





"""simple docstring"""






from collections.abc import Generator
from math import sin

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       bytes:






       """simple docstring"""




       if len(SCREAMING_SNAKE_CASE							) != 32:
              raise ValueError('Input must be of length 32'							)

       _UpperCAmelCase     =     b''
       for i in [3, 2, 1, 0]:
              little_endian += string_aa[8 * i : 8 * i + 8]
       return little_endian

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       bytes:






       """simple docstring"""




       if i < 0:
              raise ValueError('Input must be non-negative'							)

       _UpperCAmelCase     =     format(SCREAMING_SNAKE_CASE,'08x'							)[-8:]
       _UpperCAmelCase     =     b''
       for i in [3, 2, 1, 0]:
              little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'							)
       return little_endian_hex

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       bytes:






       """simple docstring"""




       _UpperCAmelCase     =     b''
       for char in message:
              bit_string += format(SCREAMING_SNAKE_CASE,'08b'							).encode('utf-8'							)
       _UpperCAmelCase     =     format(len(SCREAMING_SNAKE_CASE							),'064b'							).encode('utf-8'							)

       # Pad bit_string to a multiple of 512 chars
       bit_string += b"1"
       while len(SCREAMING_SNAKE_CASE							) % 512 != 448:
              bit_string += b"0"
       bit_string += to_little_endian(start_len[32:]							) + to_little_endian(start_len[:32]							)

       return bit_string

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Generator[list[int], None, None]:






       """simple docstring"""




       if len(SCREAMING_SNAKE_CASE							) % 512 != 0:
              raise ValueError('Input must have length that\'s a multiple of 512'							)

       for pos in range(0,len(SCREAMING_SNAKE_CASE							),512							):
              _UpperCAmelCase     =     bit_string[pos : pos + 512]
              _UpperCAmelCase     =     []
              for i in range(0,512,32							):
                     block_words.append(int(to_little_endian(block[i : i + 32]							),2							)							)
              yield block_words

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       if i < 0:
              raise ValueError('Input must be non-negative'							)

       _UpperCAmelCase     =     format(SCREAMING_SNAKE_CASE,'032b'							)
       _UpperCAmelCase     =     ''
       for c in i_str:
              new_str += "1" if c == "0" else "0"
       return int(SCREAMING_SNAKE_CASE,2							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return (a + b) % 2**32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       if i < 0:
              raise ValueError('Input must be non-negative'							)
       if shift < 0:
              raise ValueError('Shift must be non-negative'							)
       return ((i << shift) ^ (i >> (32 - shift))) % 2**32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       bytes:






       """simple docstring"""




       _UpperCAmelCase     =     preprocess(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     [int(2**32 * abs(sin(i + 1							)							)							) for i in range(64							)]

       # Starting states
       _UpperCAmelCase     =     0x67_452_301
       _UpperCAmelCase     =     0xEF_CDA_B89
       _UpperCAmelCase     =     0x98_BAD_CFE
       _UpperCAmelCase     =     0x10_325_476

       _UpperCAmelCase     =     [
           7,
           12,
           17,
           22,
           7,
           12,
           17,
           22,
           7,
           12,
           17,
           22,
           7,
           12,
           17,
           22,
           5,
           9,
           14,
           20,
           5,
           9,
           14,
           20,
           5,
           9,
           14,
           20,
           5,
           9,
           14,
           20,
           4,
           11,
           16,
           23,
           4,
           11,
           16,
           23,
           4,
           11,
           16,
           23,
           4,
           11,
           16,
           23,
           6,
           10,
           15,
           21,
           6,
           10,
           15,
           21,
           6,
           10,
           15,
           21,
           6,
           10,
           15,
           21,
       ]

       # Process bit string in chunks, each with 16 32-char words
       for block_words in get_block_words(SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     aa
              _UpperCAmelCase     =     ba
              _UpperCAmelCase     =     ca
              _UpperCAmelCase     =     da

              # Hash current chunk
              for i in range(64							):
                     if i <= 15:
                            # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                            _UpperCAmelCase     =     d ^ (b & (c ^ d))
                            _UpperCAmelCase     =     i
                     elif i <= 31:
                            # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                            _UpperCAmelCase     =     c ^ (d & (b ^ c))
                            _UpperCAmelCase     =     (5 * i + 1) % 16
                     elif i <= 47:
                            _UpperCAmelCase     =     b ^ c ^ d
                            _UpperCAmelCase     =     (3 * i + 5) % 16
                     else:
                            _UpperCAmelCase     =     c ^ (b | not_aa(SCREAMING_SNAKE_CASE							))
                            _UpperCAmelCase     =     (7 * i) % 16
                     _UpperCAmelCase     =     (f + a + added_consts[i] + block_words[g]) % 2**32
                     _UpperCAmelCase     =     d
                     _UpperCAmelCase     =     c
                     _UpperCAmelCase     =     b
                     _UpperCAmelCase     =     sum_aa(SCREAMING_SNAKE_CASE,left_rotate_aa(SCREAMING_SNAKE_CASE,shift_amounts[i]							)							)

