __init__( self			:		Union[str, Any]     ,      UpperCAmelCase			:		bytes		)  ->  None:


							'''simple docstring'''

							lowercase					:   Union[str, Any]				  =data

							# Initialize hash values
							lowercase					:   Dict				  =[
							    0X6A_09E_667,
							    0XBB_67A_E85,
							    0X3C_6EF_372,
							    0XA5_4FF_53A,
							    0X51_0E5_27F,
							    0X9B_056_88C,
							    0X1F_83D_9AB,
							    0X5B_E0C_D19,
							]

							# Initialize round constants
							lowercase					:   Tuple				  =[
							    0X42_8A2_F98,
							    0X71_374_491,
							    0XB5_C0F_BCF,
							    0XE9_B5D_BA5,
							    0X39_56C_25B,
							    0X59_F11_1F1,
							    0X92_3F8_2A4,
							    0XAB_1C5_ED5,
							    0XD8_07A_A98,
							    0X12_835_B01,
							    0X24_318_5BE,
							    0X55_0C7_DC3,
							    0X72_BE5_D74,
							    0X80_DEB_1FE,
							    0X9B_DC0_6A7,
							    0XC1_9BF_174,
							    0XE4_9B6_9C1,
							    0XEF_BE4_786,
							    0X0F_C19_DC6,
							    0X24_0CA_1CC,
							    0X2D_E92_C6F,
							    0X4A_748_4AA,
							    0X5C_B0A_9DC,
							    0X76_F98_8DA,
							    0X98_3E5_152,
							    0XA8_31C_66D,
							    0XB0_032_7C8,
							    0XBF_597_FC7,
							    0XC6_E00_BF3,
							    0XD5_A79_147,
							    0X06_CA6_351,
							    0X14_292_967,
							    0X27_B70_A85,
							    0X2E_1B2_138,
							    0X4D_2C6_DFC,
							    0X53_380_D13,
							    0X65_0A7_354,
							    0X76_6A0_ABB,
							    0X81_C2C_92E,
							    0X92_722_C85,
							    0XA2_BFE_8A1,
							    0XA8_1A6_64B,
							    0XC2_4B8_B70,
							    0XC7_6C5_1A3,
							    0XD1_92E_819,
							    0XD6_990_624,
							    0XF4_0E3_585,
							    0X10_6AA_070,
							    0X19_A4C_116,
							    0X1E_376_C08,
							    0X27_487_74C,
							    0X34_B0B_CB5,
							    0X39_1C0_CB3,
							    0X4E_D8A_A4A,
							    0X5B_9CC_A4F,
							    0X68_2E6_FF3,
							    0X74_8F8_2EE,
							    0X78_A56_36F,
							    0X84_C87_814,
							    0X8C_C70_208,
							    0X90_BEF_FFA,
							    0XA4_506_CEB,
							    0XBE_F9A_3F7,
							    0XC6_717_8F2,
							]

							lowercase					:   Dict				  =self.preprocessing(self.data		)
							self.final_hash()






						@staticmethod
						def 				A__      ( UpperCAmelCase			:		bytes		)  ->  bytes:


							'''simple docstring'''

							lowercase					:   str				  =b'''\x80''' + (b'''\x00''' * (63 - (len(__A		) + 8) % 64))
							lowercase					:   Tuple				  =struct.pack('''>Q'''     ,      (len(__A		) * 8)		)
							return data + padding + big_endian_integer






						def 				A__      ( self			:		Dict		)  ->  None:


