





'''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,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowercase__       : Optional[int]         =       16
lowercase__       : Optional[int]         =       32






def 			_lowerCAmelCase     (    __snake_case    :					Accelerator					,				__snake_case    :					int = 16     )	->	Optional[Any]:
     __A		:					Dict						=			AutoTokenizer.from_pretrained('bert-base-cased'     )
     __A		:					List[Any]						=			load_dataset('glue'					,				'mrpc'     )

     def tokenize_function(__snake_case    :					Optional[int]     ):
          # max_length=None => use the model max length (it's actually the default)
          __A		:					Optional[Any]						=			tokenizer(examples['sentence1']					,				examples['sentence2']					,				truncation=_snake_case					,				max_length=_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():
          __A		:					List[str]						=			datasets.map(
              _snake_case					,				batched=_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
     __A		:					Tuple						=			tokenized_datasets.rename_column('label'					,				'labels'     )

     def collate_fn(__snake_case    :					Any     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A		:					Union[str, Any]						=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __A		:					Tuple						=			16
          elif accelerator.mixed_precision != "no":
               __A		:					Optional[int]						=			8
          else:
               __A		:					List[Any]						=			None

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

     # Instantiate dataloaders.
     __A		:					Any						=			DataLoader(
         tokenized_datasets['train']					,				shuffle=_snake_case					,				collate_fn=_snake_case					,				batch_size=_snake_case     )
     __A		:					Optional[int]						=			DataLoader(
         tokenized_datasets['validation']					,				shuffle=_snake_case					,				collate_fn=_snake_case					,				batch_size=_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

  lowercase__       : Optional[Any]         =       mocked_dataloaders  # noqa: F811






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					str     )	->	Optional[int]:
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'					,				_snake_case     ) == "1":
          __A		:					Dict						=			2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          __A		:					List[str]						=			Accelerator(
              cpu=args.cpu					,				mixed_precision=args.mixed_precision					,				log_with='all'					,				project_dir=args.project_dir     )
     else:
          __A		:					Union[str, Any]						=			Accelerator(cpu=args.cpu					,				mixed_precision=args.mixed_precision     )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A		:					Any						=			config['lr']
     __A		:					Dict						=			int(config['num_epochs']     )
     __A		:					Optional[int]						=			int(config['seed']     )
     __A		:					List[Any]						=			int(config['batch_size']     )
     set_seed(_snake_case     )

     __A					,__A		:					List[str]						=			get_dataloaders(_snake_case					,				_snake_case     )
     __A		:					Optional[Any]						=			evaluate.load('glue'					,				'mrpc'     )

     # If the batch size is too big we use gradient accumulation
     __A		:					str						=			1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __A		:					Dict						=			batch_size // MAX_GPU_BATCH_SIZE
          __A		:					Optional[Any]						=			MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A		:					Union[str, Any]						=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,				return_dict=_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).
     __A		:					Union[str, Any]						=			model.to(accelerator.device     )

     # Instantiate optimizer
     __A		:					List[str]						=			AdamW(params=model.parameters()					,				lr=_snake_case     )

     # Instantiate scheduler
     __A		:					str						=			get_linear_schedule_with_warmup(
         optimizer=_snake_case					,				num_warmup_steps=1_00					,				num_training_steps=(len(_snake_case     ) * num_epochs) // gradient_accumulation_steps					,				)

     # 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.
     __A					,__A					,__A					,__A					,__A		:					Dict						=			accelerator.prepare(
         _snake_case					,				_snake_case					,				_snake_case					,				_snake_case					,				_snake_case     )

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          __A		:					str						=			os.path.split(_snake_case     )[-1].split('.'     )[0]
          accelerator.init_trackers(_snake_case					,				_snake_case     )

     # Now we train the model
     for epoch in range(_snake_case     ):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               __A		:					int						=			0
          for step, batch in enumerate(_snake_case     ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device     )
               __A		:					Tuple						=			model(**_snake_case     )
               __A		:					Tuple						=			outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               __A		:					str						=			loss / gradient_accumulation_steps
               accelerator.backward(_snake_case     )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

          __A		:					Optional[int]						=			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'					,				_snake_case     )

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': eval_metric['accuracy'],
                       'f1': eval_metric['f1'],
                       'train_loss': total_loss.item() / len(_snake_case     ),
                       'epoch': epoch,
                   }					,				step=_snake_case					,				)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()






def 			_lowerCAmelCase     (    )	->	List[Any]:
     __A		:					int						=			argparse.ArgumentParser(description='Simple example of training script.'     )
     parser.add_argument(
         '--mixed_precision'					,				type=_snake_case					,				default=_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.'					,				)
     parser.add_argument('--cpu'					,				action='store_true'					,				help='If passed, will train on the CPU.'     )
     parser.add_argument(
         '--with_tracking'					,				action='store_true'					,				help='Whether to load in all available experiment trackers from the environment and use them for logging.'					,				)
     parser.add_argument(
         '--project_dir'					,				type=_snake_case					,				default='logs'					,				help='Location on where to store experiment tracking logs` and relevent project information'					,				)
     __A		:					Union[str, Any]						=			parser.parse_args()
     __A		:					Union[str, Any]						=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(_snake_case					,				_snake_case     )


if __name__ == "__main__":
  main()




UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   if p_len > t_len:
      return False

   _A      =       0
   _A      =       0
   _A      =       1

   # Calculating the hash of pattern and substring of text
   for i in range(_snake_case				):
      _A      =       (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      _A      =       (ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _A      =       (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
      _A      =       (
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False

def 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



