


'''simple docstring'''




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case       		=16
__snake_case       		=32





def       a_   (					lowerCamelCase      :	Optional[int]      ,					lowerCamelCase      :	Dict = 16      ,					lowerCamelCase      :	str = "bert-base-cased"							):
   lowerCAmelCase	=     AutoTokenizer.from_pretrained(_lowercase							)
   lowerCAmelCase	=     load_dataset('glue'      ,					'mrpc'							)

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   lowerCAmelCase	=     datasets.map(
       _lowercase      ,					batched=_lowercase      ,					remove_columns=['idx', 'sentence1', 'sentence2']      ,					load_from_cache_file=_lowercase							)

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

   def collate_fn(lowerCamelCase      :	Optional[Any]							):
      # 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(_lowercase      ,					padding='max_length'      ,					max_length=128      ,					return_tensors='pt'							)
      return tokenizer.pad(_lowercase      ,					padding='longest'      ,					return_tensors='pt'							)

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

   return train_dataloader, eval_dataloader





def       a_   (					lowerCamelCase      :	Optional[Any]      ,					lowerCamelCase      :	Optional[Any]							):
   lowerCAmelCase	=     Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase	=     config['lr']
   lowerCAmelCase	=     int(config['num_epochs']							)
   lowerCAmelCase	=     int(config['seed']							)
   lowerCAmelCase	=     int(config['batch_size']							)
   lowerCAmelCase	=     args.model_name_or_path

   set_seed(_lowercase							)
   lowerCAmelCase	,    lowerCAmelCase	=     get_dataloaders(_lowercase      ,					_lowercase      ,					_lowercase							)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCAmelCase	=     AutoModelForSequenceClassification.from_pretrained(_lowercase      ,					return_dict=_lowercase							)

   # Instantiate optimizer
   lowerCAmelCase	=     (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowerCAmelCase	=     optimizer_cls(params=model.parameters()      ,					lr=_lowercase							)

   if accelerator.state.deepspeed_plugin is not None:
      lowerCAmelCase	=     accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      lowerCAmelCase	=     1
   lowerCAmelCase	=     (len(_lowercase							) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      lowerCAmelCase	=     get_linear_schedule_with_warmup(
          optimizer=_lowercase      ,					num_warmup_steps=0      ,					num_training_steps=_lowercase      ,					)
   else:
      lowerCAmelCase	=     DummyScheduler(_lowercase      ,					total_num_steps=_lowercase      ,					warmup_num_steps=0							)

   # 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.
   lowerCAmelCase	,    lowerCAmelCase	,    lowerCAmelCase	,    lowerCAmelCase	,    lowerCAmelCase	=     accelerator.prepare(
       _lowercase      ,					_lowercase      ,					_lowercase      ,					_lowercase      ,					_lowercase							)

   # We need to keep track of how many total steps we have iterated over
   lowerCAmelCase	=     0
   # We also need to keep track of the stating epoch so files are named properly
   lowerCAmelCase	=     0

   # Now we train the model
   lowerCAmelCase	=     evaluate.load('glue'      ,					'mrpc'							)
   lowerCAmelCase	=     0
   lowerCAmelCase	=     {}
   for epoch in range(_lowercase      ,					_lowercase							):
      model.train()
      for step, batch in enumerate(_lowercase							):
         lowerCAmelCase	=     model(**_lowercase							)
         lowerCAmelCase	=     outputs.loss
         lowerCAmelCase	=     loss / gradient_accumulation_steps
         accelerator.backward(_lowercase							)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

