





"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class       a							(   a__  ):
 snake_case__             =       0
 snake_case__             =       False
 snake_case__             =       3.0







class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.assertDictEqual(MockClass().to_kwargs()				,       {}     )
  self.assertDictEqual(MockClass(a=2     ).to_kwargs()				,       {'a': 2}     )
  self.assertDictEqual(MockClass(a=2				,       b=_snake_case     ).to_kwargs()				,       {'a': 2, 'b': True}     )
  self.assertDictEqual(MockClass(a=2				,       c=2.25     ).to_kwargs()				,       {'a': 2, 'c': 2.25}     )



 @require_cuda
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= GradScalerKwargs(init_scale=10_24				,       growth_factor=2     )
  AcceleratorState._reset_state()
  lowerCAmelCase    				= Accelerator(mixed_precision='fp16'				,       kwargs_handlers=[scaler_handler]     )
  print(accelerator.use_fpaa     )
  lowerCAmelCase    				= accelerator.scaler

  # Check the kwargs have been applied
  self.assertEqual(scaler._init_scale				,       1_024.0     )
  self.assertEqual(scaler._growth_factor				,       2.0     )

  # Check the other values are at the default
  self.assertEqual(scaler._backoff_factor				,       0.5     )
  self.assertEqual(scaler._growth_interval				,       20_00     )
  self.assertEqual(scaler._enabled				,       _snake_case     )



 @require_multi_gpu
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__     )]
  execute_subprocess_async(_snake_case				,       env=os.environ.copy()     )


if __name__ == "__main__":
       __UpperCamelCase     :						List[str]						       =  DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
       __UpperCamelCase     :						Optional[Any]						       =  Accelerator(kwargs_handlers=[ddp_scaler])
       __UpperCamelCase     :						Dict						       =  torch.nn.Linear(100, 200)
       __UpperCamelCase     :						Optional[Any]						       =  accelerator.prepare(model)

       # Check the values changed in kwargs
       __UpperCamelCase     :						Dict						       =  ''''''
       __UpperCamelCase     :						Tuple						       =  model.bucket_bytes_cap // (1024 * 1024)
       if observed_bucket_cap_map != 15:
              error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
       if model.find_unused_parameters is not True:
              error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

       # Check the values of the defaults
       if model.dim != 0:
              error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
       if model.broadcast_buffers is not True:
              error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
       if model.gradient_as_bucket_view is not False:
              error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

       # Raise error at the end to make sure we don't stop at the first failure.
       if len(error_msg) > 0:
              raise ValueError(error_msg)












"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path'  ,     type=_UpperCAmelCase  ,     default='data/dump.txt'  ,     help='The path to the data.'			)
 parser.add_argument('--tokenizer_type'  ,     type=_UpperCAmelCase  ,     default='bert'  ,     choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name'  ,     type=_UpperCAmelCase  ,     default='bert-base-uncased'  ,     help='The tokenizer to use.'			)
 parser.add_argument('--dump_file'  ,     type=_UpperCAmelCase  ,     default='data/dump'  ,     help='The dump file prefix.'			)
 lowerCAmelCase    				= parser.parse_args()

 logger.info(F'Loading Tokenizer ({args.tokenizer_name})'			)
 if args.tokenizer_type == "bert":
  lowerCAmelCase    				= BertTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  lowerCAmelCase    				= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `<s>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  lowerCAmelCase    				= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(F'Loading text from {args.file_path}'			)
 with open(args.file_path  ,     'r'  ,     encoding='utf8'			) as fp:
  lowerCAmelCase    				= fp.readlines()

 logger.info('Start encoding'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples to process.'			)

 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 1_0000
 lowerCAmelCase    				= time.time()
 for text in data:
  lowerCAmelCase    				= F'{bos} {text.strip()} {sep}'
  lowerCAmelCase    				= tokenizer.encode(_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase			)
  rslt.append(_UpperCAmelCase			)

  iter += 1
  if iter % interval == 0:
   lowerCAmelCase    				= time.time()
   logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
   lowerCAmelCase    				= time.time()
 logger.info('Finished binarization'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples processed.'			)

 lowerCAmelCase    				= F'{args.dump_file}.{args.tokenizer_name}.pickle'
 lowerCAmelCase    				= tokenizer.vocab_size
 if vocab_size < (1 << 16):
  lowerCAmelCase    				= [np.uintaa(_UpperCAmelCase			) for d in rslt]
 else:
  lowerCAmelCase    				= [np.intaa(_UpperCAmelCase			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(F'Dump to {dp_file}'			)
 with open(_UpperCAmelCase  ,     'wb'			) as handle:
  pickle.dump(rslt_  ,     _UpperCAmelCase  ,     protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
       main()





