






'''simple docstring'''
import math
import os
import sys




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     List[Any]							     =  ""
   try:
      with open(_UpperCamelCase    ,		"rb"				) as binary_file:
         lowercase_						:     Optional[int]							     =  binary_file.read()
      for dat in data:
         lowercase_						:     Tuple							     =  F"""{dat:08b}"""
         result += curr_byte
      return result
   except OSError:
      print("File not accessible"				)
      sys.exit()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lexicon.pop(_UpperCamelCase				)
   lowercase_						:     Dict							     =  last_match_id

   if math.loga(_UpperCamelCase				).is_integer():
      for curr_key in lexicon:
         lowercase_						:     Dict							     =  "0" + lexicon[curr_key]

   lowercase_						:     Union[str, Any]							     =  bin(_UpperCamelCase				)[2:]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  {"0": "0", "1": "1"}
   lowercase_     ,		lowercase_						:     List[str]							     =  "", ""
   lowercase_						:     Optional[Any]							     =  len(_UpperCamelCase				)

   for i in range(len(_UpperCamelCase				)				):
      curr_string += data_bits[i]
      if curr_string not in lexicon:
         continue

      lowercase_						:     Union[str, Any]							     =  lexicon[curr_string]
      result += last_match_id
      add_key_to_lexicon(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
      index += 1
      lowercase_						:     Tuple							     =  ""

   while curr_string != "" and curr_string not in lexicon:
      curr_string += "0"

   if curr_string != "":
      lowercase_						:     List[str]							     =  lexicon[curr_string]
      result += last_match_id

   return result




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  os.path.getsize(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  bin(_UpperCamelCase				)[2:]
   lowercase_						:     str							     =  len(_UpperCamelCase				)

   return "0" * (length_length - 1) + file_length_binary + compressed




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  8
   try:
      with open(_UpperCamelCase    ,		"wb"				) as opened_file:
         lowercase_						:     int							     =  [
             to_write[i : i + byte_length]
             for i in range(0    ,		len(_UpperCamelCase				)    ,		_UpperCamelCase				)
         ]

         if len(result_byte_array[-1]				) % byte_length == 0:
            result_byte_array.append("10000000"				)
         else:
            result_byte_array[-1] += "1" + "0" * (
                byte_length - len(result_byte_array[-1]				) - 1
            )

         for elem in result_byte_array:
            opened_file.write(int(_UpperCamelCase    ,		2				).to_bytes(1    ,		byteorder="big"				)				)
   except OSError:
      print("File not accessible"				)
      sys.exit()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     List[Any]							     =  read_file_binary(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  compress_data(_UpperCamelCase				)
   lowercase_						:     Any							     =  add_file_length(_UpperCamelCase    ,		_UpperCamelCase				)
   write_file_binary(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])












'''simple docstring'''
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 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							int	,  a	:							Any=1_0_2_4	,  a	:							Tuple=1_0_2_4	,  a	:							Optional[int]=3.6      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  tokenizer
       lowercase_						:     Union[str, Any]							     =  tokenizer.bos_token_id
       lowercase_						:     Union[str, Any]							     =  dataset
       lowercase_						:     Optional[Any]							     =  seq_length
       lowercase_						:     Any							     =  seq_length * chars_per_token * num_of_sequences





    def __iter__(      self	:							int      ):






       '''simple docstring'''






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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {"streaming": True}
   lowercase_						:     List[Any]							     =  load_dataset(args.dataset_name    ,		split="train"    ,		**_UpperCamelCase				)
   lowercase_						:     int							     =  ConstantLengthDataset(_UpperCamelCase    ,		_UpperCamelCase    ,		seq_length=args.seq_length				)
   lowercase_						:     Dict							     =  DataLoader(_UpperCamelCase    ,		batch_size=args.batch_size				)
   return eval_dataloader




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   model.eval()
   lowercase_						:     Optional[Any]							     =  []
   for step, batch in enumerate(_UpperCamelCase				):
      with torch.no_grad():
         lowercase_						:     str							     =  model(_UpperCamelCase    ,		labels=_UpperCamelCase				)
      lowercase_						:     List[Any]							     =  outputs.loss.repeat(args.batch_size				)
      losses.append(accelerator.gather(_UpperCamelCase				)				)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   lowercase_						:     Optional[int]							     =  torch.mean(torch.cat(_UpperCamelCase				)				)
   try:
      lowercase_						:     Dict							     =  torch.exp(_UpperCamelCase				)
   except OverflowError:
      lowercase_						:     Dict							     =  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}""")