      model.eval()
      lowerCAmelCase	=     0
      for step, batch in enumerate(_lowercase							):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device							)
         with torch.no_grad():
            lowerCAmelCase	=     model(**_lowercase							)
         lowerCAmelCase	=     outputs.logits.argmax(dim=-1							)
         # It is slightly faster to call this once, than multiple times
         lowerCAmelCase	,    lowerCAmelCase	=     accelerator.gather(
             (predictions, batch['labels'])							)  # If we are in a multiprocess environment, the last batch has duplicates
         if accelerator.use_distributed:
            if step == len(_lowercase							) - 1:
               lowerCAmelCase	=     predictions[: len(eval_dataloader.dataset							) - samples_seen]
               lowerCAmelCase	=     references[: len(eval_dataloader.dataset							) - samples_seen]
            else:
               samples_seen += references.shape[0]
         metric.add_batch(
             predictions=_lowercase      ,					references=_lowercase      ,					)

      lowerCAmelCase	=     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''      ,					_lowercase							)
      lowerCAmelCase	=     eval_metric['accuracy']

      if best_performance < eval_metric["accuracy"]:
         lowerCAmelCase	=     eval_metric['accuracy']

   if args.performance_lower_bound is not None:
      assert (
          args.performance_lower_bound <= best_performance
      ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir      ,					'all_results.json'							)      ,					'w'							) as f:
         json.dump(_lowercase      ,					_lowercase							)





def       a_   (					):
   lowerCAmelCase	=     argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
   parser.add_argument(
       '--model_name_or_path'      ,					type=_lowercase      ,					default='bert-base-cased'      ,					help='Path to pretrained model or model identifier from huggingface.co/models.'      ,					required=_lowercase      ,					)
   parser.add_argument(
       '--output_dir'      ,					type=_lowercase      ,					default='.'      ,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,					)
   parser.add_argument(
       '--performance_lower_bound'      ,					type=_lowercase      ,					default=_lowercase      ,					help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'      ,					)
   parser.add_argument(
       '--num_epochs'      ,					type=_lowercase      ,					default=3      ,					help='Number of train epochs.'      ,					)
   lowerCAmelCase	=     parser.parse_args()
   lowerCAmelCase	=     {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
   training_function(_lowercase      ,					_lowercase							)


if __name__ == "__main__":
       main()









from collections.abc import Generator
from math import sin






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     if len(_lowercase ) != 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 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


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

     __UpperCamelCase =			format(_lowercase					,  '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 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


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

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_lowercase ) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

     return bit_string






def 		_A		(		_lowercase )					-> Generator[list[int], None, None]:


     """simple docstring"""


     if len(_lowercase ) % 5_12 != 0:
          raise ValueError('Input must have length that\'s a multiple of 512' )

     for pos in range(0					,  len(_lowercase )					,  5_12 ):
          __UpperCamelCase =			bit_string[pos : pos + 5_12]
          __UpperCamelCase =			[]
          for i in range(0					,  5_12					,  32 ):
               block_words.append(int(to_little_endian(block[i : i + 32] )					,  2 ) )
          yield block_words






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


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

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






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (a + b) % 2**32






def 		_A		(		_lowercase					,  _lowercase )					-> 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 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     __UpperCamelCase =			preprocess(_lowercase )

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

     # Starting states
     __UpperCamelCase =			0X67_45_23_01
     __UpperCamelCase =			0Xef_cd_ab_89
     __UpperCamelCase =			0X98_ba_dc_fe
     __UpperCamelCase =			0X10_32_54_76

     __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(_lowercase ):
          __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(_lowercase ))
                    __UpperCamelCase =			(7 * i) % 16
               __UpperCamelCase =			(f + a + added_consts[i] + block_words[g]) % 2**32
               __UpperCamelCase =			d
               __UpperCamelCase =			c
               __UpperCamelCase =			b
               __UpperCamelCase =			sum_aa(_lowercase					,  left_rotate_aa(_lowercase					,  shift_amounts[i] ) )

          # Add hashed chunk to running total
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )

     __UpperCamelCase =			reformat_hex(_lowercase ) + reformat_hex(_lowercase ) + reformat_hex(_lowercase ) + reformat_hex(_lowercase )
     return digest


if __name__ == "__main__":
    import doctest

    doctest.testmod()


