



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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__UpperCAmelCase          =       16
__UpperCAmelCase          =       32
def 		UpperCamelCase	(   snake_case__       :    Accelerator       ,     snake_case__       :    int = 16	)	->     List[Any]:
   UpperCamelCase    :  Optional[int]							=	AutoTokenizer.from_pretrained('bert-base-cased'	)
   UpperCamelCase    :  Tuple							=	load_dataset('glue'       ,     'mrpc'	)

   def tokenize_function(snake_case__       :    Dict	):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase    :  List[str]							=	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():
      UpperCamelCase    :  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
   UpperCamelCase    :  Union[str, Any]							=	tokenized_datasets.rename_column('label'       ,     'labels'	)

   def collate_fn(snake_case__       :    List[Any]	):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase    :  Tuple							=	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    :  Dict							=	16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase    :  Union[str, Any]							=	8
      else:
         UpperCamelCase    :  Optional[Any]							=	None

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

   # Instantiate dataloaders.
   UpperCamelCase    :  Any							=	DataLoader(
       tokenized_datasets['train']       ,     shuffle=snake_case__       ,     collate_fn=snake_case__       ,     batch_size=snake_case__	)
   UpperCamelCase    :  Union[str, Any]							=	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

    __UpperCAmelCase          =       mocked_dataloaders  # noqa: F811
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[Any]	)	->     Union[str, Any]:
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,     snake_case__	) == "1":
      UpperCamelCase    :  int							=	2
   # Initialize accelerator
   UpperCamelCase    :  str							=	Accelerator(cpu=args.cpu       ,     mixed_precision=args.mixed_precision	)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase    :  Tuple							=	config['lr']
   UpperCamelCase    :  Optional[Any]							=	int(config['num_epochs']	)
   UpperCamelCase    :  Any							=	int(config['seed']	)
   UpperCamelCase    :  Union[str, Any]							=	int(config['batch_size']	)

   UpperCamelCase    :  Union[str, Any]							=	evaluate.load('glue'       ,     'mrpc'	)

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

   set_seed(snake_case__	)
   UpperCamelCase	,     UpperCamelCase    :  Tuple							=	get_dataloaders(snake_case__       ,     snake_case__	)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase    :  Dict							=	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).
   UpperCamelCase    :  List[str]							=	model.to(accelerator.device	)

   # Instantiate optimizer
   UpperCamelCase    :  Tuple							=	AdamW(params=model.parameters()       ,     lr=snake_case__	)

   # Instantiate scheduler
   UpperCamelCase    :  Optional[Any]							=	get_linear_schedule_with_warmup(
       optimizer=snake_case__       ,     num_warmup_steps=100       ,     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.
   UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	accelerator.prepare(
       snake_case__       ,     snake_case__       ,     snake_case__       ,     snake_case__       ,     snake_case__	)

   # Now we train the model
   for epoch in range(snake_case__	):
      model.train()
      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	)
         UpperCamelCase    :  Tuple							=	model(**snake_case__	)
         UpperCamelCase    :  Union[str, Any]							=	outputs.loss
         UpperCamelCase    :  List[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()
      UpperCamelCase    :  Optional[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	)
         with torch.no_grad():
            UpperCamelCase    :  Tuple							=	model(**snake_case__	)
         UpperCamelCase    :  Any							=	outputs.logits.argmax(dim=-1	)
         UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	accelerator.gather((predictions, batch['labels'])	)
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(snake_case__	) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               UpperCamelCase    :  List[Any]							=	predictions[: len(eval_dataloader.dataset	) - samples_seen]
               UpperCamelCase    :  List[str]							=	references[: len(eval_dataloader.dataset	) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=snake_case__       ,     references=snake_case__       ,     )

      UpperCamelCase    :  List[Any]							=	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""       ,     snake_case__	)
def 		UpperCamelCase	(   )	->     List[str]:
   UpperCamelCase    :  Tuple							=	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.'	)
   UpperCamelCase    :  Tuple							=	parser.parse_args()
   UpperCamelCase    :  str							=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__       ,     snake_case__	)


if __name__ == "__main__":
    main()








import functools
from typing import Any
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    list[str]	)	->     bool:

   # Validation
   if not isinstance(snake_case__       ,     snake_case__	) or len(snake_case__	) == 0:
      raise ValueError('the string should be not empty string'	)

   if not isinstance(snake_case__       ,     snake_case__	) or not all(
       isinstance(snake_case__       ,     snake_case__	) and len(snake_case__	) > 0 for item in words	):
      raise ValueError('the words should be a list of non-empty strings'	)

   # Build trie
   UpperCamelCase    :  dict[str, Any]							=	{}
   UpperCamelCase    :  List[str]							=	'WORD_KEEPER'

   for word in words:
      UpperCamelCase    :  List[str]							=	trie
      for c in word:
         if c not in trie_node:
            UpperCamelCase    :  int							=	{}

         UpperCamelCase    :  str							=	trie_node[c]

      UpperCamelCase    :  Tuple							=	True

   UpperCamelCase    :  List[Any]							=	len(snake_case__	)

   # Dynamic programming method
   @functools.cache
   def is_breakable(snake_case__       :    int	) -> bool:
      if index == len_string:
         return True

      UpperCamelCase    :  Dict							=	trie
      for i in range(snake_case__       ,     snake_case__	):
         UpperCamelCase    :  List[Any]							=	trie_node.get(string[i]       ,     snake_case__	)

         if trie_node is None:
            return False

         if trie_node.get(snake_case__       ,     snake_case__	) and is_breakable(i + 1	):
            return True

      return False

   return is_breakable(0	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



