


'''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 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
#
########################################################################


__magic_name__								=   16
__magic_name__								=   32




def   lowerCamelCase       (   lowerCamelCase	:   Accelerator				,    lowerCamelCase	:   int = 16):
      A_    :	Optional[Any]							 =					AutoTokenizer.from_pretrained("""bert-base-cased""")
      A_    :	List[str]							 =					load_dataset("""glue"""				,    """mrpc""")

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

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

            return tokenizer.pad(
                a__				,    padding="""longest"""				,    max_length=a__				,    pad_to_multiple_of=a__				,    return_tensors="""pt"""				,    )

      # Instantiate dataloaders.
      A_    :	Union[str, Any]							 =					DataLoader(
          tokenized_datasets["""train"""]				,    shuffle=a__				,    collate_fn=a__				,    batch_size=a__)
      A_    :	List[str]							 =					DataLoader(
          tokenized_datasets["""validation"""]				,    shuffle=a__				,    collate_fn=a__				,    batch_size=a__)

      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

     __magic_name__								=   mocked_dataloaders  # noqa: F811




def   lowerCamelCase       (   lowerCamelCase	:   List[str]				,    lowerCamelCase	:   Optional[int]):





      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,    a__) == "1":
            A_    :	str							 =					2
      # Initialize accelerator
      A_    :	Tuple							 =					Accelerator(cpu=args.cpu				,    mixed_precision=args.mixed_precision)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      A_    :	Dict							 =					config["""lr"""]
      A_    :	Any							 =					int(config["""num_epochs"""])
      A_    :	str							 =					int(config["""seed"""])
      A_    :	Optional[Any]							 =					int(config["""batch_size"""])

      A_    :	Any							 =					evaluate.load("""glue"""				,    """mrpc""")

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

      set_seed(a__)
      A_ ,       A_    :	Tuple							 =					get_dataloaders(a__				,    a__)
      # 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=a__)

      # 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_    :	List[str]							 =					model.to(accelerator.device)

      # Instantiate optimizer
      A_    :	Dict							 =					AdamW(params=model.parameters()				,    lr=a__)

      # Instantiate scheduler
      A_    :	Tuple							 =					get_linear_schedule_with_warmup(
          optimizer=a__				,    num_warmup_steps=100				,    num_training_steps=(len(a__) * 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_    :	Optional[int]							 =					accelerator.prepare(
          a__				,    a__				,    a__				,    a__				,    a__)

      # Now we train the model
      for epoch in range(a__):
            model.train()
            for step, batch in enumerate(a__):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device)
                  A_    :	Union[str, Any]							 =					model(**a__)
                  A_    :	str							 =					outputs.loss
                  A_    :	List[str]							 =					loss / gradient_accumulation_steps
                  accelerator.backward(a__)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            A_    :	str							 =					0
            for step, batch in enumerate(a__):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device)
                  with torch.no_grad():
                        A_    :	Optional[int]							 =					model(**a__)
                  A_    :	str							 =					outputs.logits.argmax(dim=-1)
                  A_ ,       A_    :	int							 =					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(a__) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              A_    :	Optional[int]							 =					predictions[: len(eval_dataloader.dataset) - samples_seen]
                              A_    :	Optional[int]							 =					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=a__				,    references=a__				,    )

            A_    :	List[str]							 =					metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'				,    a__)




def   lowerCamelCase       (   ):
      A_    :	int							 =					argparse.ArgumentParser(description="""Simple example of training script.""")
      parser.add_argument(
          """--mixed_precision"""				,    type=a__				,    default=a__				,    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.""")
      A_    :	Any							 =					parser.parse_args()
      A_    :	int							 =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(a__				,    a__)


if __name__ == "__main__":
     main()




from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						bool:


							'''simple docstring'''





							_UpperCAmelCase											=     get_failure_array(a__			)

							# 2) Step through text searching for pattern
							_UpperCAmelCase						,			_UpperCAmelCase											=     0, 0  # index into text, pattern
							while i < len(a__			):
														if pattern[j] == text[i]:
																					if j == (len(a__			) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					_UpperCAmelCase											=     failure[j - 1]
																					continue
														i += 1
							return False

def 			lowerCAmelCase__					(     a__:						str			)    ->						list[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     [0]
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     1
							while j < len(a__			):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					_UpperCAmelCase											=     failure[i - 1]
																					continue
														j += 1
														failure.append(a__			)
							return failure


if __name__ == "__main__":
		# Test 1)
		lowerCAmelCase__    :List[Any]       =				'''abc1abc12'''
		lowerCAmelCase__    :Optional[int]       =				'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
		lowerCAmelCase__    :int       =				'''alskfjaldsk23adsfabcabc'''
		assert kmp(pattern, texta) and not kmp(pattern, texta)

		# Test 2)
		lowerCAmelCase__    :List[str]       =				'''ABABX'''
		lowerCAmelCase__    :int       =				'''ABABZABABYABABX'''
		assert kmp(pattern, text)

		# Test 3)
		lowerCAmelCase__    :int       =				'''AAAB'''
		lowerCAmelCase__    :Any       =				'''ABAAAAAB'''
		assert kmp(pattern, text)

		# Test 4)
		lowerCAmelCase__    :Union[str, Any]       =				'''abcdabcy'''
		lowerCAmelCase__    :Any       =				'''abcxabcdabxabcdabcdabcy'''
		assert kmp(pattern, text)

		# Test 5)
		lowerCAmelCase__    :List[Any]       =				'''aabaabaaa'''
		assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
