




import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: str    ,       UpperCamelCase_: int    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: str=10_24    ,       UpperCamelCase_: int=10_24    ,       UpperCamelCase_: Any=3.6   ):
             __lowerCamelCase   =						tokenizer
             __lowerCamelCase   =						tokenizer.bos_token_id
             __lowerCamelCase   =						dataset
             __lowerCamelCase   =						seq_length
             __lowerCamelCase   =						seq_length * chars_per_token * num_of_sequences




      def __iter__(				self: Optional[int]   ):
             __lowerCamelCase   =						iter(self.dataset   )
             __lowerCamelCase   =						True
             while more_examples:
                    __lowerCamelCase,   __lowerCamelCase   =						[], 0
                    while True:
                           if buffer_len >= self.input_characters:
                                  break
                           try:
                                  buffer.append(next(UpperCamelCase_   )["""content"""]   )
                                  buffer_len += len(buffer[-1]   )
                           except StopIteration:
                                  __lowerCamelCase   =						False
                                  break
                    __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       truncation=UpperCamelCase_   )["""input_ids"""]
                    __lowerCamelCase   =						[]
                    for tokenized_input in tokenized_inputs:
                           all_token_ids.extend(tokenized_input + [self.concat_token_id]   )
                    for i in range(0    ,       len(UpperCamelCase_   )    ,       self.seq_length   ):
                           __lowerCamelCase   =						all_token_ids[i : i + self.seq_length]
                           if len(UpperCamelCase_   ) == self.seq_length:
                                  yield torch.tensor(UpperCamelCase_   )




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       __lowerCamelCase   =						{"""streaming""": True}
       __lowerCamelCase   =						load_dataset(args.dataset_name  ,			split="""train"""  ,			**A__	)
       __lowerCamelCase   =						ConstantLengthDataset(A__  ,			A__  ,			seq_length=args.seq_length	)
       __lowerCamelCase   =						DataLoader(A__  ,			batch_size=args.batch_size	)
       return eval_dataloader




def 					lowerCamelCase__       (						A__       :					List[str]	):





       '''simple docstring'''



       model.eval()
       __lowerCamelCase   =						[]
       for step, batch in enumerate(A__	):
              with torch.no_grad():
                     __lowerCamelCase   =						model(A__  ,			labels=A__	)
              __lowerCamelCase   =						outputs.loss.repeat(args.batch_size	)
              losses.append(accelerator.gather(A__	)	)

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       __lowerCamelCase   =						torch.mean(torch.cat(A__	)	)
       try:
              __lowerCamelCase   =						torch.exp(A__	)
       except OverflowError:
              __lowerCamelCase   =						float("""inf"""	)
       return loss.item(), perplexity.item()


# Setup Accelerator
UpperCAmelCase_									=	Accelerator()

# Parse configuration
UpperCAmelCase_									=	HfArgumentParser(EvaluationArguments)
UpperCAmelCase_									=	parser.parse_args()
set_seed(args.seed)

# Logging
UpperCAmelCase_									=	logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
UpperCAmelCase_									=	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_									=	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCAmelCase_									=	create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCAmelCase_				,			UpperCAmelCase_									=	accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase_				,			UpperCAmelCase_									=	evaluate(args)




logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







"""simple docstring"""


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






class 					lowercase:







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  data
   _snake_case							:		Dict								=  [0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]


 @staticmethod
 def  UpperCamelCase_   (				a_:		Optional[Any],      a_:		Dict		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		List[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.padding()
   _snake_case							:		str								=  self.split_blocks()
   for block in self.blocks:
     _snake_case							:		Any								=  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							:		int								=  (b & c) | ((~b) & d)
         _snake_case							:		str								=  0X5a827999
       elif 20 <= i < 40:
         _snake_case							:		Optional[int]								=  b ^ c ^ d
         _snake_case							:		str								=  0X6ed9eba1
       elif 40 <= i < 60:
         _snake_case							:		List[Any]								=  (b & c) | (b & d) | (c & d)
         _snake_case							:		List[Any]								=  0X8f1bbcdc
       elif 60 <= i < 80:
         _snake_case							:		List[Any]								=  b ^ c ^ d
         _snake_case							:		int								=  0Xca62c1d6
       _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  (
           self.rotate(a_,      5		) + f + e + k + expanded_block[i] & 0Xffffffff,
           a,
           self.rotate(a_,      30		),
           c,
           d,
       )
     _snake_case							:		Union[str, Any]								=  (
         self.h[0] + a & 0Xffffffff,
         self.h[1] + b & 0Xffffffff,
         self.h[2] + c & 0Xffffffff,
         self.h[3] + d & 0Xffffffff,
         self.h[4] + e & 0Xffffffff,
     )
   return ("{:08x}" * 5).format(*self.h		)







def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  B"""Test String"""
  assert SHAaHash(snake_case__    ).final_hash() == hashlib.shaa(snake_case__    ).hexdigest()  # noqa: S324


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		List[Any]								=  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							:		Union[str, 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							:		str								=  f.read()
  else:
    _snake_case							:		int								=  bytes(snake_case__		,   """utf-8"""    )
  print(SHAaHash(snake_case__    ).final_hash()    )


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

     doctest.testmod()


