


import argparse
import struct
import unittest






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[int]     =       data

        # Initialize hash values
        lowercase_       :Any     =       [
            0X6A09E667,
            0XBB67AE85,
            0X3C6EF372,
            0XA54FF53A,
            0X510E527F,
            0X9B05688C,
            0X1F83D9AB,
            0X5BE0CD19,
        ]

        # Initialize round constants
        lowercase_       :Optional[int]     =       [
            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_       :List[Any]     =       self.preprocessing(self.data		)
        self.final_hash()
   @staticmethod
   def 		UpperCamelCase  (     UpperCamelCase_		):
        lowercase_       :Optional[Any]     =       b"""\x80""" + (b"""\x00""" * (63 - (len(A__		) + 8) % 64))
        lowercase_       :List[str]     =       struct.pack('''>Q'''   ,							(len(A__		) * 8)		)
        return data + padding + big_endian_integer
   def 		UpperCamelCase  (     self		):
        # Convert into blocks of 64 bytes
        lowercase_       :str     =       [
            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_       :Optional[int]     =       list(struct.unpack('''>16L'''   ,							A__		)		)
             # add 48 0-ed integers
             words += [0] * 48

             lowercase_       :Any     =       self.hashes

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

                       lowercase_       :List[str]     =       (
                           words[index - 16] + sa + words[index - 7] + sa
                       ) % 0X100000000

                  # Compression
                  lowercase_       :Optional[int]     =       self.ror(A__   ,							6		) ^ self.ror(A__   ,							11		) ^ self.ror(A__   ,							25		)
                  lowercase_       :List[Any]     =       (e & f) ^ ((~e & 0XFFFFFFFF) & g)
                  lowercase_       :List[Any]     =       (
                      h + sa + ch + self.round_constants[index] + words[index]
                  ) % 0X100000000
                  lowercase_       :str     =       self.ror(A__   ,							2		) ^ self.ror(A__   ,							13		) ^ self.ror(A__   ,							22		)
                  lowercase_       :str     =       (a & b) ^ (a & c) ^ (b & c)
                  lowercase_       :Dict     =       (sa + maj) % 0X100000000

                  lowercase_       :Optional[int]     =       (
                      g,
                      f,
                      e,
                      ((d + tempa) % 0X100000000),
                      c,
                      b,
                      a,
                      ((tempa + tempa) % 0X100000000),
                  )

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

             # Modify final values
             lowercase_       :int     =       [
                 ((element + mutated_hash_values[index]) % 0X100000000)
                 for index, element in enumerate(self.hashes		)
             ]

        lowercase_       :List[Any]     =       """""".join([hex(A__		)[2:].zfill(8		) for value in self.hashes]		)




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return 0XFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        import hashlib

        lowercase_       :Optional[Any]     =       bytes('''Test String'''   ,							'''utf-8'''		)
        self.assertEqual(SHAaaa(A__		).hash   ,							hashlib.shaaaa(A__		).hexdigest()		)




def     UpperCamelCase    (      )					->							None:
     '''simple docstring'''





     import doctest

     doctest.testmod()

     lowercase_       :Any     =       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_       :Dict     =       parser.parse_args()

     lowercase_       :List[str]     =       args.input_string

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

     print(SHAaaa(lowercase_		).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


SCREAMING_SNAKE_CASE       :		List[str]         =				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      UpperCamelCase					(		lowercase__     ):


      '''simple docstring'''







      def __init__(     self   ,							UpperCamelCase_ = 101		):
           lowercase_       :Tuple     =       length
      def __len__(     self		):
           return self.length




      def __getitem__(     self   ,							UpperCamelCase_		):
           return i






   class      UpperCamelCase					:


      '''simple docstring'''







      def __call__(     self   ,							UpperCamelCase_		):
           return {"input_ids": torch.tensor(UpperCamelCase_		), "labels": torch.tensor(UpperCamelCase_		)}






   class      UpperCamelCase					(		nn.Module     ):


      '''simple docstring'''







      def __init__(     self		):
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           lowercase_       :List[Any]     =       nn.Linear(120   ,							80		)




      def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None		):
           if labels is not None:
                return torch.tensor(0.0   ,							device=input_ids.device		), input_ids
           else:
                return input_ids






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







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






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_multi_gpu
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :str     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Dict     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							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

   SCREAMING_SNAKE_CASE       :		Optional[int]         =				HfArgumentParser((TrainingArguments,))
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				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 [101, 40, 7]:
      SCREAMING_SNAKE_CASE       :		Dict         =				DummyDataset(dataset_length)




      def     UpperCamelCase    (      _a		)					->							Dict:
           '''simple docstring'''





           lowercase_       :Optional[Any]     =       list(range(len(_a		)		)		)
           lowercase_       :Optional[Any]     =       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}

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

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

      SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				2

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

      SCREAMING_SNAKE_CASE       :		List[str]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)
      SCREAMING_SNAKE_CASE       :		Optional[int]         =				None

