






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




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						data
            __snake_case			:str          =						[0X67_452_301, 0Xef_cda_b89, 0X98_bad_cfe, 0X10_325_476, 0Xc3_d2e_1f0]



       @staticmethod
       def 					__lowercase						(							a__			,						a__    ) ->      Dict:






            '''simple docstring'''





            return ((n << b) | (n >> (32 - b))) & 0Xff_fff_fff



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						B"""\x80""" + B"""\x00""" * (63 - (len(self.data    ) + 8) % 64)
            __snake_case			:List[Any]          =						self.data + padding + struct.pack(""">Q"""			,						8 * len(self.data    )    )
            return padded_data



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            return [
                self.padded_data[i : i + 64] for i in range(0			,						len(self.padded_data    )			,						64    )
            ]



       def 					__lowercase						(							self			,						a__    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Any          =						list(struct.unpack(""">16L"""			,						a__    )    ) + [0] * 64
            for i in range(16			,						80    ):
                 __snake_case			:Dict          =						self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])			,						1    )
            return w



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.padding()
            __snake_case			:List[Any]          =						self.split_blocks()
            for block in self.blocks:
                 __snake_case			:Optional[int]          =						self.expand_block(a__    )
                 __snake_case       , __snake_case       , __snake_case       , __snake_case       , __snake_case			:Optional[int]          =						self.h
                 for i in range(0			,						80    ):
                      if 0 <= i < 20:
                           __snake_case			:str          =						(b & c) | ((~b) & d)
                           __snake_case			:int          =						0X5a_827_999
                      elif 20 <= i < 40:
                           __snake_case			:List[Any]          =						b ^ c ^ d
                           __snake_case			:Union[str, Any]          =						0X6e_d9e_ba1
                      elif 40 <= i < 60:
                           __snake_case			:Any          =						(b & c) | (b & d) | (c & d)
                           __snake_case			:int          =						0X8f_1bb_cdc
                      elif 60 <= i < 80:
                           __snake_case			:Optional[int]          =						b ^ c ^ d
                           __snake_case			:Optional[int]          =						0Xca_62c_1d6
                      __snake_case       , __snake_case       , __snake_case       , __snake_case       , __snake_case			:Dict          =						(
                          self.rotate(a__			,						5    ) + f + e + k + expanded_block[i] & 0Xff_fff_fff,
                          a,
                          self.rotate(a__			,						30    ),
                          c,
                          d,
                      )
                 __snake_case			:str          =						(
                     self.h[0] + a & 0Xff_fff_fff,
                     self.h[1] + b & 0Xff_fff_fff,
                     self.h[2] + c & 0Xff_fff_fff,
                     self.h[3] + d & 0Xff_fff_fff,
                     self.h[4] + e & 0Xff_fff_fff,
                 )
            return ("{:08x}" * 5).format(*self.h    )






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						B"""Test String"""
     assert SHAaHash(snake_case__  ).final_hash() == hashlib.shaa(snake_case__  ).hexdigest()  # noqa: S324






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Tuple          =						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"""  )
     __snake_case			:List[Any]          =						parser.parse_args()
     __snake_case			:List[Any]          =						args.input_string
     # In any case hash input should be a bytestring
     if args.input_file:
          with open(args.input_file	,"""rb"""  ) as f:
               __snake_case			:Tuple          =						f.read()
     else:
          __snake_case			:Tuple          =						bytes(snake_case__	,"""utf-8"""  )
     print(SHAaHash(snake_case__  ).final_hash()  )


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

     doctest.testmod()










import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						np.random.default_rng(a__    )
            __snake_case			:List[str]          =						length
            __snake_case			:Optional[Any]          =						rng.normal(size=(length,)    ).astype(np.floataa    )
            __snake_case			:Optional[int]          =						a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return {"x": self.x[i], "y": self.y[i]}




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:List[Any]          =						False
            return x * self.a + self.b






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

     __snake_case			:List[Any]          =						{v: i for i, v in enumerate(snake_case__  )}

     def tokenize_function(snake_case__     :   List[Any]  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[Any]          =						tokenizer(
              examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""  )
          if "label" in examples:
               __snake_case			:Dict          =						[label_to_id[l] for l in examples["""label"""]]
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __snake_case			:List[str]          =						datasets.map(
         snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

     def collate_fn(snake_case__     :   Optional[int]  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case__	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


