


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


__UpperCamelCase       :       Dict						       =				16
__UpperCamelCase       :       List[Any]						       =				32

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Union[str, Any] = 16    , SCREAMING_SNAKE_CASE	:				Dict = "bert-base-cased"     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   AutoTokenizer.from_pretrained(lowerCAmelCase__     )
 UpperCamelCase__					:    Dict          =   load_dataset('''glue'''    , '''mrpc'''     )

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

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

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

 def collate_fn(SCREAMING_SNAKE_CASE	:				List[str]     ):
  # 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(lowerCAmelCase__    , padding='''max_length'''    , max_length=128    , return_tensors='''pt'''     )
  return tokenizer.pad(lowerCAmelCase__    , padding='''longest'''    , return_tensors='''pt'''     )

 # Instantiate dataloaders.
 UpperCamelCase__					:    Optional[Any]          =   DataLoader(
     tokenized_datasets['''train''']    , shuffle=lowerCAmelCase__    , collate_fn=lowerCAmelCase__    , batch_size=lowerCAmelCase__     )
 UpperCamelCase__					:    List[str]          =   DataLoader(
     tokenized_datasets['''validation''']    , shuffle=lowerCAmelCase__    , collate_fn=lowerCAmelCase__    , batch_size=lowerCAmelCase__     )

 return train_dataloader, eval_dataloader

def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   Accelerator()

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

 set_seed(lowerCAmelCase__     )
 UpperCamelCase__       ,				UpperCamelCase__					:    Optional[Any]          =   get_dataloaders(lowerCAmelCase__    , lowerCAmelCase__    , lowerCAmelCase__     )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase__					:    int          =   AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__    , return_dict=lowerCAmelCase__     )

 # Instantiate optimizer
 UpperCamelCase__					:    str          =   (
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 UpperCamelCase__					:    Dict          =   optimizer_cls(params=model.parameters()    , lr=lowerCAmelCase__     )

 if accelerator.state.deepspeed_plugin is not None:
  UpperCamelCase__					:    List[Any]          =   accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  UpperCamelCase__					:    Dict          =   1
 UpperCamelCase__					:    Optional[int]          =   (len(lowerCAmelCase__     ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  UpperCamelCase__					:    Optional[Any]          =   get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase__    , num_warmup_steps=0    , num_training_steps=lowerCAmelCase__    , )
 else:
  UpperCamelCase__					:    Dict          =   DummyScheduler(lowerCAmelCase__    , total_num_steps=lowerCAmelCase__    , 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.
 UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    str          =   accelerator.prepare(
     lowerCAmelCase__    , lowerCAmelCase__    , lowerCAmelCase__    , lowerCAmelCase__    , lowerCAmelCase__     )

 # We need to keep track of how many total steps we have iterated over
 UpperCamelCase__					:    List[str]          =   0
 # We also need to keep track of the stating epoch so files are named properly
 UpperCamelCase__					:    Any          =   0

 # Now we train the model
 UpperCamelCase__					:    Any          =   evaluate.load('''glue'''    , '''mrpc'''     )
 UpperCamelCase__					:    Optional[Any]          =   0
 UpperCamelCase__					:    List[str]          =   {}
 for epoch in range(lowerCAmelCase__    , lowerCAmelCase__     ):
  model.train()
  for step, batch in enumerate(lowerCAmelCase__     ):
   UpperCamelCase__					:    List[str]          =   model(**lowerCAmelCase__     )
   UpperCamelCase__					:    Union[str, Any]          =   outputs.loss
   UpperCamelCase__					:    Union[str, Any]          =   loss / gradient_accumulation_steps
   accelerator.backward(lowerCAmelCase__     )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

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

  UpperCamelCase__					:    Any          =   metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"    , lowerCAmelCase__     )
  UpperCamelCase__					:    Union[str, Any]          =   eval_metric['''accuracy''']

  if best_performance < eval_metric["accuracy"]:
   UpperCamelCase__					:    Optional[int]          =   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(lowerCAmelCase__    , lowerCAmelCase__     )

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''     )
 parser.add_argument(
     '''--model_name_or_path'''    , type=lowerCAmelCase__    , default='''bert-base-cased'''    , help='''Path to pretrained model or model identifier from huggingface.co/models.'''    , required=lowerCAmelCase__    , )
 parser.add_argument(
     '''--output_dir'''    , type=lowerCAmelCase__    , 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=lowerCAmelCase__    , default=lowerCAmelCase__    , 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=lowerCAmelCase__    , default=3    , help='''Number of train epochs.'''    , )
 UpperCamelCase__					:    Any          =   parser.parse_args()
 UpperCamelCase__					:    str          =   {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
 training_function(lowerCAmelCase__    , lowerCAmelCase__     )


if __name__ == "__main__":
      main()






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (modulus_power * alphabet_size) % modulus

 for i in range(0    , t_len - p_len + 1     ):
  if text_hash == p_hash and text[i : i + p_len] == pattern:
   return True
  if i == t_len - p_len:
   continue
  # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
  UpperCamelCase__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


