'''simple docstring'''





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




class 			__A :



    def __init__(self       :       str  ,     __a       :       Any					):
         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		(__a       :       List[str]  ,     __a       :       str					):
         return ((n << b) | (n >> (32 - b))) & 0Xff_ff_ff_ff



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  B"\x80" + B"\x00" * (63 - (len(self.data					) + 8) % 64)
         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 + 64] for i in range(0  ,     len(self.padded_data					)  ,     64					)
         ]



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  list(struct.unpack(">16L"  ,     __a					)					) + [0] * 64
         for i in range(16  ,     80					):
              UpperCAmelCase_			       =  self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])  ,     1					)
         return w






    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.padding()
         UpperCAmelCase_			       =  self.split_blocks()
         for block in self.blocks:
              UpperCAmelCase_			       =  self.expand_block(__a					)
              UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.h
              for i in range(0  ,     80					):
                   if 0 <= i < 20:
                        UpperCAmelCase_			       =  (b & c) | ((~b) & d)
                        UpperCAmelCase_			       =  0X5a_82_79_99
                   elif 20 <= i < 40:
                        UpperCAmelCase_			       =  b ^ c ^ d
                        UpperCAmelCase_			       =  0X6e_d9_eb_a1
                   elif 40 <= i < 60:
                        UpperCAmelCase_			       =  (b & c) | (b & d) | (c & d)
                        UpperCAmelCase_			       =  0X8f_1b_bc_dc
                   elif 60 <= i < 80:
                        UpperCAmelCase_			       =  b ^ c ^ d
                        UpperCAmelCase_			       =  0Xca_62_c1_d6
                   UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  (
                       self.rotate(__a  ,     5					) + f + e + k + expanded_block[i] & 0Xff_ff_ff_ff,
                       a,
                       self.rotate(__a  ,     30					),
                       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       lowerCAmelCase_							(   )       -> Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  B"Test String"
     assert SHAaHash(snake_case_     ).final_hash() == hashlib.shaa(snake_case_     ).hexdigest()  # noqa: S324





def       lowerCAmelCase_							(   )       -> Dict:


     '''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(snake_case_   ,       "utf-8"     )
     print(SHAaHash(snake_case_     ).final_hash()     )


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

    doctest.testmod()






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