              # Add hashed chunk to running total
              _UpperCAmelCase     =     sum_aa(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     sum_aa(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     sum_aa(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     sum_aa(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     reformat_hex(SCREAMING_SNAKE_CASE							) + reformat_hex(SCREAMING_SNAKE_CASE							) + reformat_hex(SCREAMING_SNAKE_CASE							) + reformat_hex(SCREAMING_SNAKE_CASE							)
       return digest


if __name__ == "__main__":
    import doctest

    doctest.testmod()













"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_			=							16
lowerCAmelCase_			=							32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = 16							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     AutoTokenizer.from_pretrained('bert-base-cased'							)
       _UpperCAmelCase     =     load_dataset('glue','mrpc'							)

       def tokenize_function(SCREAMING_SNAKE_CASE							):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase     =     tokenizer(examples['sentence1'],examples['sentence2'],truncation=SCREAMING_SNAKE_CASE,max_length=SCREAMING_SNAKE_CASE							)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              _UpperCAmelCase     =     datasets.map(
                  SCREAMING_SNAKE_CASE,batched=SCREAMING_SNAKE_CASE,remove_columns=['idx', 'sentence1', 'sentence2'],)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _UpperCAmelCase     =     tokenized_datasets.rename_column('label','labels'							)

       def collate_fn(SCREAMING_SNAKE_CASE							):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _UpperCAmelCase     =     128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _UpperCAmelCase     =     16
              elif accelerator.mixed_precision != "no":
                     _UpperCAmelCase     =     8
              else:
                     _UpperCAmelCase     =     None

              return tokenizer.pad(
                  SCREAMING_SNAKE_CASE,padding='longest',max_length=SCREAMING_SNAKE_CASE,pad_to_multiple_of=SCREAMING_SNAKE_CASE,return_tensors='pt',)

       # Instantiate dataloaders.
       _UpperCAmelCase     =     DataLoader(
           tokenized_datasets['train'],shuffle=SCREAMING_SNAKE_CASE,collate_fn=SCREAMING_SNAKE_CASE,batch_size=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     DataLoader(
           tokenized_datasets['validation'],shuffle=SCREAMING_SNAKE_CASE,collate_fn=SCREAMING_SNAKE_CASE,batch_size=SCREAMING_SNAKE_CASE							)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    lowerCAmelCase_			=							mocked_dataloaders  # noqa: F811

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       if os.environ.get('TESTING_MOCKED_DATALOADERS',SCREAMING_SNAKE_CASE							) == "1":
              _UpperCAmelCase     =     2
       # New Code #
       _UpperCAmelCase     =     int(args.gradient_accumulation_steps							)
       # Initialize accelerator
       _UpperCAmelCase     =     Accelerator(
           cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'							)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCAmelCase     =     config['lr']
       _UpperCAmelCase     =     int(config['num_epochs']							)
       _UpperCAmelCase     =     int(config['seed']							)
       _UpperCAmelCase     =     int(config['batch_size']							)

       _UpperCAmelCase     =     evaluate.load('glue','mrpc'							)

       set_seed(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase     =     get_dataloaders(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCAmelCase     =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased',return_dict=SCREAMING_SNAKE_CASE							)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       _UpperCAmelCase     =     model.to(accelerator.device							)

       # Instantiate optimizer
       _UpperCAmelCase     =     AdamW(params=model.parameters(),lr=SCREAMING_SNAKE_CASE							)

       # Instantiate scheduler
       _UpperCAmelCase     =     get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE,num_warmup_steps=100,num_training_steps=(len(SCREAMING_SNAKE_CASE							) * num_epochs),)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     accelerator.prepare(
           SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE							):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device							)
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                            _UpperCAmelCase     =     model(**SCREAMING_SNAKE_CASE							)
                            _UpperCAmelCase     =     output.loss
                            accelerator.backward(SCREAMING_SNAKE_CASE							)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device							)
                     with torch.no_grad():
                            _UpperCAmelCase     =     model(**SCREAMING_SNAKE_CASE							)
                     _UpperCAmelCase     =     outputs.logits.argmax(dim=-1							)
                     _UpperCAmelCase    ,							_UpperCAmelCase     =     accelerator.gather_for_metrics((predictions, batch['labels'])							)
                     metric.add_batch(
                         predictions=SCREAMING_SNAKE_CASE,references=SCREAMING_SNAKE_CASE,)

              _UpperCAmelCase     =     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""",SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     argparse.ArgumentParser(description='Simple example of training script.'							)
       parser.add_argument(
           '--mixed_precision',type=SCREAMING_SNAKE_CASE,default=SCREAMING_SNAKE_CASE,choices=['no', 'fp16', 'bf16', 'fp8'],help='Whether to use mixed precision. Choose'
           'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
           'and an Nvidia Ampere GPU.',)
       # New Code #
       parser.add_argument(
           '--gradient_accumulation_steps',type=SCREAMING_SNAKE_CASE,default=1,help='The number of minibatches to be ran before gradients are accumulated.',)
       parser.add_argument('--cpu',action='store_true',help='If passed, will train on the CPU.'							)
       _UpperCAmelCase     =     parser.parse_args()
       _UpperCAmelCase     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
    main()