							'''simple docstring'''

							lowercase					:   Union[str, Any]				  =[
							    self.preprocessed_data[x : x + 64]
							    for x in range(0     ,      len(self.preprocessed_data		)     ,      64		)
							]

							for block in self.blocks:
								# Convert the given block into a list of 4 byte integers
								lowercase					:   Tuple				  =list(struct.unpack('''>16L'''     ,      __A		)		)
								# add 48 0-ed integers
								words += [0] * 48

								lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =self.hashes

								for index in range(0     ,      64		):
									if index > 15:
										# modify the zero-ed indexes at the end of the array
										lowercase					:   Tuple				  =(
										    self.ror(words[index - 15]     ,      7		)
										    ^ self.ror(words[index - 15]     ,      18		)
										    ^ (words[index - 15] >> 3)
										)
										lowercase					:   List[Any]				  =(
										    self.ror(words[index - 2]     ,      17		)
										    ^ self.ror(words[index - 2]     ,      19		)
										    ^ (words[index - 2] >> 10)
										)

										lowercase					:   int				  =(
										    words[index - 16] + sa + words[index - 7] + sa
										) % 0X100_000_000

									# Compression
									lowercase					:   Optional[int]				  =self.ror(__A     ,      6		) ^ self.ror(__A     ,      11		) ^ self.ror(__A     ,      25		)
									lowercase					:   List[str]				  =(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
									lowercase					:   Union[str, Any]				  =(
									    h + sa + ch + self.round_constants[index] + words[index]
									) % 0X100_000_000
									lowercase					:   Optional[int]				  =self.ror(__A     ,      2		) ^ self.ror(__A     ,      13		) ^ self.ror(__A     ,      22		)
									lowercase					:   Optional[Any]				  =(a & b) ^ (a & c) ^ (b & c)
									lowercase					:   Union[str, Any]				  =(sa + maj) % 0X100_000_000

									lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   List[Any]				  =(
									    g,
									    f,
									    e,
									    ((d + tempa) % 0X100_000_000),
									    c,
									    b,
									    a,
									    ((tempa + tempa) % 0X100_000_000),
									)

								lowercase					:   Dict				  =[a, b, c, d, e, f, g, h]

								# Modify final values
								lowercase					:   Optional[Any]				  =[
								    ((element + mutated_hash_values[index]) % 0X100_000_000)
								    for index, element in enumerate(self.hashes		)
								]

							lowercase					:   Any				  =''''''.join([hex(__A		)[2:].zfill(8		) for value in self.hashes]		)






						def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int		)  ->  int:


							'''simple docstring'''

							return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)





class UpperCAmelCase_					(    unittest.TestCase				):




						"""simple docstring"""




						def 				A__      ( self			:		Dict		)  ->  None:


							'''simple docstring'''

							import hashlib

							lowercase					:   int				  =bytes('''Test String'''     ,      '''utf-8'''		)
							self.assertEqual(SHAaaa(__A		).hash     ,      hashlib.shaaaa(__A		).hexdigest()		)





def 							lowercase_				(    )       ->      List[str]:



	"""simple docstring"""




	import doctest

	doctest.testmod()

	lowercase					:   Optional[int]				  =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'''			)

	lowercase					:   List[Any]				  =parser.parse_args()

	lowercase					:   Optional[int]				  =args.input_string

	# hash input should be a bytestring
	if args.input_file:
		with open(args.input_file					,      '''rb'''			) as f:
			lowercase					:   Dict				  =f.read()
	else:
		lowercase					:   Tuple				  =bytes(_lowercase					,      '''utf-8'''			)

	print(SHAaaa(_lowercase			).hash			)


if __name__ == "__main__":
						main()











'''simple docstring'''



from typing import Dict, Iterable, 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, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       List[Any]					,      __A :       int					,      __A :       int			)       ->      Optional[int]:



 """simple docstring"""




 return [
     int(1_0_0_0 * (box[0] / width)			),
     int(1_0_0_0 * (box[1] / height)			),
     int(1_0_0_0 * (box[2] / width)			),
     int(1_0_0_0 * (box[3] / height)			),
 ]


def 							lowercase_				(    __A :       np.ndarray					,      __A :       Optional[str]					,      __A :       Optional[str]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   int				  =to_pil_image(__A			)
 lowercase  ,		lowercase					:   Tuple				  =pil_image.size
 lowercase					:   Optional[Any]				  =pytesseract.image_to_data(__A					,      lang=__A					,      output_type='''dict'''					,      config=__A			)
 lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

