





import argparse
import struct
import unittest





class 				A_				:







       """simple docstring"""





       def __init__(    self	:	Union[str, Any]						,__A	:	bytes  )						->					None:
         _lowercase							= data

         # Initialize hash values
         _lowercase							= [
             0x6A09E667,
             0xBB67AE85,
             0x3C6EF372,
             0xA54FF53A,
             0x510E527F,
             0x9B05688C,
             0x1F83D9AB,
             0x5BE0CD19,
         ]

         # Initialize round constants
         _lowercase							= [
             0x428A2F98,
             0x71374491,
             0xB5C0FBCF,
             0xE9B5DBA5,
             0x3956C25B,
             0x59F111F1,
             0x923F82A4,
             0xAB1C5ED5,
             0xD807AA98,
             0x12835B01,
             0x243185BE,
             0x550C7DC3,
             0x72BE5D74,
             0x80DEB1FE,
             0x9BDC06A7,
             0xC19BF174,
             0xE49B69C1,
             0xEFBE4786,
             0x0FC19DC6,
             0x240CA1CC,
             0x2DE92C6F,
             0x4A7484AA,
             0x5CB0A9DC,
             0x76F988DA,
             0x983E5152,
             0xA831C66D,
             0xB00327C8,
             0xBF597FC7,
             0xC6E00BF3,
             0xD5A79147,
             0x06CA6351,
             0x14292967,
             0x27B70A85,
             0x2E1B2138,
             0x4D2C6DFC,
             0x53380D13,
             0x650A7354,
             0x766A0ABB,
             0x81C2C92E,
             0x92722C85,
             0xA2BFE8A1,
             0xA81A664B,
             0xC24B8B70,
             0xC76C51A3,
             0xD192E819,
             0xD6990624,
             0xF40E3585,
             0x106AA070,
             0x19A4C116,
             0x1E376C08,
             0x2748774C,
             0x34B0BCB5,
             0x391C0CB3,
             0x4ED8AA4A,
             0x5B9CCA4F,
             0x682E6FF3,
             0x748F82EE,
             0x78A5636F,
             0x84C87814,
             0x8CC70208,
             0x90BEFFFA,
             0xA4506CEB,
             0xBEF9A3F7,
             0xC67178F2,
         ]

         _lowercase							= self.preprocessing(self.data  )
         self.final_hash()







       @staticmethod
       def    __UpperCAmelCase (    __A	:	bytes  )						->					bytes:
         _lowercase							= B'\x80' + (B'\x00' * (63 - (len(__A  ) + 8) % 64))
         _lowercase							= struct.pack('>Q'						,(len(__A  ) * 8)  )
         return data + padding + big_endian_integer







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					None:
         # Convert into blocks of 64 bytes
         _lowercase							= [
             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							= list(struct.unpack('>16L'						,__A  )  )
           # add 48 0-ed integers
           words += [0] * 48

           _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= self.hashes

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

               _lowercase							= (
                   words[index - 16] + sa + words[index - 7] + sa
               ) % 0x100000000

             # Compression
             _lowercase							= self.ror(__A						,6  ) ^ self.ror(__A						,11  ) ^ self.ror(__A						,25  )
             _lowercase							= (e & f) ^ ((~e & 0xFFFFFFFF) & g)
             _lowercase							= (
                 h + sa + ch + self.round_constants[index] + words[index]
             ) % 0x100000000
             _lowercase							= self.ror(__A						,2  ) ^ self.ror(__A						,13  ) ^ self.ror(__A						,22  )
             _lowercase							= (a & b) ^ (a & c) ^ (b & c)
             _lowercase							= (sa + maj) % 0x100000000

             _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= (
                 g,
                 f,
                 e,
                 ((d + tempa) % 0x100000000),
                 c,
                 b,
                 a,
                 ((tempa + tempa) % 0x100000000),
             )

           _lowercase							= [a, b, c, d, e, f, g, h]

           # Modify final values
           _lowercase							= [
               ((element + mutated_hash_values[index]) % 0x100000000)
               for index, element in enumerate(self.hashes  )
           ]

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







       def    __UpperCAmelCase (    self	:	int						,__A	:	int						,__A	:	int  )						->					int:
         return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	List[str]  )						->					None:
         import hashlib

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

def 							SCREAMING_SNAKE_CASE__       (  )      ->						None:
  import doctest

  doctest.testmod()

  _lowercase							= 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							= parser.parse_args()

  _lowercase							= args.input_string

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

  print(SHAaaa(snake_case__     ).hash     )


if __name__ == "__main__":
      main()






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


snake_case 				=       logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer





      class 				A_				(      UpperCAmelCase	):







             """simple docstring"""





             def __init__(    self	:	str						,__A	:	int = 101  )						->					Optional[int]:
               _lowercase							= length







             def __len__(    self	:	List[Any]  )						->					Any:
               return self.length







             def __getitem__(    self	:	List[Any]						,__A	:	Optional[int]  )						->					int:
               return i





      class 				A_				:







             """simple docstring"""





             def __call__(    self	:	str						,__A	:	Union[str, Any]  )						->					Any:
               return {"input_ids": torch.tensor(__A  ), "labels": torch.tensor(__A  )}





      class 				A_				(      nn.Module	):







             """simple docstring"""





             def __init__(    self	:	Dict  )						->					Any:
               super().__init__()
               # Add some (unused) params otherwise DDP will complain.
               _lowercase							= nn.Linear(120						,80  )







             def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict						,__A	:	Any=None  )						->					Tuple:
               if labels is not None:
                 return torch.tensor(0.0						,device=input_ids.device  ), input_ids
               else:
                 return input_ids





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_neuroncore
       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_multi_gpu
       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         _lowercase							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
      # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
      #
      # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

      snake_case 				=       HfArgumentParser((TrainingArguments,))
      snake_case 				=       parser.parse_args_into_dataclasses()[0]

      logger.warning(
          F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
          F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
      )

      # Essentially, what we want to verify in the distributed case is that we get all samples back,
      # in the right order. (this is crucial for prediction for instance)
      for dataset_length in [1_0_1, 4_0, 7]:
            snake_case 				=       DummyDataset(dataset_length)

            def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :EvalPrediction     )      ->						Dict:
              _lowercase							= list(range(len(snake_case__     )     )     )
              _lowercase							= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
              if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""     )
              return {"success": success}

            snake_case 				=       Trainer(
                model=DummyModel(),
                args=training_args,
                data_collator=DummyDataCollator(),
                eval_dataset=dataset,
                compute_metrics=compute_metrics,
            )
            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            snake_case 				=       trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)

            snake_case 				=       2

            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            snake_case 				=       trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)







            snake_case 				=       None