 # filter empty words and corresponding coordinates
 lowercase					:   Dict				  =[idx for idx, word in enumerate(__A			) if not word.strip()]
 lowercase					:   str				  =[word for idx, word in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   Optional[int]				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   List[Any]				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   str				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   int				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]

 # turn coordinates into (left, top, left+width, top+height) format
 lowercase					:   Tuple				  =[]
 for x, y, w, h in zip(__A					,      __A					,      __A					,      __A			):
  lowercase					:   str				  =[x, y, x + w, y + h]
  actual_boxes.append(__A			)

 # finally, normalize the bounding boxes
 lowercase					:   List[str]				  =[]
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(__A					,      __A					,      __A			)			)

 assert len(__A			) == len(__A			), "Not as many words as there are bounding boxes"

 return words, normalized_boxes





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		float = 1 / 255     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = ""     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase					:   Tuple				  =size if size is not None else {'''height''': 224, '''width''': 224}
       lowercase					:   Optional[Any]				  =get_size_dict(UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =do_resize
       lowercase					:   List[Any]				  =size
       lowercase					:   List[str]				  =resample
       lowercase					:   Dict				  =do_rescale
       lowercase					:   str				  =rescale_value
       lowercase					:   Optional[int]				  =do_normalize
       lowercase					:   Any				  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase					:   Union[str, Any]				  =image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowercase					:   List[Any]				  =apply_ocr
       lowercase					:   Union[str, Any]				  =ocr_lang
       lowercase					:   str				  =tesseract_config






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Dict[str, int]     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       lowercase					:   Tuple				  =get_size_dict(UpperCAmelCase		)
       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()}'		)
       lowercase					:   Optional[Any]				  =(size['''height'''], size['''width'''])
       return resize(UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[int, float]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  np.ndarray:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		ImageInput     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		float = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		ChannelDimension = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		List[str]     ,      )  ->  PIL.Image.Image:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =do_resize if do_resize is not None else self.do_resize
       lowercase					:   Tuple				  =size if size is not None else self.size
       lowercase					:   Optional[int]				  =get_size_dict(UpperCAmelCase		)
       lowercase					:   List[str]				  =resample if resample is not None else self.resample
       lowercase					:   List[Any]				  =do_rescale if do_rescale is not None else self.do_rescale
       lowercase					:   List[Any]				  =rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase					:   Optional[int]				  =do_normalize if do_normalize is not None else self.do_normalize
       lowercase					:   List[Any]				  =image_mean if image_mean is not None else self.image_mean
       lowercase					:   Optional[int]				  =image_std if image_std is not None else self.image_std
       lowercase					:   Any				  =apply_ocr if apply_ocr is not None else self.apply_ocr
       lowercase					:   Any				  =ocr_lang if ocr_lang is not None else self.ocr_lang
       lowercase					:   Dict				  =tesseract_config if tesseract_config is not None else self.tesseract_config

       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:
        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.'''		)

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''		)

       # All transformations expect numpy arrays.
       lowercase					:   Tuple				  =[to_numpy_array(UpperCAmelCase		) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
        requires_backends(self     ,      '''pytesseract'''		)
        lowercase					:   int				  =[]
        lowercase					:   Tuple				  =[]
        for image in images:
         lowercase  ,		lowercase					:   Dict				  =apply_tesseract(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)
         words_batch.append(UpperCAmelCase		)
         boxes_batch.append(UpperCAmelCase		)

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

       if do_rescale:
        lowercase					:   Tuple				  =[self.rescale(image=UpperCAmelCase     ,      scale=UpperCAmelCase		) for image in images]

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

       lowercase					:   Optional[Any]				  =[to_channel_dimension_format(UpperCAmelCase     ,      UpperCAmelCase		) for image in images]

       lowercase					:   Dict				  =BatchFeature(data={'''pixel_values''': images}     ,      tensor_type=UpperCAmelCase		)

       if apply_ocr:
        lowercase					:   int				  =words_batch
        lowercase					:   List[str]				  =boxes_batch
       return data



